chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Initially taken from Github's Python gitignore file
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# tests and logs
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tests/fixtures/cached_*_text.txt
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logs/
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lightning_logs/
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lang_code_data/
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# vscode
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.vs
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.vscode
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# Pycharm
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.idea
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# TF code
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tensorflow_code
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# Models
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proc_data
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# examples
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runs
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/runs_old
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/wandb
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/examples/runs
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/examples/**/*.args
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/examples/rag/sweep
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# data
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/data
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serialization_dir
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# emacs
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*.*~
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debug.env
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# vim
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.*.swp
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#ctags
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tags
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# pre-commit
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.pre-commit*
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# .lock
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*.lock
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# DS_Store (MacOS)
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.DS_Store
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# ruff
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.ruff_cache
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# our proj
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/output/
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/outputs/
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/checkpoint/
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/checkpoints/
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exp
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.gradio/
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experimental_voices
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# Contributing to VibeVoice
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Thank you for your interest in **VibeVoice**. VibeVoice is an academic-oriented research project.
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Our core principles are **code minimalism**, **high readability**, and **functional purity**.
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## 1. Preferred Contributions
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- **Bug Fixes & New Features**
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These are our primary focus.
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- **Minimalism First**
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Code must be concise, clear, and minimal.
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## 2. Rejected Patterns
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||||
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||||
- **Over-Engineering**
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We will reject unnecessary encapsulation, excessive abstraction, or complex architectural refactoring.
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Please remember this is research-oriented code, not a commercial enterprise project.
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- **Style Tweaks**
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PRs solely for formatting, beautification, or non-functional style adjustments will be rejected.
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- **Non-English Contributions**
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PRs containing non-English code comments, documentation, commit messages, or descriptions will be rejected.
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## 3. Review Policy
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- **Line-by-Line Review**
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Maintainers strictly audit every single line of change manually. Before submitting, ensure you have personally scrutinized and fully mastered all code logic.
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- **Caution Regarding AI-Generated Code**
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LLM-generated code often contains redundant logic and subtle defects. Large chunks of AI-generated code will be rejected unless they have undergone rigorous human cleaning and verification.
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Ensure that every line of code has an absolute necessity to exist.
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## 4. Documentation Standards
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- **Precise & Concise**
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VibeVoice's documentation serves a global research community. We hold extremely high standards: changes must be succinct and accurate.
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Please eliminate all verbosity and redundancy to ensure maximum information density.
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---
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We look forward to receiving **precise, streamlined, and substantively valuable** research contributions.
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MIT License
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Copyright (c) 2025 Microsoft
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Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
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in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
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||||
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||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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<div align="center">
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## 🎙️ VibeVoice: Open-Source Frontier Voice AI
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[](https://microsoft.github.io/VibeVoice)
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[](https://huggingface.co/collections/microsoft/vibevoice-68a2ef24a875c44be47b034f)
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[](https://openreview.net/pdf?id=FihSkzyxdv)
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||||
[](https://arxiv.org/pdf/2601.18184)
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||||
[](https://colab.research.google.com/github/microsoft/VibeVoice/blob/main/demo/VibeVoice_colab.ipynb)
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||||
[](https://aka.ms/vibevoice-asr)
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[](https://trendshift.io/repositories/15465)
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</div>
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||||
|
||||
|
||||
<div align="center">
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: dark)" srcset="Figures/VibeVoice_logo_white.png">
|
||||
<img src="Figures/VibeVoice_logo.png" alt="VibeVoice Logo" width="300">
|
||||
</picture>
|
||||
</div>
|
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<div align="left">
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<h3>📰 News</h3>
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<strong>2026-03-06: 🚀 VibeVoice ASR is now part of a <a href="https://huggingface.co/microsoft/VibeVoice-ASR-HF">Transformers release</a>! You can now use our speech recognition model directly through the Hugging Face Transformers library for seamless integration into your projects.</strong>
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<strong>2026-01-21:</strong> 📣 We open-sourced <a href="docs/vibevoice-asr.md"><strong>VibeVoice-ASR</strong></a>, a unified speech-to-text model designed to handle 60-minute long-form audio in a single pass, generating structured transcriptions containing Who (Speaker), When (Timestamps), and What (Content), with support for User-Customized Context. Try it in [Playground](https://aka.ms/vibevoice-asr).
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- ⭐️ VibeVoice-ASR is natively multilingual, supporting over 50 languages — check the [supported languages](docs/vibevoice-asr.md#language-distribution) for details.
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- 🔥 The VibeVoice-ASR [finetuning code](finetuning-asr/README.md) is now available!
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- ⚡️ **vLLM inference** is now supported for faster inference; see [vllm-asr](docs/vibevoice-vllm-asr.md) for more details.
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- 📑 [VibeVoice-ASR Technique Report](https://arxiv.org/pdf/2601.18184) is available.
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2025-12-16: 📣 We added experimental speakers to <a href="docs/vibevoice-realtime-0.5b.md"><strong>VibeVoice‑Realtime‑0.5B</strong></a> for exploration, including multilingual voices in nine languages (DE, FR, IT, JP, KR, NL, PL, PT, ES) and 11 distinct English style voices. [Try it](docs/vibevoice-realtime-0.5b.md#optional-more-experimental-voices). More speaker types will be added over time.
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2025-12-03: 📣 We open-sourced <a href="docs/vibevoice-realtime-0.5b.md"><strong>VibeVoice‑Realtime‑0.5B</strong></a>, a real‑time text‑to‑speech model that supports streaming text input and robust long-form speech generation. Try it on [Colab](https://colab.research.google.com/github/microsoft/VibeVoice/blob/main/demo/vibevoice_realtime_colab.ipynb).
|
||||
|
||||
|
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2025-09-05: VibeVoice is an open-source research framework intended to advance collaboration in the speech synthesis community. After release, we discovered instances where the tool was used in ways inconsistent with the stated intent. Since responsible use of AI is one of Microsoft’s guiding principles, we have removed the VibeVoice-TTS code from this repository.
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2025-08-25: 📣 We open-sourced <a href="docs/vibevoice-tts.md"><strong>VibeVoice-TTS</strong></a>, a long-form multi-speaker text-to-speech model that can synthesize speech up to 90 minutes long with up to 4 distinct speakers. — accepted as an [Oral](https://openreview.net/forum?id=FihSkzyxdv) at ICLR 2026! 🔥
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</div>
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|
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## Overview
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VibeVoice is a **family of open-source frontier voice AI models** that includes both Text-to-Speech (TTS) and Automatic Speech Recognition (ASR) models.
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||||
A core innovation of VibeVoice is its use of continuous speech tokenizers (Acoustic and Semantic) operating at an ultra-low frame rate of **7.5 Hz**. These tokenizers efficiently preserve audio fidelity while significantly boosting computational efficiency for processing long sequences. VibeVoice employs a [next-token diffusion](https://arxiv.org/abs/2412.08635) framework, leveraging a Large Language Model (LLM) to understand textual context and dialogue flow, and a diffusion head to generate high-fidelity acoustic details.
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|
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For more information, demos, and examples, please visit our [Project Page](https://microsoft.github.io/VibeVoice).
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<div align="center">
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| Model | Weight | Quick Try |
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|-------|--------------|---------|
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| VibeVoice-ASR-7B | [HF Link](https://huggingface.co/microsoft/VibeVoice-ASR) | [Playground](https://aka.ms/vibevoice-asr) |
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| VibeVoice-TTS-1.5B | [HF Link](https://huggingface.co/microsoft/VibeVoice-1.5B) | Disabled |
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| VibeVoice-Realtime-0.5B | [HF Link](https://huggingface.co/microsoft/VibeVoice-Realtime-0.5B) | [Colab](https://colab.research.google.com/github/microsoft/VibeVoice/blob/main/demo/vibevoice_realtime_colab.ipynb) |
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</div>
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## Models
|
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### 1. 📖 [VibeVoice-ASR](docs/vibevoice-asr.md) - Long-form Speech Recognition
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|
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**VibeVoice-ASR** is a unified speech-to-text model designed to handle **60-minute long-form audio** in a single pass, generating structured transcriptions containing **Who (Speaker), When (Timestamps), and What (Content)**, with support for **Customized Hotwords**.
|
||||
|
||||
- **🕒 60-minute Single-Pass Processing**:
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Unlike conventional ASR models that slice audio into short chunks (often losing global context), VibeVoice ASR accepts up to **60 minutes** of continuous audio input within 64K token length. This ensures consistent speaker tracking and semantic coherence across the entire hour.
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- **👤 Customized Hotwords**:
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||||
Users can provide customized hotwords (e.g., specific names, technical terms, or background info) to guide the recognition process, significantly improving accuracy on domain-specific content.
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||||
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- **📝 Rich Transcription (Who, When, What)**:
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The model jointly performs ASR, diarization, and timestamping, producing a structured output that indicates *who* said *what* and *when*.
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|
||||
[📖 Documentation](docs/vibevoice-asr.md) | [🤗 Hugging Face](https://huggingface.co/microsoft/VibeVoice-ASR) | [🎮 Playground](https://aka.ms/vibevoice-asr) | [🛠️ Finetuning](finetuning-asr/README.md) | [📊 Paper](docs/VibeVoice-ASR-Report.pdf)
|
||||
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||||
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<p align="center">
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<img src="Figures/DER.jpg" alt="DER" width="50%"><br>
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||||
<img src="Figures/cpWER.jpg" alt="cpWER" width="50%"><br>
|
||||
<img src="Figures/tcpWER.jpg" alt="tcpWER" width="50%">
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</p>
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||||
|
||||
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||||
<div align="center" id="vibevoice-asr">
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||||
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||||
https://github.com/user-attachments/assets/acde5602-dc17-4314-9e3b-c630bc84aefa
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|
||||
</div>
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||||
<br>
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||||
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||||
### 2. 🎙️ [VibeVoice-TTS](docs/vibevoice-tts.md) - Long-form Multi-speaker TTS
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||||
**Best for**: Long-form conversational audio, podcasts, multi-speaker dialogues
|
||||
|
||||
- **⏱️ 90-minute Long-form Generation**:
|
||||
Synthesizes conversational/single-speaker speech up to **90 minutes** in a single pass, maintaining speaker consistency and semantic coherence throughout.
|
||||
|
||||
- **👥 Multi-speaker Support**:
|
||||
Supports up to **4 distinct speakers** in a single conversation, with natural turn-taking and speaker consistency across long dialogues.
|
||||
|
||||
- **🎭 Expressive Speech**:
|
||||
Generates expressive, natural-sounding speech that captures conversational dynamics and emotional nuances.
|
||||
|
||||
- **🌐 Multi-lingual Support**:
|
||||
Supports English, Chinese and other languages.
|
||||
|
||||
|
||||
[📖 Documentation](docs/vibevoice-tts.md) | [🤗 Hugging Face](https://huggingface.co/microsoft/VibeVoice-1.5B) | [📊 Paper](https://arxiv.org/pdf/2508.19205)
|
||||
|
||||
|
||||
<div align="center">
|
||||
<img src="Figures/VibeVoice-TTS-results.jpg" alt="VibeVoice Results" width="80%">
|
||||
</div>
|
||||
|
||||
|
||||
**English**
|
||||
<div align="center">
|
||||
|
||||
https://github.com/user-attachments/assets/0967027c-141e-4909-bec8-091558b1b784
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
**Chinese**
|
||||
<div align="center">
|
||||
|
||||
https://github.com/user-attachments/assets/322280b7-3093-4c67-86e3-10be4746c88f
|
||||
|
||||
</div>
|
||||
|
||||
**Cross-Lingual**
|
||||
<div align="center">
|
||||
|
||||
https://github.com/user-attachments/assets/838d8ad9-a201-4dde-bb45-8cd3f59ce722
|
||||
|
||||
</div>
|
||||
|
||||
**Spontaneous Singing**
|
||||
<div align="center">
|
||||
|
||||
https://github.com/user-attachments/assets/6f27a8a5-0c60-4f57-87f3-7dea2e11c730
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
**Long Conversation with 4 people**
|
||||
<div align="center">
|
||||
|
||||
https://github.com/user-attachments/assets/a357c4b6-9768-495c-a576-1618f6275727
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
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||||
|
||||
<br>
|
||||
|
||||
### 3. ⚡ [VibeVoice-Streaming](docs/vibevoice-realtime-0.5b.md) - Real-time Streaming TTS
|
||||
|
||||
VibeVoice-Realtime is a **lightweight real‑time** text-to-speech model supporting **streaming text input** and **robust long-form speech generation**.
|
||||
|
||||
- Parameter size: 0.5B (deployment-friendly)
|
||||
- Real-time TTS (~300 milliseconds first audible latency)
|
||||
- Streaming text input
|
||||
- Robust long-form speech generation (~10 minutes)
|
||||
|
||||
[📖 Documentation](docs/vibevoice-realtime-0.5b.md) | [🤗 Hugging Face](https://huggingface.co/microsoft/VibeVoice-Realtime-0.5B) | [🚀 Colab](https://colab.research.google.com/github/microsoft/VibeVoice/blob/main/demo/vibevoice_realtime_colab.ipynb)
|
||||
|
||||
|
||||
<div align="center" id="generated-example-audio-vibevoice-realtime">
|
||||
|
||||
https://github.com/user-attachments/assets/0901d274-f6ae-46ef-a0fd-3c4fba4f76dc
|
||||
|
||||
</div>
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||||
|
||||
<br>
|
||||
|
||||
## Contributing
|
||||
|
||||
Please see [CONTRIBUTING.md](CONTRIBUTING.md) for detailed contribution guidelines.
|
||||
|
||||
|
||||
|
||||
## ⚠️ Risks and Limitations
|
||||
|
||||
|
||||
While efforts have been made to optimize it through various techniques, it may still produce outputs that are unexpected, biased, or inaccurate. VibeVoice inherits any biases, errors, or omissions produced by its base model (specifically, Qwen2.5 1.5b in this release).
|
||||
Potential for Deepfakes and Disinformation: High-quality synthetic speech can be misused to create convincing fake audio content for impersonation, fraud, or spreading disinformation. Users must ensure transcripts are reliable, check content accuracy, and avoid using generated content in misleading ways. Users are expected to use the generated content and to deploy the models in a lawful manner, in full compliance with all applicable laws and regulations in the relevant jurisdictions. It is best practice to disclose the use of AI when sharing AI-generated content.
|
||||
|
||||
|
||||
We do not recommend using VibeVoice in commercial or real-world applications without further testing and development. This model is intended for research and development purposes only. Please use responsibly.
|
||||
|
||||
## Star History
|
||||
|
||||
|
||||
@@ -0,0 +1,7 @@
|
||||
# WeHub 来源说明
|
||||
|
||||
- 原始项目：`microsoft/VibeVoice`
|
||||
- 原始仓库：https://github.com/microsoft/VibeVoice
|
||||
- 导入方式：上游默认分支的最新快照
|
||||
- 原作者、版权和许可证信息以原始仓库及本仓库 LICENSE 为准
|
||||
- 本文件仅用于记录来源，不代表 WeHub 是原项目作者
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
<!-- BEGIN MICROSOFT SECURITY.MD V1.0.0 BLOCK -->
|
||||
|
||||
## Security
|
||||
|
||||
Microsoft takes the security of our software products and services seriously, which
|
||||
includes all source code repositories in our GitHub organizations.
|
||||
|
||||
**Please do not report security vulnerabilities through public GitHub issues.**
|
||||
|
||||
For security reporting information, locations, contact information, and policies,
|
||||
please review the latest guidance for Microsoft repositories at
|
||||
[https://aka.ms/SECURITY.md](https://aka.ms/SECURITY.md).
|
||||
|
||||
<!-- END MICROSOFT SECURITY.MD BLOCK -->
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,48 @@
|
||||
#!/usr/bin/env bash
|
||||
set -e
|
||||
|
||||
echo "[INFO] Starting download of experimental voices..."
|
||||
|
||||
# Absolute path of the current script directory
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
|
||||
# Target directory relative to this script location
|
||||
TARGET_DIR="$SCRIPT_DIR/voices/streaming_model/experimental_voices"
|
||||
|
||||
echo "[INFO] Script directory: $SCRIPT_DIR"
|
||||
echo "[INFO] Target directory: $TARGET_DIR"
|
||||
|
||||
# Ensure the target directory exists
|
||||
echo "[INFO] Creating target directory if needed..."
|
||||
mkdir -p "$TARGET_DIR"
|
||||
|
||||
# List of archives and their URLs
|
||||
FILES=(
|
||||
"experimental_voices_de.tar.gz|https://github.com/user-attachments/files/24035887/experimental_voices_de.tar.gz"
|
||||
"experimental_voices_fr.tar.gz|https://github.com/user-attachments/files/24035880/experimental_voices_fr.tar.gz"
|
||||
"experimental_voices_jp.tar.gz|https://github.com/user-attachments/files/24035882/experimental_voices_jp.tar.gz"
|
||||
"experimental_voices_kr.tar.gz|https://github.com/user-attachments/files/24035883/experimental_voices_kr.tar.gz"
|
||||
"experimental_voices_pl.tar.gz|https://github.com/user-attachments/files/24035885/experimental_voices_pl.tar.gz"
|
||||
"experimental_voices_pt.tar.gz|https://github.com/user-attachments/files/24035886/experimental_voices_pt.tar.gz"
|
||||
"experimental_voices_sp.tar.gz|https://github.com/user-attachments/files/24035884/experimental_voices_sp.tar.gz"
|
||||
"experimental_voices_en1.tar.gz|https://github.com/user-attachments/files/24189272/experimental_voices_en1.tar.gz"
|
||||
"experimental_voices_en2.tar.gz|https://github.com/user-attachments/files/24189273/experimental_voices_en2.tar.gz"
|
||||
)
|
||||
|
||||
# Download, extract, and clean up each archive
|
||||
for entry in "${FILES[@]}"; do
|
||||
IFS="|" read -r FNAME URL <<< "$entry"
|
||||
|
||||
echo "[INFO] Downloading $FNAME ..."
|
||||
wget -O "$FNAME" "$URL"
|
||||
|
||||
echo "[INFO] Extracting $FNAME ..."
|
||||
tar -xzvf "$FNAME" -C "$TARGET_DIR"
|
||||
|
||||
echo "[INFO] Cleaning up $FNAME ..."
|
||||
rm -f "$FNAME"
|
||||
done
|
||||
|
||||
echo "[SUCCESS] All experimental speakers installed successfully!"
|
||||
echo "[SUCCESS] Speakers are located at:"
|
||||
echo " $TARGET_DIR"
|
||||
@@ -0,0 +1,311 @@
|
||||
import argparse
|
||||
import os
|
||||
import re
|
||||
import traceback
|
||||
from typing import List, Tuple, Union, Dict, Any
|
||||
import time
|
||||
import torch
|
||||
import copy
|
||||
import glob
|
||||
|
||||
from vibevoice.modular.modeling_vibevoice_streaming_inference import VibeVoiceStreamingForConditionalGenerationInference
|
||||
from vibevoice.processor.vibevoice_streaming_processor import VibeVoiceStreamingProcessor
|
||||
from transformers.cache_utils import DynamicCache
|
||||
from transformers.modeling_outputs import BaseModelOutputWithPast
|
||||
from transformers.utils import logging
|
||||
|
||||
logging.set_verbosity_info()
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class VoiceMapper:
|
||||
"""Maps speaker names to voice file paths"""
|
||||
|
||||
def __init__(self):
|
||||
self.setup_voice_presets()
|
||||
# for k, v in self.voice_presets.items():
|
||||
# print(f"{k}: {v}")
|
||||
|
||||
def setup_voice_presets(self):
|
||||
"""Setup voice presets by scanning the voices directory."""
|
||||
voices_dir = os.path.join(os.path.dirname(__file__), "voices/streaming_model")
|
||||
|
||||
# Check if voices directory exists
|
||||
if not os.path.exists(voices_dir):
|
||||
print(f"Warning: Voices directory not found at {voices_dir}")
|
||||
self.voice_presets = {}
|
||||
self.available_voices = {}
|
||||
return
|
||||
|
||||
# Scan for all VOICE files in the voices directory
|
||||
self.voice_presets = {}
|
||||
|
||||
# Get all .pt files in the voices directory
|
||||
pt_files = glob.glob(os.path.join(voices_dir, "**", "*.pt"), recursive=True)
|
||||
|
||||
# Create dictionary with filename (without extension) as key
|
||||
for pt_file in pt_files:
|
||||
# key: filename without extension
|
||||
name = os.path.splitext(os.path.basename(pt_file))[0].lower()
|
||||
full_path = os.path.abspath(pt_file)
|
||||
self.voice_presets[name] = full_path
|
||||
|
||||
# Sort the voice presets alphabetically by name for better UI
|
||||
self.voice_presets = dict(sorted(self.voice_presets.items()))
|
||||
|
||||
# Filter out voices that don't exist (this is now redundant but kept for safety)
|
||||
self.available_voices = {
|
||||
name: path for name, path in self.voice_presets.items()
|
||||
if os.path.exists(path)
|
||||
}
|
||||
|
||||
print(f"Found {len(self.available_voices)} voice files in {voices_dir}")
|
||||
print(f"Available voices: {', '.join(self.available_voices.keys())}")
|
||||
|
||||
def get_voice_path(self, speaker_name: str) -> str:
|
||||
"""Get voice file path for a given speaker name"""
|
||||
# First try exact match
|
||||
speaker_name = speaker_name.lower()
|
||||
if speaker_name in self.voice_presets:
|
||||
return self.voice_presets[speaker_name]
|
||||
|
||||
# Try partial matching (case insensitive)
|
||||
matched_path = None
|
||||
for preset_name, path in self.voice_presets.items():
|
||||
if preset_name.lower() in speaker_name or speaker_name in preset_name.lower():
|
||||
if matched_path is not None:
|
||||
raise ValueError(f"Multiple voice presets match the speaker name '{speaker_name}', please make the speaker_name more specific.")
|
||||
matched_path = path
|
||||
if matched_path is not None:
|
||||
return matched_path
|
||||
|
||||
# Default to first voice if no match found
|
||||
default_voice = list(self.voice_presets.values())[0]
|
||||
print(f"Warning: No voice preset found for '{speaker_name}', using default voice: {default_voice}")
|
||||
return default_voice
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description="VibeVoiceStreaming Processor TXT Input Test")
|
||||
parser.add_argument(
|
||||
"--model_path",
|
||||
type=str,
|
||||
default="microsoft/VibeVoice-Realtime-0.5B",
|
||||
help="Path to the HuggingFace model directory",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--txt_path",
|
||||
type=str,
|
||||
default="demo/text_examples/1p_vibevoice.txt",
|
||||
help="Path to the txt file containing the script",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--speaker_name",
|
||||
type=str,
|
||||
default="Wayne",
|
||||
help="Single speaker name (e.g., --speaker_name Wayne)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output_dir",
|
||||
type=str,
|
||||
default="./outputs",
|
||||
help="Directory to save output audio files",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default=("cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")),
|
||||
help="Device for inference: cuda | mps | cpu",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--cfg_scale",
|
||||
type=float,
|
||||
default=1.5,
|
||||
help="CFG (Classifier-Free Guidance) scale for generation (default: 1.5)",
|
||||
)
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
|
||||
# Normalize potential 'mpx' typo to 'mps'
|
||||
if args.device.lower() == "mpx":
|
||||
print("Note: device 'mpx' detected, treating it as 'mps'.")
|
||||
args.device = "mps"
|
||||
|
||||
# Validate mps availability if requested
|
||||
if args.device == "mps" and not torch.backends.mps.is_available():
|
||||
print("Warning: MPS not available. Falling back to CPU.")
|
||||
args.device = "cpu"
|
||||
|
||||
print(f"Using device: {args.device}")
|
||||
|
||||
# Initialize voice mapper
|
||||
voice_mapper = VoiceMapper()
|
||||
|
||||
# Check if txt file exists
|
||||
if not os.path.exists(args.txt_path):
|
||||
print(f"Error: txt file not found: {args.txt_path}")
|
||||
return
|
||||
|
||||
# Read and parse txt file
|
||||
print(f"Reading script from: {args.txt_path}")
|
||||
with open(args.txt_path, 'r', encoding='utf-8') as f:
|
||||
scripts = f.read().strip()
|
||||
|
||||
if not scripts:
|
||||
print("Error: No valid scripts found in the txt file")
|
||||
return
|
||||
|
||||
full_script = scripts.replace("’", "'").replace('“', '"').replace('”', '"')
|
||||
|
||||
print(f"Loading processor & model from {args.model_path}")
|
||||
processor = VibeVoiceStreamingProcessor.from_pretrained(args.model_path)
|
||||
|
||||
# Decide dtype & attention implementation
|
||||
if args.device == "mps":
|
||||
load_dtype = torch.float32 # MPS requires float32
|
||||
attn_impl_primary = "sdpa" # flash_attention_2 not supported on MPS
|
||||
elif args.device == "cuda":
|
||||
load_dtype = torch.bfloat16
|
||||
attn_impl_primary = "flash_attention_2"
|
||||
else: # cpu
|
||||
load_dtype = torch.float32
|
||||
attn_impl_primary = "sdpa"
|
||||
print(f"Using device: {args.device}, torch_dtype: {load_dtype}, attn_implementation: {attn_impl_primary}")
|
||||
# Load model with device-specific logic
|
||||
try:
|
||||
if args.device == "mps":
|
||||
model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
|
||||
args.model_path,
|
||||
torch_dtype=load_dtype,
|
||||
attn_implementation=attn_impl_primary,
|
||||
device_map=None, # load then move
|
||||
)
|
||||
model.to("mps")
|
||||
elif args.device == "cuda":
|
||||
model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
|
||||
args.model_path,
|
||||
torch_dtype=load_dtype,
|
||||
device_map="cuda",
|
||||
attn_implementation=attn_impl_primary,
|
||||
)
|
||||
else: # cpu
|
||||
model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
|
||||
args.model_path,
|
||||
torch_dtype=load_dtype,
|
||||
device_map="cpu",
|
||||
attn_implementation=attn_impl_primary,
|
||||
)
|
||||
except Exception as e:
|
||||
if attn_impl_primary == 'flash_attention_2':
|
||||
print(f"[ERROR] : {type(e).__name__}: {e}")
|
||||
print(traceback.format_exc())
|
||||
print("Error loading the model. Trying to use SDPA. However, note that only flash_attention_2 has been fully tested, and using SDPA may result in lower audio quality.")
|
||||
model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
|
||||
args.model_path,
|
||||
torch_dtype=load_dtype,
|
||||
device_map=(args.device if args.device in ("cuda", "cpu") else None),
|
||||
attn_implementation='sdpa'
|
||||
)
|
||||
if args.device == "mps":
|
||||
model.to("mps")
|
||||
else:
|
||||
raise e
|
||||
|
||||
|
||||
model.eval()
|
||||
model.set_ddpm_inference_steps(num_steps=5)
|
||||
|
||||
if hasattr(model.model, 'language_model'):
|
||||
print(f"Language model attention: {model.model.language_model.config._attn_implementation}")
|
||||
|
||||
target_device = args.device if args.device != "cpu" else "cpu"
|
||||
voice_sample = voice_mapper.get_voice_path(args.speaker_name)
|
||||
print(f"Using voice preset for {args.speaker_name}: {voice_sample}")
|
||||
with torch.serialization.safe_globals([BaseModelOutputWithPast, DynamicCache]):
|
||||
all_prefilled_outputs = torch.load(voice_sample, map_location=target_device, weights_only=True)
|
||||
|
||||
# Prepare inputs for the model
|
||||
inputs = processor.process_input_with_cached_prompt(
|
||||
text=full_script,
|
||||
cached_prompt=all_prefilled_outputs,
|
||||
padding=True,
|
||||
return_tensors="pt",
|
||||
return_attention_mask=True,
|
||||
)
|
||||
|
||||
# Move tensors to target device
|
||||
for k, v in inputs.items():
|
||||
if torch.is_tensor(v):
|
||||
inputs[k] = v.to(target_device)
|
||||
|
||||
print(f"Starting generation with cfg_scale: {args.cfg_scale}")
|
||||
|
||||
# Generate audio
|
||||
start_time = time.time()
|
||||
outputs = model.generate(
|
||||
**inputs,
|
||||
max_new_tokens=None,
|
||||
cfg_scale=args.cfg_scale,
|
||||
tokenizer=processor.tokenizer,
|
||||
generation_config={'do_sample': False},
|
||||
verbose=True,
|
||||
all_prefilled_outputs=copy.deepcopy(all_prefilled_outputs) if all_prefilled_outputs is not None else None,
|
||||
)
|
||||
generation_time = time.time() - start_time
|
||||
print(f"Generation time: {generation_time:.2f} seconds")
|
||||
|
||||
# Calculate audio duration and additional metrics
|
||||
if outputs.speech_outputs and outputs.speech_outputs[0] is not None:
|
||||
# Assuming 24kHz sample rate (common for speech synthesis)
|
||||
sample_rate = 24000
|
||||
audio_samples = outputs.speech_outputs[0].shape[-1] if len(outputs.speech_outputs[0].shape) > 0 else len(outputs.speech_outputs[0])
|
||||
audio_duration = audio_samples / sample_rate
|
||||
rtf = generation_time / audio_duration if audio_duration > 0 else float('inf')
|
||||
|
||||
print(f"Generated audio duration: {audio_duration:.2f} seconds")
|
||||
print(f"RTF (Real Time Factor): {rtf:.2f}x")
|
||||
else:
|
||||
print("No audio output generated")
|
||||
return
|
||||
|
||||
# Calculate token metrics
|
||||
input_tokens = inputs['tts_text_ids'].shape[1] # Number of input tokens
|
||||
output_tokens = outputs.sequences.shape[1] # Total tokens (input + generated)
|
||||
generated_tokens = output_tokens - input_tokens - all_prefilled_outputs['tts_lm']['last_hidden_state'].size(1)
|
||||
|
||||
print(f"Prefilling text tokens: {input_tokens}")
|
||||
print(f"Generated speech tokens: {generated_tokens}")
|
||||
print(f"Total tokens: {output_tokens}")
|
||||
|
||||
# Save output (processor handles device internally)
|
||||
txt_filename = os.path.splitext(os.path.basename(args.txt_path))[0]
|
||||
output_path = os.path.join(args.output_dir, f"{txt_filename}_generated.wav")
|
||||
os.makedirs(args.output_dir, exist_ok=True)
|
||||
|
||||
processor.save_audio(
|
||||
outputs.speech_outputs[0], # First (and only) batch item
|
||||
output_path=output_path,
|
||||
)
|
||||
print(f"Saved output to {output_path}")
|
||||
|
||||
# Print summary
|
||||
print("\n" + "="*50)
|
||||
print("GENERATION SUMMARY")
|
||||
print("="*50)
|
||||
print(f"Input file: {args.txt_path}")
|
||||
print(f"Output file: {output_path}")
|
||||
print(f"Speaker names: {args.speaker_name}")
|
||||
print(f"Prefilling text tokens: {input_tokens}")
|
||||
print(f"Generated speech tokens: {generated_tokens}")
|
||||
print(f"Total tokens: {output_tokens}")
|
||||
print(f"Generation time: {generation_time:.2f} seconds")
|
||||
print(f"Audio duration: {audio_duration:.2f} seconds")
|
||||
print(f"RTF (Real Time Factor): {rtf:.2f}x")
|
||||
|
||||
print("="*50)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,2 @@
|
||||
Generating long-form, multi-speaker conversational audio like podcasts poses significant challenges for traditional Text-to-Speech (TTS) systems, particularly in scalability, speaker consistency, and natural turn-taking. This report presents VibeVoice, a novel model designed to synthesize long-form speech with multiple speakers by employing the next-token diffusion framework, a unified method for modeling continuous data by autoregressively generating latent vectors via diffusion.
|
||||
A core component of our approach is the continuous speech tokenizers operating at an ultra-low frame rate of 7.5. This tokenizer effectively preserves audio fidelity while significantly boosting computational efficiency for processing long sequences. This enables VibeVoice to synthesize long-form speech for up to 90 minutes (in a 64K context window length) with up to 4 speakers, capturing the authentic conversational "vibe" and surpassing all known open-source and closed-source dialogue models (for example, Gemini 2.5 Pro Preview TTS). Code and checkpoint are available now.
|
||||
@@ -0,0 +1 @@
|
||||
VibeVoice is a novel framework designed for generating expressive, long-form, multi-speaker conversational audio, such as podcasts, from text. It addresses significant challenges in traditional Text-to-Speech (TTS) systems, particularly in scalability, speaker consistency, and natural turn-taking. A core innovation of VibeVoice is its use of continuous speech tokenizers operating at an ultra-low frame rate of 7.5 Hz. These tokenizers efficiently preserve audio fidelity while significantly boosting computational efficiency for processing long sequences. VibeVoice employs a next-token diffusion framework, leveraging a Large Language Model to understand textual context and dialogue flow, and a diffusion head to generate high-fidelity acoustic details. The model can synthesize speech up to 90 minutes long with up to 4 distinct speakers, surpassing the typical 1-2 speaker limits of many prior models.
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,581 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
VibeVoice ASR Batch Inference Demo Script
|
||||
|
||||
This script supports batch inference for ASR model and compares results
|
||||
between batch processing and single-sample processing.
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import torch
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
import argparse
|
||||
import time
|
||||
import json
|
||||
import re
|
||||
from typing import List, Dict, Any, Optional
|
||||
from functools import wraps
|
||||
|
||||
from vibevoice.modular.modeling_vibevoice_asr import VibeVoiceASRForConditionalGeneration
|
||||
from vibevoice.processor.vibevoice_asr_processor import VibeVoiceASRProcessor
|
||||
from vibevoice.processor.audio_utils import COMMON_AUDIO_EXTS
|
||||
|
||||
|
||||
class VibeVoiceASRBatchInference:
|
||||
"""Batch inference wrapper for VibeVoice ASR model."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_path: str,
|
||||
device: str = "cuda",
|
||||
dtype: torch.dtype = torch.bfloat16,
|
||||
attn_implementation: str = "sdpa"
|
||||
):
|
||||
"""
|
||||
Initialize the ASR batch inference pipeline.
|
||||
|
||||
Args:
|
||||
model_path: Path to the pretrained model
|
||||
device: Device to run inference on (cuda, mps, xpu, cpu, auto)
|
||||
dtype: Data type for model weights
|
||||
attn_implementation: Attention implementation to use ('flash_attention_2', 'sdpa', 'eager')
|
||||
"""
|
||||
print(f"Loading VibeVoice ASR model from {model_path}")
|
||||
|
||||
# Load processor
|
||||
self.processor = VibeVoiceASRProcessor.from_pretrained(
|
||||
model_path,
|
||||
language_model_pretrained_name="Qwen/Qwen2.5-7B"
|
||||
)
|
||||
|
||||
# Load model with specified attention implementation
|
||||
print(f"Using attention implementation: {attn_implementation}")
|
||||
self.model = VibeVoiceASRForConditionalGeneration.from_pretrained(
|
||||
model_path,
|
||||
dtype=dtype,
|
||||
device_map=device if device == "auto" else None,
|
||||
attn_implementation=attn_implementation,
|
||||
trust_remote_code=True
|
||||
)
|
||||
|
||||
if device != "auto":
|
||||
self.model = self.model.to(device)
|
||||
|
||||
self.device = device if device != "auto" else next(self.model.parameters()).device
|
||||
self.dtype = dtype
|
||||
self.model.eval()
|
||||
|
||||
print(f"Model loaded successfully on {self.device}")
|
||||
|
||||
def _prepare_generation_config(
|
||||
self,
|
||||
max_new_tokens: int = 512,
|
||||
temperature: float = 0.0,
|
||||
top_p: float = 0.9,
|
||||
do_sample: bool = True,
|
||||
num_beams: int = 1,
|
||||
) -> dict:
|
||||
"""Prepare generation configuration."""
|
||||
config = {
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"pad_token_id": self.processor.pad_id,
|
||||
"eos_token_id": self.processor.tokenizer.eos_token_id,
|
||||
}
|
||||
|
||||
# Beam search vs sampling
|
||||
if num_beams > 1:
|
||||
config["num_beams"] = num_beams
|
||||
config["do_sample"] = False # Beam search doesn't use sampling
|
||||
else:
|
||||
config["do_sample"] = do_sample
|
||||
# Only set temperature and top_p when sampling is enabled
|
||||
if do_sample:
|
||||
config["temperature"] = temperature
|
||||
config["top_p"] = top_p
|
||||
|
||||
return config
|
||||
|
||||
def transcribe_batch(
|
||||
self,
|
||||
audio_inputs: List,
|
||||
max_new_tokens: int = 512,
|
||||
temperature: float = 0.0,
|
||||
top_p: float = 1.0,
|
||||
do_sample: bool = True,
|
||||
num_beams: int = 1,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Transcribe multiple audio files/arrays in a single batch.
|
||||
|
||||
Args:
|
||||
audio_inputs: List of audio file paths or (array, sampling_rate) tuples
|
||||
max_new_tokens: Maximum tokens to generate
|
||||
temperature: Temperature for sampling
|
||||
top_p: Top-p for nucleus sampling
|
||||
do_sample: Whether to use sampling
|
||||
|
||||
Returns:
|
||||
List of transcription results
|
||||
"""
|
||||
if len(audio_inputs) == 0:
|
||||
return []
|
||||
|
||||
batch_size = len(audio_inputs)
|
||||
print(f"\nProcessing batch of {batch_size} audio(s)...")
|
||||
|
||||
# Process all audio together
|
||||
inputs = self.processor(
|
||||
audio=audio_inputs,
|
||||
sampling_rate=None,
|
||||
return_tensors="pt",
|
||||
padding=True,
|
||||
add_generation_prompt=True
|
||||
)
|
||||
|
||||
# Move to device
|
||||
inputs = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v
|
||||
for k, v in inputs.items()}
|
||||
|
||||
# Print batch info
|
||||
print(f" Input IDs shape: {inputs['input_ids'].shape}")
|
||||
print(f" Speech tensors shape: {inputs['speech_tensors'].shape}")
|
||||
print(f" Attention mask shape: {inputs['attention_mask'].shape}")
|
||||
|
||||
# Generate
|
||||
generation_config = self._prepare_generation_config(
|
||||
max_new_tokens=max_new_tokens,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
do_sample=do_sample,
|
||||
num_beams=num_beams,
|
||||
)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
with torch.no_grad():
|
||||
output_ids = self.model.generate(
|
||||
**inputs,
|
||||
**generation_config
|
||||
)
|
||||
|
||||
generation_time = time.time() - start_time
|
||||
|
||||
# Decode outputs for each sample in the batch
|
||||
results = []
|
||||
input_length = inputs['input_ids'].shape[1]
|
||||
|
||||
for i, audio_input in enumerate(audio_inputs):
|
||||
# Get generated tokens for this sample (excluding input tokens)
|
||||
generated_ids = output_ids[i, input_length:]
|
||||
|
||||
# Remove padding tokens from the end
|
||||
# Find the first eos_token or pad_token
|
||||
eos_positions = (generated_ids == self.processor.tokenizer.eos_token_id).nonzero(as_tuple=True)[0]
|
||||
if len(eos_positions) > 0:
|
||||
generated_ids = generated_ids[:eos_positions[0] + 1]
|
||||
|
||||
generated_text = self.processor.decode(generated_ids, skip_special_tokens=True)
|
||||
|
||||
# Parse structured output
|
||||
try:
|
||||
transcription_segments = self.processor.post_process_transcription(generated_text)
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to parse structured output: {e}")
|
||||
transcription_segments = []
|
||||
|
||||
# Get file name based on input type
|
||||
if isinstance(audio_input, str):
|
||||
file_name = audio_input
|
||||
elif isinstance(audio_input, dict) and 'id' in audio_input:
|
||||
file_name = audio_input['id']
|
||||
else:
|
||||
file_name = f"audio_{i}"
|
||||
|
||||
results.append({
|
||||
"file": file_name,
|
||||
"raw_text": generated_text,
|
||||
"segments": transcription_segments,
|
||||
"generation_time": generation_time / batch_size,
|
||||
})
|
||||
|
||||
print(f" Total generation time: {generation_time:.2f}s")
|
||||
print(f" Average time per sample: {generation_time/batch_size:.2f}s")
|
||||
|
||||
return results
|
||||
|
||||
def transcribe_with_batching(
|
||||
self,
|
||||
audio_inputs: List,
|
||||
batch_size: int = 4,
|
||||
max_new_tokens: int = 512,
|
||||
temperature: float = 0.0,
|
||||
top_p: float = 1.0,
|
||||
do_sample: bool = True,
|
||||
num_beams: int = 1,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Transcribe multiple audio files/arrays with automatic batching.
|
||||
|
||||
Args:
|
||||
audio_inputs: List of audio file paths or (array, sampling_rate) tuples
|
||||
batch_size: Number of samples per batch
|
||||
max_new_tokens: Maximum tokens to generate
|
||||
temperature: Temperature for sampling
|
||||
top_p: Top-p for nucleus sampling
|
||||
do_sample: Whether to use sampling
|
||||
|
||||
Returns:
|
||||
List of transcription results
|
||||
"""
|
||||
all_results = []
|
||||
|
||||
# Process in batches
|
||||
for i in range(0, len(audio_inputs), batch_size):
|
||||
batch_inputs = audio_inputs[i:i + batch_size]
|
||||
print(f"\n{'='*60}")
|
||||
print(f"Processing batch {i//batch_size + 1}/{(len(audio_inputs) + batch_size - 1)//batch_size}")
|
||||
|
||||
batch_results = self.transcribe_batch(
|
||||
batch_inputs,
|
||||
max_new_tokens=max_new_tokens,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
do_sample=do_sample,
|
||||
num_beams=num_beams,
|
||||
)
|
||||
all_results.extend(batch_results)
|
||||
|
||||
return all_results
|
||||
|
||||
|
||||
def print_result(result: Dict[str, Any]):
|
||||
"""Pretty print a single transcription result."""
|
||||
print(f"\nFile: {result['file']}")
|
||||
print(f"Generation Time: {result['generation_time']:.2f}s")
|
||||
print(f"\n--- Raw Output ---")
|
||||
print(result['raw_text'][:500] + "..." if len(result['raw_text']) > 500 else result['raw_text'])
|
||||
|
||||
if result['segments']:
|
||||
print(f"\n--- Structured Output ({len(result['segments'])} segments) ---")
|
||||
for seg in result['segments'][:50]: # Show first 50 segments
|
||||
print(f"[{seg.get('start_time', 'N/A')} - {seg.get('end_time', 'N/A')}] "
|
||||
f"Speaker {seg.get('speaker_id', 'N/A')}: {seg.get('text', '')}...")
|
||||
if len(result['segments']) > 50:
|
||||
print(f" ... and {len(result['segments']) - 50} more segments")
|
||||
|
||||
|
||||
def load_dataset_and_concatenate(
|
||||
dataset_name: str,
|
||||
split: str,
|
||||
max_duration: float,
|
||||
num_audios: int,
|
||||
target_sr: int = 24000
|
||||
) -> Optional[List[np.ndarray]]:
|
||||
"""
|
||||
Load a HuggingFace dataset and concatenate audio samples into long audio chunks.
|
||||
(Note, just for demo purpose, not for benchmark evaluation)
|
||||
|
||||
Args:
|
||||
dataset_name: HuggingFace dataset name (e.g., 'openslr/librispeech_asr')
|
||||
split: Dataset split to use (e.g., 'test', 'test.other')
|
||||
max_duration: Maximum duration in seconds for each concatenated audio
|
||||
num_audios: Number of concatenated audios to create
|
||||
target_sr: Target sample rate (default: 24000)
|
||||
|
||||
Returns:
|
||||
List of concatenated audio arrays, or None if loading fails
|
||||
"""
|
||||
try:
|
||||
from datasets import load_dataset
|
||||
import torchcodec # just for decode audio in datasets
|
||||
except ImportError:
|
||||
print("Please install it with: pip install datasets torchcodec")
|
||||
return None
|
||||
|
||||
print(f"\nLoading dataset: {dataset_name} (split: {split})")
|
||||
print(f"Will create {num_audios} concatenated audio(s), each up to {max_duration:.1f}s ({max_duration/3600:.2f} hours)")
|
||||
|
||||
try:
|
||||
# Use streaming to avoid downloading the entire dataset
|
||||
dataset = load_dataset(dataset_name, split=split, streaming=True)
|
||||
print(f"Dataset loaded in streaming mode")
|
||||
|
||||
concatenated_audios = [] # List of concatenated audio metadata
|
||||
|
||||
# Create multiple concatenated audios based on num_audios
|
||||
current_chunks = []
|
||||
current_duration = 0.0
|
||||
current_samples_used = 0
|
||||
sample_idx = 0
|
||||
|
||||
for sample in dataset:
|
||||
if len(concatenated_audios) >= num_audios:
|
||||
break
|
||||
|
||||
if 'audio' not in sample:
|
||||
continue
|
||||
|
||||
audio_data = sample['audio']
|
||||
audio_array = audio_data['array']
|
||||
sr = audio_data['sampling_rate']
|
||||
|
||||
# Resample if needed
|
||||
if sr != target_sr:
|
||||
duration = len(audio_array) / sr
|
||||
new_length = int(duration * target_sr)
|
||||
audio_array = np.interp(
|
||||
np.linspace(0, len(audio_array) - 1, new_length),
|
||||
np.arange(len(audio_array)),
|
||||
audio_array
|
||||
)
|
||||
|
||||
chunk_duration = len(audio_array) / target_sr
|
||||
|
||||
# Check if adding this chunk exceeds max_duration
|
||||
if current_duration + chunk_duration > max_duration:
|
||||
remaining_duration = max_duration - current_duration
|
||||
if remaining_duration > 0.5: # Only add if > 0.5s remaining
|
||||
samples_to_take = int(remaining_duration * target_sr)
|
||||
current_chunks.append(audio_array[:samples_to_take])
|
||||
current_duration += remaining_duration
|
||||
current_samples_used += 1
|
||||
|
||||
# Save current concatenated audio and start a new one
|
||||
if current_chunks:
|
||||
concatenated_audios.append({
|
||||
'array': np.concatenate(current_chunks),
|
||||
'duration': current_duration,
|
||||
'samples_used': current_samples_used,
|
||||
})
|
||||
print(f" Created audio {len(concatenated_audios)}: {current_duration:.1f}s from {current_samples_used} samples")
|
||||
|
||||
# Reset for next concatenated audio
|
||||
current_chunks = []
|
||||
current_duration = 0.0
|
||||
current_samples_used = 0
|
||||
|
||||
if len(concatenated_audios) >= num_audios:
|
||||
break
|
||||
|
||||
current_chunks.append(audio_array)
|
||||
current_duration += chunk_duration
|
||||
current_samples_used += 1
|
||||
|
||||
sample_idx += 1
|
||||
if sample_idx % 100 == 0:
|
||||
print(f" Processed {sample_idx} samples...")
|
||||
|
||||
# Don't forget the last batch if it has content
|
||||
if current_chunks and len(concatenated_audios) < num_audios:
|
||||
concatenated_audios.append({
|
||||
'array': np.concatenate(current_chunks),
|
||||
'duration': current_duration,
|
||||
'samples_used': current_samples_used,
|
||||
})
|
||||
print(f" Created audio {len(concatenated_audios)}: {current_duration:.1f}s from {current_samples_used} samples")
|
||||
|
||||
if not concatenated_audios:
|
||||
print("Warning: No audio samples found in dataset")
|
||||
return None
|
||||
|
||||
# Extract arrays and print summary
|
||||
result = [a['array'] for a in concatenated_audios]
|
||||
total_duration = sum(a['duration'] for a in concatenated_audios)
|
||||
total_samples = sum(a['samples_used'] for a in concatenated_audios)
|
||||
print(f"\nCreated {len(result)} concatenated audio(s), total {total_duration:.1f}s ({total_duration/60:.1f} min) from {total_samples} samples")
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error loading dataset: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return None
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="VibeVoice ASR Batch Inference Demo")
|
||||
parser.add_argument(
|
||||
"--model_path",
|
||||
type=str,
|
||||
default="",
|
||||
help="Path to the model checkpoint"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--audio_files",
|
||||
type=str,
|
||||
nargs='+',
|
||||
required=False,
|
||||
help="Paths to audio files for transcription"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--audio_dir",
|
||||
type=str,
|
||||
required=False,
|
||||
help="Directory containing audio files for batch transcription"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
required=False,
|
||||
help="HuggingFace dataset name (e.g., 'openslr/librispeech_asr')"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--split",
|
||||
type=str,
|
||||
default="test",
|
||||
help="Dataset split to use (e.g., 'test', 'test.other', 'test.clean')"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max_duration",
|
||||
type=float,
|
||||
default=3600.0,
|
||||
help="Maximum duration in seconds for concatenated dataset audio (default: 3600 = 1 hour)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--batch_size",
|
||||
type=int,
|
||||
default=2,
|
||||
help="Batch size for processing multiple files"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default="cuda" if torch.cuda.is_available() else ("xpu" if torch.backends.xpu.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu") ),
|
||||
choices=["cuda", "cpu", "mps","xpu", "auto"],
|
||||
help="Device to run inference on"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max_new_tokens",
|
||||
type=int,
|
||||
default=32768,
|
||||
help="Maximum number of tokens to generate"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--temperature",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="Temperature for sampling (0 = greedy decoding)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--top_p",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="Top-p for nucleus sampling"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--num_beams",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Number of beams for beam search. Use 1 for greedy/sampling"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--attn_implementation",
|
||||
type=str,
|
||||
default="auto",
|
||||
choices=["flash_attention_2", "sdpa", "eager", "auto"],
|
||||
help="Attention implementation to use. 'auto' will select the best available for your device (flash_attention_2 for CUDA, sdpa for MPS/CPU/XPU)"
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Auto-detect best attention implementation based on device
|
||||
if args.attn_implementation == "auto":
|
||||
if args.device == "cuda" and torch.cuda.is_available():
|
||||
try:
|
||||
import flash_attn
|
||||
args.attn_implementation = "flash_attention_2"
|
||||
except ImportError:
|
||||
print("flash_attn not installed, falling back to sdpa")
|
||||
args.attn_implementation = "sdpa"
|
||||
else:
|
||||
# MPS/XPU/CPU don't support flash_attention_2
|
||||
args.attn_implementation = "sdpa"
|
||||
print(f"Auto-detected attention implementation: {args.attn_implementation}")
|
||||
|
||||
# Collect audio files
|
||||
audio_files = []
|
||||
concatenated_audio = None # For storing concatenated dataset audio
|
||||
|
||||
if args.audio_files:
|
||||
audio_files.extend(args.audio_files)
|
||||
|
||||
if args.audio_dir:
|
||||
supported = set(e.lower() for e in COMMON_AUDIO_EXTS)
|
||||
for f in os.listdir(args.audio_dir):
|
||||
if os.path.splitext(f)[1].lower() in supported:
|
||||
audio_files.append(os.path.join(args.audio_dir, f))
|
||||
|
||||
if args.dataset:
|
||||
concatenated_audio = load_dataset_and_concatenate(
|
||||
dataset_name=args.dataset,
|
||||
split=args.split,
|
||||
max_duration=args.max_duration,
|
||||
num_audios=args.batch_size,
|
||||
)
|
||||
if concatenated_audio is None:
|
||||
return
|
||||
|
||||
if len(audio_files) == 0 and concatenated_audio is None:
|
||||
print("No audio files provided. Please specify --audio_files, --audio_dir, or --dataset.")
|
||||
return
|
||||
|
||||
if audio_files:
|
||||
print(f"\nAudio files to process ({len(audio_files)}):")
|
||||
for f in audio_files:
|
||||
print(f" - {f}")
|
||||
|
||||
if concatenated_audio:
|
||||
print(f"\nConcatenated dataset audios: {len(concatenated_audio)} audio(s)")
|
||||
|
||||
# Initialize model
|
||||
# Handle MPS device and dtype
|
||||
if args.device == "mps":
|
||||
model_dtype = torch.float32 # MPS works better with float32
|
||||
elif args.device == "xpu":
|
||||
model_dtype = torch.float32
|
||||
elif args.device == "cpu":
|
||||
model_dtype = torch.float32
|
||||
else:
|
||||
model_dtype = torch.bfloat16
|
||||
|
||||
asr = VibeVoiceASRBatchInference(
|
||||
model_path=args.model_path,
|
||||
device=args.device,
|
||||
dtype=model_dtype,
|
||||
attn_implementation=args.attn_implementation
|
||||
)
|
||||
|
||||
# If temperature is 0, use greedy decoding (no sampling)
|
||||
do_sample = args.temperature > 0
|
||||
|
||||
# Combine all audio inputs
|
||||
all_audio_inputs = audio_files + (concatenated_audio or [])
|
||||
|
||||
print("\n" + "="*80)
|
||||
print(f"Processing {len(all_audio_inputs)} audio(s)")
|
||||
print("="*80)
|
||||
|
||||
all_results = asr.transcribe_with_batching(
|
||||
all_audio_inputs,
|
||||
batch_size=args.batch_size,
|
||||
max_new_tokens=args.max_new_tokens,
|
||||
temperature=args.temperature,
|
||||
top_p=args.top_p,
|
||||
do_sample=do_sample,
|
||||
num_beams=args.num_beams,
|
||||
)
|
||||
|
||||
# Print results
|
||||
print("\n" + "="*80)
|
||||
print("Results")
|
||||
print("="*80)
|
||||
for result in all_results:
|
||||
print("\n" + "-"*60)
|
||||
print_result(result)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,216 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d1785adb",
|
||||
"metadata": {
|
||||
"colab_type": "text",
|
||||
"id": "view-in-github"
|
||||
},
|
||||
"source": [
|
||||
"<a href=\"https://colab.research.google.com/github/microsoft/VibeVoice/blob/main/demo/vibevoice_realtime_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "WvIaUJD2y0yU",
|
||||
"metadata": {
|
||||
"id": "WvIaUJD2y0yU"
|
||||
},
|
||||
"source": [
|
||||
"# VibeVoice-Realtime Colab — T4 Quickstart\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e8fTKYGx7DZk",
|
||||
"metadata": {
|
||||
"id": "e8fTKYGx7DZk"
|
||||
},
|
||||
"source": [
|
||||
"## Step 1: Setup Environment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4wxJ6QHM-ZOb",
|
||||
"metadata": {
|
||||
"id": "4wxJ6QHM-ZOb"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check for T4 GPU\n",
|
||||
"import torch\n",
|
||||
"if torch.cuda.is_available() and \"T4\" in torch.cuda.get_device_name(0):\n",
|
||||
" print(\"✅ T4 GPU detected\")\n",
|
||||
"else:\n",
|
||||
" print(\"\"\"\n",
|
||||
" ⚠️ WARNING: T4 GPU not detected\n",
|
||||
"\n",
|
||||
" The recommended runtime for this Colab notebook is \"T4 GPU\".\n",
|
||||
"\n",
|
||||
" To change the runtime type:\n",
|
||||
"\n",
|
||||
" 1. Click on \"Runtime\" in the top navigation menu\n",
|
||||
" 2. Click on \"Change runtime type\"\n",
|
||||
" 3. Select \"T4 GPU\"\n",
|
||||
" 4. Click \"OK\" if a \"Disconnect and delete runtime\" window appears\n",
|
||||
" 5. Click on \"Save\"\n",
|
||||
"\n",
|
||||
" \"\"\")\n",
|
||||
"\n",
|
||||
"# Clone the VibeVoice repository\n",
|
||||
"![ -d /content/VibeVoice ] || git clone --quiet --branch main --depth 1 https://github.com/microsoft/VibeVoice.git /content/VibeVoice\n",
|
||||
"print(\"✅ Cloned VibeVoice repository\")\n",
|
||||
"\n",
|
||||
"# Install project dependencies\n",
|
||||
"!uv pip --quiet install --system -e /content/VibeVoice[streamingtts]\n",
|
||||
"!wget -q https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64 -O cloudflared && chmod +x cloudflared\n",
|
||||
"print(\"✅ Installed dependencies\")\n",
|
||||
"\n",
|
||||
"# Download model\n",
|
||||
"from huggingface_hub import snapshot_download\n",
|
||||
"snapshot_download(\"microsoft/VibeVoice-Realtime-0.5B\", local_dir=\"/content/models/VibeVoice-Realtime-0.5B\")\n",
|
||||
"print(\"✅ Downloaded model: microsoft/VibeVoice-Realtime-0.5B\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "88c727ab",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"[Optional] If the download exceeds 1 minute, it is probably stuck. You can: (1) interrupt the execution, (2) log in to Hugging Face, and (3) try download again."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "dec6b870",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from huggingface_hub import login, snapshot_download\n",
|
||||
"login()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c579654b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"snapshot_download(\"microsoft/VibeVoice-Realtime-0.5B\", local_dir=\"/content/models/VibeVoice-Realtime-0.5B\")\n",
|
||||
"print(\"✅ Downloaded model: microsoft/VibeVoice-Realtime-0.5B\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "dfe30d6f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"[Optional] More experimental voices"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "bb33c9ce",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!bash /content/VibeVoice/demo/download_experimental_voices.sh"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "pgKlV7153Ifi",
|
||||
"metadata": {
|
||||
"id": "pgKlV7153Ifi"
|
||||
},
|
||||
"source": [
|
||||
"## Step 2: Launch VibeVoice-Realtime Demo"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "Yc1N9EHswFxA",
|
||||
"metadata": {
|
||||
"id": "Yc1N9EHswFxA"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import subprocess, re, time, threading\n",
|
||||
"\n",
|
||||
"srv = subprocess.Popen(\n",
|
||||
" \"python /content/VibeVoice/demo/vibevoice_realtime_demo.py --model_path /content/models/VibeVoice-Realtime-0.5B --port 8000\",\n",
|
||||
" shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1, universal_newlines=True,\n",
|
||||
")\n",
|
||||
"cf = subprocess.Popen(\n",
|
||||
" \"./cloudflared tunnel --url http://localhost:8000 --no-autoupdate\",\n",
|
||||
" shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1, universal_newlines=True,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"public_url = None\n",
|
||||
"server_ready = False\n",
|
||||
"url_pattern = re.compile(r\"(https://[a-z0-9-]+\\.trycloudflare\\.com)\")\n",
|
||||
"\n",
|
||||
"def read_srv():\n",
|
||||
" global server_ready\n",
|
||||
" for ln in srv.stdout:\n",
|
||||
" print(ln.strip())\n",
|
||||
" if \"Uvicorn running on\" in ln:\n",
|
||||
" server_ready = True\n",
|
||||
"\n",
|
||||
"def read_cf():\n",
|
||||
" global public_url\n",
|
||||
" for ln in cf.stdout:\n",
|
||||
" m = url_pattern.search(ln)\n",
|
||||
" if m:\n",
|
||||
" public_url = m.group(1)\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
"threading.Thread(target=read_srv, daemon=True).start()\n",
|
||||
"threading.Thread(target=read_cf, daemon=True).start()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"while True:\n",
|
||||
" if server_ready and public_url:\n",
|
||||
" print(f\"✅ Public URL: {public_url}\\n\");\n",
|
||||
" public_url = None\n",
|
||||
" time.sleep(0.25)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"gpuType": "T4",
|
||||
"include_colab_link": true,
|
||||
"machine_shape": "hm",
|
||||
"name": "VibeVoice_Colab.ipynb",
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,17 @@
|
||||
import argparse, os, uvicorn
|
||||
|
||||
def main():
|
||||
p = argparse.ArgumentParser()
|
||||
p.add_argument("--port", type=int, default=3000)
|
||||
p.add_argument("--model_path", type=str, default="microsoft/VibeVoice-Realtime-0.5B")
|
||||
p.add_argument("--device", type=str, default="cuda", choices=["cpu", "cuda", "mpx", "mps"])
|
||||
p.add_argument("--reload", action="store_true", help="Reload the model or not")
|
||||
args = p.parse_args()
|
||||
|
||||
os.environ["MODEL_PATH"] = args.model_path
|
||||
os.environ["MODEL_DEVICE"] = args.device
|
||||
|
||||
uvicorn.run("web.app:app", host="0.0.0.0", port=args.port, reload=args.reload)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
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+518
@@ -0,0 +1,518 @@
|
||||
import datetime
|
||||
import builtins
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import threading
|
||||
import traceback
|
||||
from pathlib import Path
|
||||
from queue import Empty, Queue
|
||||
from typing import Any, Callable, Dict, Iterator, Optional, Tuple, cast
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from transformers.cache_utils import DynamicCache
|
||||
from transformers.modeling_outputs import BaseModelOutputWithPast
|
||||
from fastapi import FastAPI, WebSocket
|
||||
from fastapi.responses import FileResponse
|
||||
from fastapi.staticfiles import StaticFiles
|
||||
from starlette.websockets import WebSocketDisconnect, WebSocketState
|
||||
|
||||
from vibevoice.modular.modeling_vibevoice_streaming_inference import (
|
||||
VibeVoiceStreamingForConditionalGenerationInference,
|
||||
)
|
||||
from vibevoice.processor.vibevoice_streaming_processor import (
|
||||
VibeVoiceStreamingProcessor,
|
||||
)
|
||||
from vibevoice.modular.streamer import AudioStreamer
|
||||
|
||||
import copy
|
||||
|
||||
BASE = Path(__file__).parent
|
||||
SAMPLE_RATE = 24_000
|
||||
|
||||
|
||||
def get_timestamp():
|
||||
timestamp = datetime.datetime.utcnow().replace(
|
||||
tzinfo=datetime.timezone.utc
|
||||
).astimezone(
|
||||
datetime.timezone(datetime.timedelta(hours=8))
|
||||
).strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
|
||||
return timestamp
|
||||
|
||||
class StreamingTTSService:
|
||||
def __init__(
|
||||
self,
|
||||
model_path: str,
|
||||
device: str = "cuda",
|
||||
inference_steps: int = 5,
|
||||
) -> None:
|
||||
# Keep model_path as string for HuggingFace repo IDs (Path() converts / to \ on Windows)
|
||||
self.model_path = model_path
|
||||
self.inference_steps = inference_steps
|
||||
self.sample_rate = SAMPLE_RATE
|
||||
|
||||
self.processor: Optional[VibeVoiceStreamingProcessor] = None
|
||||
self.model: Optional[VibeVoiceStreamingForConditionalGenerationInference] = None
|
||||
self.voice_presets: Dict[str, Path] = {}
|
||||
self.default_voice_key: Optional[str] = None
|
||||
self._voice_cache: Dict[str, Tuple[object, Path, str]] = {}
|
||||
|
||||
if device == "mpx":
|
||||
print("Note: device 'mpx' detected, treating it as 'mps'.")
|
||||
device = "mps"
|
||||
if device == "mps" and not torch.backends.mps.is_available():
|
||||
print("Warning: MPS not available. Falling back to CPU.")
|
||||
device = "cpu"
|
||||
self.device = device
|
||||
self._torch_device = torch.device(device)
|
||||
|
||||
def load(self) -> None:
|
||||
print(f"[startup] Loading processor from {self.model_path}")
|
||||
self.processor = VibeVoiceStreamingProcessor.from_pretrained(self.model_path)
|
||||
|
||||
|
||||
# Decide dtype & attention
|
||||
if self.device == "mps":
|
||||
load_dtype = torch.float32
|
||||
device_map = None
|
||||
attn_impl_primary = "sdpa"
|
||||
elif self.device == "cuda":
|
||||
load_dtype = torch.bfloat16
|
||||
device_map = 'cuda'
|
||||
attn_impl_primary = "flash_attention_2"
|
||||
else:
|
||||
load_dtype = torch.float32
|
||||
device_map = 'cpu'
|
||||
attn_impl_primary = "sdpa"
|
||||
print(f"Using device: {device_map}, torch_dtype: {load_dtype}, attn_implementation: {attn_impl_primary}")
|
||||
# Load model
|
||||
try:
|
||||
self.model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
|
||||
self.model_path,
|
||||
torch_dtype=load_dtype,
|
||||
device_map=device_map,
|
||||
attn_implementation=attn_impl_primary,
|
||||
)
|
||||
|
||||
if self.device == "mps":
|
||||
self.model.to("mps")
|
||||
except Exception as e:
|
||||
if attn_impl_primary == 'flash_attention_2':
|
||||
print("Error loading the model. Trying to use SDPA. However, note that only flash_attention_2 has been fully tested, and using SDPA may result in lower audio quality.")
|
||||
|
||||
self.model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
|
||||
self.model_path,
|
||||
torch_dtype=load_dtype,
|
||||
device_map=self.device,
|
||||
attn_implementation='sdpa',
|
||||
)
|
||||
print("Load model with SDPA successfully ")
|
||||
else:
|
||||
raise e
|
||||
|
||||
self.model.eval()
|
||||
|
||||
self.model.model.noise_scheduler = self.model.model.noise_scheduler.from_config(
|
||||
self.model.model.noise_scheduler.config,
|
||||
algorithm_type="sde-dpmsolver++",
|
||||
beta_schedule="squaredcos_cap_v2",
|
||||
)
|
||||
self.model.set_ddpm_inference_steps(num_steps=self.inference_steps)
|
||||
|
||||
self.voice_presets = self._load_voice_presets()
|
||||
preset_name = os.environ.get("VOICE_PRESET")
|
||||
self.default_voice_key = self._determine_voice_key(preset_name)
|
||||
self._ensure_voice_cached(self.default_voice_key)
|
||||
|
||||
def _load_voice_presets(self) -> Dict[str, Path]:
|
||||
voices_dir = BASE.parent / "voices" / "streaming_model"
|
||||
if not voices_dir.exists():
|
||||
raise RuntimeError(f"Voices directory not found: {voices_dir}")
|
||||
|
||||
presets: Dict[str, Path] = {}
|
||||
for pt_path in voices_dir.rglob("*.pt"):
|
||||
presets[pt_path.stem] = pt_path
|
||||
|
||||
if not presets:
|
||||
raise RuntimeError(f"No voice preset (.pt) files found in {voices_dir}")
|
||||
|
||||
print(f"[startup] Found {len(presets)} voice presets")
|
||||
return dict(sorted(presets.items()))
|
||||
|
||||
def _determine_voice_key(self, name: Optional[str]) -> str:
|
||||
if name and name in self.voice_presets:
|
||||
return name
|
||||
|
||||
default_key = "en-Carter_man"
|
||||
if default_key in self.voice_presets:
|
||||
return default_key
|
||||
|
||||
first_key = next(iter(self.voice_presets))
|
||||
print(f"[startup] Using fallback voice preset: {first_key}")
|
||||
return first_key
|
||||
|
||||
def _ensure_voice_cached(self, key: str) -> Tuple[object, Path, str]:
|
||||
if key not in self.voice_presets:
|
||||
raise RuntimeError(f"Voice preset {key!r} not found")
|
||||
|
||||
if key not in self._voice_cache:
|
||||
preset_path = self.voice_presets[key]
|
||||
print(f"[startup] Loading voice preset {key} from {preset_path}")
|
||||
print(f"[startup] Loading prefilled prompt from {preset_path}")
|
||||
with torch.serialization.safe_globals([BaseModelOutputWithPast, DynamicCache]):
|
||||
prefilled_outputs = torch.load(
|
||||
preset_path,
|
||||
map_location=self._torch_device,
|
||||
weights_only=True,
|
||||
)
|
||||
self._voice_cache[key] = prefilled_outputs
|
||||
|
||||
return self._voice_cache[key]
|
||||
|
||||
def _get_voice_resources(self, requested_key: Optional[str]) -> Tuple[str, object, Path, str]:
|
||||
key = requested_key if requested_key and requested_key in self.voice_presets else self.default_voice_key
|
||||
if key is None:
|
||||
key = next(iter(self.voice_presets))
|
||||
self.default_voice_key = key
|
||||
|
||||
prefilled_outputs = self._ensure_voice_cached(key)
|
||||
return key, prefilled_outputs
|
||||
|
||||
def _prepare_inputs(self, text: str, prefilled_outputs: object):
|
||||
if not self.processor or not self.model:
|
||||
raise RuntimeError("StreamingTTSService not initialized")
|
||||
|
||||
processor_kwargs = {
|
||||
"text": text.strip(),
|
||||
"cached_prompt": prefilled_outputs,
|
||||
"padding": True,
|
||||
"return_tensors": "pt",
|
||||
"return_attention_mask": True,
|
||||
}
|
||||
|
||||
processed = self.processor.process_input_with_cached_prompt(**processor_kwargs)
|
||||
|
||||
prepared = {
|
||||
key: value.to(self._torch_device) if hasattr(value, "to") else value
|
||||
for key, value in processed.items()
|
||||
}
|
||||
return prepared
|
||||
|
||||
def _run_generation(
|
||||
self,
|
||||
inputs,
|
||||
audio_streamer: AudioStreamer,
|
||||
errors,
|
||||
cfg_scale: float,
|
||||
do_sample: bool,
|
||||
temperature: float,
|
||||
top_p: float,
|
||||
refresh_negative: bool,
|
||||
prefilled_outputs,
|
||||
stop_event: threading.Event,
|
||||
) -> None:
|
||||
try:
|
||||
self.model.generate(
|
||||
**inputs,
|
||||
max_new_tokens=None,
|
||||
cfg_scale=cfg_scale,
|
||||
tokenizer=self.processor.tokenizer,
|
||||
generation_config={
|
||||
"do_sample": do_sample,
|
||||
"temperature": temperature if do_sample else 1.0,
|
||||
"top_p": top_p if do_sample else 1.0,
|
||||
},
|
||||
audio_streamer=audio_streamer,
|
||||
stop_check_fn=stop_event.is_set,
|
||||
verbose=False,
|
||||
refresh_negative=refresh_negative,
|
||||
all_prefilled_outputs=copy.deepcopy(prefilled_outputs),
|
||||
)
|
||||
except Exception as exc: # pragma: no cover - diagnostic logging
|
||||
errors.append(exc)
|
||||
traceback.print_exc()
|
||||
audio_streamer.end()
|
||||
|
||||
def stream(
|
||||
self,
|
||||
text: str,
|
||||
cfg_scale: float = 1.5,
|
||||
do_sample: bool = False,
|
||||
temperature: float = 0.9,
|
||||
top_p: float = 0.9,
|
||||
refresh_negative: bool = True,
|
||||
inference_steps: Optional[int] = None,
|
||||
voice_key: Optional[str] = None,
|
||||
log_callback: Optional[Callable[[str, Dict[str, Any]], None]] = None,
|
||||
stop_event: Optional[threading.Event] = None,
|
||||
) -> Iterator[np.ndarray]:
|
||||
if not text.strip():
|
||||
return
|
||||
text = text.replace("’", "'")
|
||||
selected_voice, prefilled_outputs = self._get_voice_resources(voice_key)
|
||||
|
||||
def emit(event: str, **payload: Any) -> None:
|
||||
if log_callback:
|
||||
try:
|
||||
log_callback(event, **payload)
|
||||
except Exception as exc:
|
||||
print(f"[log_callback] Error while emitting {event}: {exc}")
|
||||
|
||||
steps_to_use = self.inference_steps
|
||||
if inference_steps is not None:
|
||||
try:
|
||||
parsed_steps = int(inference_steps)
|
||||
if parsed_steps > 0:
|
||||
steps_to_use = parsed_steps
|
||||
except (TypeError, ValueError):
|
||||
pass
|
||||
if self.model:
|
||||
self.model.set_ddpm_inference_steps(num_steps=steps_to_use)
|
||||
self.inference_steps = steps_to_use
|
||||
|
||||
inputs = self._prepare_inputs(text, prefilled_outputs)
|
||||
audio_streamer = AudioStreamer(batch_size=1, stop_signal=None, timeout=None)
|
||||
errors: list = []
|
||||
stop_signal = stop_event or threading.Event()
|
||||
|
||||
thread = threading.Thread(
|
||||
target=self._run_generation,
|
||||
kwargs={
|
||||
"inputs": inputs,
|
||||
"audio_streamer": audio_streamer,
|
||||
"errors": errors,
|
||||
"cfg_scale": cfg_scale,
|
||||
"do_sample": do_sample,
|
||||
"temperature": temperature,
|
||||
"top_p": top_p,
|
||||
"refresh_negative": refresh_negative,
|
||||
"prefilled_outputs": prefilled_outputs,
|
||||
"stop_event": stop_signal,
|
||||
},
|
||||
daemon=True,
|
||||
)
|
||||
thread.start()
|
||||
|
||||
generated_samples = 0
|
||||
|
||||
try:
|
||||
stream = audio_streamer.get_stream(0)
|
||||
for audio_chunk in stream:
|
||||
if torch.is_tensor(audio_chunk):
|
||||
audio_chunk = audio_chunk.detach().cpu().to(torch.float32).numpy()
|
||||
else:
|
||||
audio_chunk = np.asarray(audio_chunk, dtype=np.float32)
|
||||
|
||||
if audio_chunk.ndim > 1:
|
||||
audio_chunk = audio_chunk.reshape(-1)
|
||||
|
||||
peak = np.max(np.abs(audio_chunk)) if audio_chunk.size else 0.0
|
||||
if peak > 1.0:
|
||||
audio_chunk = audio_chunk / peak
|
||||
|
||||
generated_samples += int(audio_chunk.size)
|
||||
emit(
|
||||
"model_progress",
|
||||
generated_sec=generated_samples / self.sample_rate,
|
||||
chunk_sec=audio_chunk.size / self.sample_rate,
|
||||
)
|
||||
|
||||
chunk_to_yield = audio_chunk.astype(np.float32, copy=False)
|
||||
|
||||
yield chunk_to_yield
|
||||
finally:
|
||||
stop_signal.set()
|
||||
audio_streamer.end()
|
||||
thread.join()
|
||||
if errors:
|
||||
emit("generation_error", message=str(errors[0]))
|
||||
raise errors[0]
|
||||
|
||||
def chunk_to_pcm16(self, chunk: np.ndarray) -> bytes:
|
||||
chunk = np.clip(chunk, -1.0, 1.0)
|
||||
pcm = (chunk * 32767.0).astype(np.int16)
|
||||
return pcm.tobytes()
|
||||
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
|
||||
@app.on_event("startup")
|
||||
async def _startup() -> None:
|
||||
model_path = os.environ.get("MODEL_PATH")
|
||||
if not model_path:
|
||||
raise RuntimeError("MODEL_PATH not set in environment")
|
||||
|
||||
device = os.environ.get("MODEL_DEVICE", "cuda")
|
||||
|
||||
service = StreamingTTSService(
|
||||
model_path=model_path,
|
||||
device=device
|
||||
)
|
||||
service.load()
|
||||
|
||||
app.state.tts_service = service
|
||||
app.state.model_path = model_path
|
||||
app.state.device = device
|
||||
app.state.websocket_lock = asyncio.Lock()
|
||||
print("[startup] Model ready.")
|
||||
|
||||
|
||||
def streaming_tts(text: str, **kwargs) -> Iterator[np.ndarray]:
|
||||
service: StreamingTTSService = app.state.tts_service
|
||||
yield from service.stream(text, **kwargs)
|
||||
|
||||
@app.websocket("/stream")
|
||||
async def websocket_stream(ws: WebSocket) -> None:
|
||||
await ws.accept()
|
||||
text = ws.query_params.get("text", "")
|
||||
print(f"Client connected, text={text!r}")
|
||||
cfg_param = ws.query_params.get("cfg")
|
||||
steps_param = ws.query_params.get("steps")
|
||||
voice_param = ws.query_params.get("voice")
|
||||
|
||||
try:
|
||||
cfg_scale = float(cfg_param) if cfg_param is not None else 1.5
|
||||
except ValueError:
|
||||
cfg_scale = 1.5
|
||||
if cfg_scale <= 0:
|
||||
cfg_scale = 1.5
|
||||
try:
|
||||
inference_steps = int(steps_param) if steps_param is not None else None
|
||||
if inference_steps is not None and inference_steps <= 0:
|
||||
inference_steps = None
|
||||
except ValueError:
|
||||
inference_steps = None
|
||||
|
||||
service: StreamingTTSService = app.state.tts_service
|
||||
lock: asyncio.Lock = app.state.websocket_lock
|
||||
|
||||
if lock.locked():
|
||||
busy_message = {
|
||||
"type": "log",
|
||||
"event": "backend_busy",
|
||||
"data": {"message": "Please wait for the other requests to complete."},
|
||||
"timestamp": get_timestamp(),
|
||||
}
|
||||
print("Please wait for the other requests to complete.")
|
||||
try:
|
||||
await ws.send_text(json.dumps(busy_message))
|
||||
except Exception:
|
||||
pass
|
||||
await ws.close(code=1013, reason="Service busy")
|
||||
return
|
||||
|
||||
acquired = False
|
||||
try:
|
||||
await lock.acquire()
|
||||
acquired = True
|
||||
|
||||
log_queue: "Queue[Dict[str, Any]]" = Queue()
|
||||
|
||||
def enqueue_log(event: str, **data: Any) -> None:
|
||||
log_queue.put({"event": event, "data": data})
|
||||
|
||||
async def flush_logs() -> None:
|
||||
while True:
|
||||
try:
|
||||
entry = log_queue.get_nowait()
|
||||
except Empty:
|
||||
break
|
||||
message = {
|
||||
"type": "log",
|
||||
"event": entry.get("event"),
|
||||
"data": entry.get("data", {}),
|
||||
"timestamp": get_timestamp(),
|
||||
}
|
||||
try:
|
||||
await ws.send_text(json.dumps(message))
|
||||
except Exception:
|
||||
break
|
||||
|
||||
enqueue_log(
|
||||
"backend_request_received",
|
||||
text_length=len(text or ""),
|
||||
cfg_scale=cfg_scale,
|
||||
inference_steps=inference_steps,
|
||||
voice=voice_param,
|
||||
)
|
||||
|
||||
stop_signal = threading.Event()
|
||||
|
||||
iterator = streaming_tts(
|
||||
text,
|
||||
cfg_scale=cfg_scale,
|
||||
inference_steps=inference_steps,
|
||||
voice_key=voice_param,
|
||||
log_callback=enqueue_log,
|
||||
stop_event=stop_signal,
|
||||
)
|
||||
sentinel = object()
|
||||
first_ws_send_logged = False
|
||||
|
||||
await flush_logs()
|
||||
|
||||
try:
|
||||
while ws.client_state == WebSocketState.CONNECTED:
|
||||
await flush_logs()
|
||||
chunk = await asyncio.to_thread(next, iterator, sentinel)
|
||||
if chunk is sentinel:
|
||||
break
|
||||
chunk = cast(np.ndarray, chunk)
|
||||
payload = service.chunk_to_pcm16(chunk)
|
||||
await ws.send_bytes(payload)
|
||||
if not first_ws_send_logged:
|
||||
first_ws_send_logged = True
|
||||
enqueue_log("backend_first_chunk_sent")
|
||||
await flush_logs()
|
||||
except WebSocketDisconnect:
|
||||
print("Client disconnected (WebSocketDisconnect)")
|
||||
enqueue_log("client_disconnected")
|
||||
stop_signal.set()
|
||||
except Exception as e:
|
||||
print(f"Error in websocket stream: {e}")
|
||||
traceback.print_exc()
|
||||
enqueue_log("backend_error", message=str(e))
|
||||
stop_signal.set()
|
||||
finally:
|
||||
stop_signal.set()
|
||||
enqueue_log("backend_stream_complete")
|
||||
await flush_logs()
|
||||
try:
|
||||
iterator_close = getattr(iterator, "close", None)
|
||||
if callable(iterator_close):
|
||||
iterator_close()
|
||||
except Exception:
|
||||
pass
|
||||
# clear the log queue
|
||||
while not log_queue.empty():
|
||||
try:
|
||||
log_queue.get_nowait()
|
||||
except Empty:
|
||||
break
|
||||
try:
|
||||
if ws.client_state == WebSocketState.CONNECTED:
|
||||
await ws.close()
|
||||
except Exception as e:
|
||||
print(f"Error closing websocket: {e}")
|
||||
print("WS handler exit")
|
||||
finally:
|
||||
if acquired:
|
||||
lock.release()
|
||||
|
||||
|
||||
@app.get("/")
|
||||
def index():
|
||||
return FileResponse(BASE / "index.html")
|
||||
|
||||
|
||||
@app.get("/config")
|
||||
def get_config():
|
||||
service: StreamingTTSService = app.state.tts_service
|
||||
voices = sorted(service.voice_presets.keys())
|
||||
return {
|
||||
"voices": voices,
|
||||
"default_voice": service.default_voice_key,
|
||||
}
|
||||
|
||||
+1017
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,196 @@
|
||||
# VibeVoice Gradio Demo Setup Guide
|
||||
|
||||
End-to-end instructions to deploy the VibeVoice ASR server and launch the Gradio web demo.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- CUDA-capable GPU(s)
|
||||
- Docker with GPU support (`nvidia-docker`)
|
||||
- VibeVoice repository cloned locally
|
||||
|
||||
```bash
|
||||
git clone https://github.com/microsoft/VibeVoice.git
|
||||
cd VibeVoice
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Step 1 — Start the ASR Server
|
||||
|
||||
Launch a Docker container running the vLLM ASR server. The launcher script handles everything automatically (system deps, pip install, model download, tokenizer generation, server start).
|
||||
|
||||
### Single GPU (default)
|
||||
|
||||
```bash
|
||||
docker run -d --gpus '"device=0"' --name vibevoice-asr-demo \
|
||||
--ipc=host \
|
||||
-p 6001:6001 \
|
||||
-e VIBEVOICE_FFMPEG_MAX_CONCURRENCY=64 \
|
||||
-e PYTORCH_ALLOC_CONF=expandable_segments:True \
|
||||
-v $(pwd):/app \
|
||||
-w /app \
|
||||
--entrypoint bash \
|
||||
vllm/vllm-openai:v0.14.1 \
|
||||
-c "python3 /app/vllm_plugin/scripts/start_server.py --port 6001"
|
||||
```
|
||||
|
||||
### Multi-GPU with Data Parallel (load balancing)
|
||||
|
||||
Run 4 independent replicas, one per GPU. vLLM distributes requests automatically:
|
||||
|
||||
```bash
|
||||
docker run -d --gpus '"device=0,1,2,3"' --name vibevoice-asr-demo \
|
||||
--ipc=host \
|
||||
-p 6001:6001 \
|
||||
-e VIBEVOICE_FFMPEG_MAX_CONCURRENCY=64 \
|
||||
-e PYTORCH_ALLOC_CONF=expandable_segments:True \
|
||||
-v $(pwd):/app \
|
||||
-w /app \
|
||||
--entrypoint bash \
|
||||
vllm/vllm-openai:v0.14.1 \
|
||||
-c "python3 /app/vllm_plugin/scripts/start_server.py --port 6001 --dp 4"
|
||||
```
|
||||
|
||||
> **Tip**: Use `--dp N` for N-way data parallel (throughput scaling). Use `--tp N` for tensor parallel (large models). See `docs/vibevoice-vllm-asr.md` for details.
|
||||
|
||||
### Check Logs
|
||||
|
||||
```bash
|
||||
docker logs -f vibevoice-asr-demo
|
||||
```
|
||||
|
||||
Wait until you see `Application startup complete.` — this means the server is ready.
|
||||
|
||||
---
|
||||
|
||||
## Step 2 — Verify the Server
|
||||
|
||||
```bash
|
||||
# Check the model is loaded
|
||||
curl http://localhost:6001/v1/models
|
||||
```
|
||||
|
||||
Expected output:
|
||||
```json
|
||||
{
|
||||
"data": [{ "id": "vibevoice", ... }]
|
||||
}
|
||||
```
|
||||
|
||||
### Quick Test with Audio File
|
||||
|
||||
```bash
|
||||
docker exec -it vibevoice-asr-demo \
|
||||
python3 /app/vllm_plugin/tests/test_api.py /app/en-Alice_woman.wav \
|
||||
--url http://localhost:6001
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Step 3 — Launch the Gradio Demo
|
||||
|
||||
### Install tmux inside the container (to keep it running)
|
||||
|
||||
```bash
|
||||
docker exec vibevoice-asr-demo apt-get install -y tmux
|
||||
```
|
||||
|
||||
### Start Gradio in tmux
|
||||
|
||||
```bash
|
||||
docker exec vibevoice-asr-demo bash -c \
|
||||
"PYTHONUNBUFFERED=1 tmux new-session -d -s gradio \
|
||||
'PYTHONUNBUFFERED=1 python3 /app/vllm_plugin/scripts/gradio_asr_demo_api_video.py \
|
||||
--api_url http://localhost:6001 --share \
|
||||
2>&1 | tee /tmp/gradio.log'"
|
||||
```
|
||||
|
||||
### Get the Share Link
|
||||
|
||||
Wait ~20 seconds, then:
|
||||
|
||||
```bash
|
||||
docker exec vibevoice-asr-demo cat /tmp/gradio.log
|
||||
```
|
||||
|
||||
You should see:
|
||||
```
|
||||
✅ Connected to API: http://localhost:6001 | Model: vibevoice
|
||||
🚀 Starting VibeVoice ASR Demo
|
||||
* Running on local URL: http://0.0.0.0:7860
|
||||
* Running on public URL: https://xxxxxx.gradio.live
|
||||
```
|
||||
|
||||
The `gradio.live` link is publicly accessible (valid for 1 week).
|
||||
|
||||
### Gradio Options
|
||||
|
||||
| Flag | Description | Default |
|
||||
|------|-------------|---------|
|
||||
| `--api_url URL` | vLLM server URL | `http://localhost:8000` |
|
||||
| `--share` | Create a public Gradio link | off |
|
||||
| `--port PORT` | Local Gradio port | `7860` |
|
||||
| `--cloudflared` | Use Cloudflare tunnel instead of Gradio share | off |
|
||||
| `--max_video_size MB` | Max upload video size | `50` |
|
||||
|
||||
---
|
||||
|
||||
## Managing the Service
|
||||
|
||||
### Stop Gradio (keep ASR server running)
|
||||
|
||||
```bash
|
||||
docker exec vibevoice-asr-demo tmux kill-session -t gradio
|
||||
```
|
||||
|
||||
### Restart Gradio
|
||||
|
||||
Re-run the tmux command from Step 3.
|
||||
|
||||
### Stop Everything
|
||||
|
||||
```bash
|
||||
docker stop vibevoice-asr-demo
|
||||
docker rm vibevoice-asr-demo
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Example: Full Setup on GPU 0 with Port 6001
|
||||
|
||||
```bash
|
||||
# 1. Start server
|
||||
docker run -d --gpus '"device=0"' --name vibevoice-asr-demo \
|
||||
--ipc=host -p 6001:6001 \
|
||||
-e VIBEVOICE_FFMPEG_MAX_CONCURRENCY=64 \
|
||||
-e PYTORCH_ALLOC_CONF=expandable_segments:True \
|
||||
-v $(pwd):/app -w /app \
|
||||
--entrypoint bash \
|
||||
vllm/vllm-openai:v0.14.1 \
|
||||
-c "python3 /app/vllm_plugin/scripts/start_server.py --port 6001"
|
||||
|
||||
# 2. Wait for startup (~2 min), then verify
|
||||
docker logs -f vibevoice-asr-demo # wait for "Application startup complete."
|
||||
curl http://localhost:6001/v1/models
|
||||
|
||||
# 3. Install tmux and launch Gradio
|
||||
docker exec vibevoice-asr-demo apt-get install -y tmux
|
||||
docker exec vibevoice-asr-demo bash -c \
|
||||
"PYTHONUNBUFFERED=1 tmux new-session -d -s gradio \
|
||||
'PYTHONUNBUFFERED=1 python3 /app/vllm_plugin/scripts/gradio_asr_demo_api_video.py \
|
||||
--api_url http://localhost:6001 --share \
|
||||
2>&1 | tee /tmp/gradio.log'"
|
||||
|
||||
# 4. Get the public link
|
||||
sleep 20 && docker exec vibevoice-asr-demo cat /tmp/gradio.log
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
| Issue | Fix |
|
||||
|-------|-----|
|
||||
| `CUDA out of memory` | Use a different GPU (`device=X`) or reduce `--gpu-memory-utilization 0.7` in `start_server.py` |
|
||||
| Gradio log is empty | Wait longer (~30s); Gradio buffers output. Use `PYTHONUNBUFFERED=1` as shown above |
|
||||
| `Port already in use` | Pick a different port or stop the existing container: `docker stop <name> && docker rm <name>` |
|
||||
| Share link shows "No interface" | Gradio is still loading. Wait for `Application startup complete` in the log |
|
||||
| `tmux: command not found` | Run `docker exec <container> apt-get install -y tmux` first |
|
||||
@@ -0,0 +1,134 @@
|
||||
# VibeVoice-ASR
|
||||
|
||||
[](https://huggingface.co/microsoft/VibeVoice-ASR)
|
||||
[](https://aka.ms/vibevoice-asr)
|
||||
|
||||
**VibeVoice-ASR** is a unified speech-to-text model designed to handle **60-minute long-form audio** in a single pass, generating structured transcriptions containing **Who (Speaker), When (Timestamps), and What (Content)**, with support for **Customized Hotwords** and over **50 languages**.
|
||||
|
||||
**Model:** [VibeVoice-ASR-7B](https://huggingface.co/microsoft/VibeVoice-ASR)<br>
|
||||
**Demo:** [VibeVoice-ASR-Demo](https://aka.ms/vibevoice-asr)<br>
|
||||
**Report:** [VibeVoice-ASR-Report](https://arxiv.org/pdf/2601.18184)<br>
|
||||
**Finetuning:** [finetune-guide](../finetuning-asr/README.md)<br>
|
||||
**vLLM:** [vLLM-asr](./vibevoice-vllm-asr.md)<br>
|
||||
**Transformers:** [VibeVoice-ASR-HF](https://huggingface.co/microsoft/VibeVoice-ASR-HF)<br>
|
||||
|
||||
|
||||
## 🔥 Key Features
|
||||
|
||||
- **🕒 60-minute Single-Pass Processing**:
|
||||
Unlike conventional ASR models that slice audio into short chunks (often losing global context), VibeVoice ASR accepts up to **60 minutes** of continuous audio input within 64K token length. This ensures consistent speaker tracking and semantic coherence across the entire hour.
|
||||
|
||||
- **👤 Customized Hotwords**:
|
||||
Users can provide customized hotwords (e.g., specific names, technical terms, or background info) to guide the recognition process, significantly improving accuracy on domain-specific content.
|
||||
|
||||
- **📝 Rich Transcription (Who, When, What)**:
|
||||
The model jointly performs ASR, diarization, and timestamping, producing a structured output that indicates *who* said *what* and *when*.
|
||||
|
||||
- **🌍 Multilingual & Code-Switching Support**:
|
||||
It supports over 50 languages, requires no explicit language setting, and natively handles code-switching within and across utterances. See the [Language distribution](#language-distribution).
|
||||
|
||||
|
||||
## 🏗️ Model Architecture
|
||||
|
||||
<p align="center">
|
||||
<img src="../Figures/VibeVoice_ASR_archi.png" alt="VibeVoice ASR Architecture" width="80%">
|
||||
</p>
|
||||
|
||||
# Demo
|
||||
|
||||
<div align="center" id="vibevoice-asr">
|
||||
|
||||
https://github.com/user-attachments/assets/acde5602-dc17-4314-9e3b-c630bc84aefa
|
||||
|
||||
</div>
|
||||
|
||||
## Evaluation
|
||||
<p align="center">
|
||||
<img src="../Figures/DER.jpg" alt="DER" width="50%"><br>
|
||||
<img src="../Figures/cpWER.jpg" alt="cpWER" width="50%"><br>
|
||||
<img src="../Figures/tcpWER.jpg" alt="tcpWER" width="50%">
|
||||
</p>
|
||||
|
||||
|
||||
|
||||
## Installation
|
||||
We recommend using NVIDIA Deep Learning Container to manage the CUDA environment.
|
||||
|
||||
1. Launch docker
|
||||
```bash
|
||||
# NVIDIA PyTorch Container 24.07 ~ 25.12 verified.
|
||||
# Previous versions are also compatible.
|
||||
sudo docker run --privileged --net=host --ipc=host --ulimit memlock=-1:-1 --ulimit stack=-1:-1 --gpus all --rm -it nvcr.io/nvidia/pytorch:25.12-py3
|
||||
|
||||
## If flash attention is not included in your docker environment, you need to install it manually
|
||||
## Refer to https://github.com/Dao-AILab/flash-attention for installation instructions
|
||||
# pip install flash-attn --no-build-isolation
|
||||
```
|
||||
|
||||
2. Install from github
|
||||
```bash
|
||||
git clone https://github.com/microsoft/VibeVoice.git
|
||||
cd VibeVoice
|
||||
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
## Usages
|
||||
|
||||
### Usage 1: Launch Gradio demo
|
||||
```bash
|
||||
apt update && apt install ffmpeg -y # for demo
|
||||
|
||||
python demo/vibevoice_asr_gradio_demo.py --model_path microsoft/VibeVoice-ASR --share
|
||||
```
|
||||
|
||||
### Usage 2: Inference from files directly
|
||||
```bash
|
||||
python demo/vibevoice_asr_inference_from_file.py --model_path microsoft/VibeVoice-ASR --audio_files [add an audio path here]
|
||||
```
|
||||
|
||||
|
||||
## Finetuning
|
||||
LoRA (Low-Rank Adaptation) fine-tuning is supported. See [Finetuning](../finetuning-asr/README.md) for detailed guide.
|
||||
|
||||
|
||||
|
||||
## Results
|
||||
|
||||
### Multilingual
|
||||
| Dataset | Language | DER | cpWER | tcpWER | WER |
|
||||
|----------------|-----------|------|-------|--------|------|
|
||||
| MLC-Challenge | English | 4.28 | 11.48 | 13.02 | 7.99 |
|
||||
| MLC-Challenge | French | 3.80 | 18.80 | 19.64 | 15.21 |
|
||||
| MLC-Challenge | German | 1.04 | 17.10 | 17.26 | 16.30 |
|
||||
| MLC-Challenge | Italian | 2.08 | 15.76 | 15.91 | 13.91 |
|
||||
| MLC-Challenge | Japanese | 0.82 | 15.33 | 15.41 | 14.69 |
|
||||
| MLC-Challenge | Korean | 4.52 | 15.35 | 16.07 | 9.65 |
|
||||
| MLC-Challenge | Portuguese| 7.98 | 29.91 | 31.65 | 21.54 |
|
||||
| MLC-Challenge | Russian | 0.90 | 12.94 | 12.98 | 12.40 |
|
||||
| MLC-Challenge | Spanish | 2.67 | 10.51 | 11.71 | 8.04 |
|
||||
| MLC-Challenge | Thai | 4.09 | 14.91 | 15.57 | 13.61 |
|
||||
| MLC-Challenge | Vietnamese| 0.16 | 14.57 | 14.57 | 14.43 |
|
||||
|
||||
---
|
||||
|
||||
| Dataset | Language | DER | cpWER | tcpWER | WER |
|
||||
|----------------|-----------|------|-------|--------|------|
|
||||
| AISHELL-4 | Chinese | 6.77 | 24.99 | 25.35 | 21.40 |
|
||||
| AMI-IHM | English | 11.92| 20.41 | 20.82 | 18.81 |
|
||||
| AMI-SDM | English | 13.43| 28.82 | 29.80 | 24.65 |
|
||||
| AliMeeting | Chinese | 10.92| 29.33 | 29.51 | 27.40 |
|
||||
| MLC-Challenge | Average | 3.42 | 14.81 | 15.66 | 12.07|
|
||||
|
||||
|
||||
## Language Distribution
|
||||
<p align="center">
|
||||
<img src="../Figures/language_distribution_horizontal.png" alt="Language Distribution" width="80%">
|
||||
</p>
|
||||
|
||||
## 📄 License
|
||||
|
||||
This project is licensed under the [MIT License](../LICENSE).
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,142 @@
|
||||
<div align="center">
|
||||
|
||||
## 🎙️ VibeVoice-Realtime: Real-time Long‑Form Text‑to‑Speech with Streaming Input
|
||||
[](https://huggingface.co/microsoft/VibeVoice-Realtime-0.5B)
|
||||
[](https://colab.research.google.com/github/microsoft/VibeVoice/blob/main/demo/vibevoice_realtime_colab.ipynb)
|
||||
</div>
|
||||
|
||||
VibeVoice-Realtime is a **lightweight real‑time** text-to-speech model supporting **streaming text input** and **robust long-form speech generation**. It can be used to build real-time TTS services, narrate live data streams, and let different LLMs start speaking from their very first tokens (plug in your preferred model) long before a full answer is generated. It produces initial audible speech in **~200 milliseconds** (hardware dependent).
|
||||
|
||||
|
||||
**Model:** [VibeVoice Realtime 0.5B (on Hugging Face)](https://huggingface.co/microsoft/VibeVoice-Realtime-0.5B)<br>
|
||||
**Colab:** [VibeVoice Realtime Colab (Jupyter Notebook)](https://colab.research.google.com/github/microsoft/VibeVoice/blob/main/demo/vibevoice_realtime_colab.ipynb)<br>
|
||||
|
||||
|
||||
> Note (multilingual exploration): Although the model is primarily built for English, we found that it still exhibits a certain level of multilingual capability—and even performs reasonably well in some languages. We provide nine additional languages (German, French, Italian, Japanese, Korean, Dutch, Polish, Portuguese, and Spanish) for users to explore. These multilingual behaviors have not been extensively tested; use with caution and share observations.
|
||||
|
||||
The model uses an interleaved, windowed design: it incrementally encodes incoming text chunks while, in parallel, continuing diffusion-based acoustic latent generation from prior context. Unlike the full multi-speaker long-form variants, this streaming model removes the semantic tokenizer and relies solely on an efficient acoustic tokenizer operating at an ultra-low frame rate (7.5 Hz).
|
||||
|
||||
<div align="center">
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: dark)" srcset="../Figures/VibeVoice_logo_white.png">
|
||||
<img src="../Figures/VibeVoice_Realtime.png" alt="VibeVoice Realtime Overview" width="800" />
|
||||
</picture>
|
||||
<br>
|
||||
<em>Overview of VibeVoice Realtime Model.</em>
|
||||
</div>
|
||||
|
||||
Key features:
|
||||
- Parameter size: 0.5B (deployment-friendly)
|
||||
- Real-time TTS (~200 milliseconds first audible latency)
|
||||
- Streaming text input
|
||||
- Robust long-form speech generation
|
||||
- 8k context window( ~10 minutes audio generation)
|
||||
|
||||
This real-time variant supports only a single speaker. For multi‑speaker conversational speech generation, please use other VibeVoice models (long‑form multi‑speaker variants). The model is currently intended for English speech only; other languages may produce unpredictable results.
|
||||
|
||||
To mitigate deepfake risks and ensure low latency for the first speech chunk, voice prompts are provided in an embedded format. For users requiring voice customization, please reach out to our team. We will also be expanding the range of available speakers.
|
||||
|
||||
|
||||
### 📋 TODO
|
||||
|
||||
- [√] Add more voices (expand available speakers/voice timbres)
|
||||
- [ ] Implement streaming text input function to feed new tokens while audio is still being generated
|
||||
- [ ] Merge models into official HuggingFace's `transformers` repository
|
||||
|
||||
|
||||
### 🎵 Demo Examples
|
||||
|
||||
<div align="center" id="generated-example-audio-vibevoice-realtime">
|
||||
|
||||
https://github.com/user-attachments/assets/9aa8ab3c-681d-4a02-b9ea-3f54ffd180b2
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
## Results
|
||||
|
||||
The model achieves satisfactory performance on short-sentence benchmarks, while the model is more focused on long‑form speech generation.
|
||||
|
||||
### Zero-shot TTS performance on LibriSpeech test-clean set
|
||||
|
||||
| Model | WER (%) ↓ | Speaker Similarity ↑ |
|
||||
|:--------------------|:---------:|:----------------:|
|
||||
| VALL-E 2 | 2.40 | 0.643 |
|
||||
| Voicebox | 1.90 | 0.662 |
|
||||
| MELLE | 2.10 | 0.625 |
|
||||
| **VibeVoice-Realtime-0.5B** | 2.00 | 0.695 |
|
||||
|
||||
### Zero-shot TTS performance on SEED test-en set
|
||||
|
||||
| Model | WER (%) ↓ | Speaker Similarity ↑ |
|
||||
|:--------------------|:---------:|:----------------:|
|
||||
| MaskGCT | 2.62 | 0.714 |
|
||||
| Seed-TTS | 2.25 | 0.762 |
|
||||
| FireRedTTS | 3.82 | 0.460 |
|
||||
| SparkTTS | 1.98 | 0.584 |
|
||||
| CosyVoice2 | 2.57 | 0.652 |
|
||||
| **VibeVoice-Realtime-0.5B** | 2.05 | 0.633 |
|
||||
|
||||
|
||||
## Installation
|
||||
We recommend using NVIDIA Deep Learning Container to manage the CUDA environment.
|
||||
|
||||
1. Launch docker
|
||||
```bash
|
||||
# NVIDIA PyTorch Container 24.07 / 24.10 / 24.12 verified.
|
||||
# Later versions are also compatible.
|
||||
sudo docker run --privileged --net=host --ipc=host --ulimit memlock=-1:-1 --ulimit stack=-1:-1 --gpus all --rm -it nvcr.io/nvidia/pytorch:24.07-py3
|
||||
|
||||
## If flash attention is not included in your docker environment, you need to install it manually
|
||||
## Refer to https://github.com/Dao-AILab/flash-attention for installation instructions
|
||||
# pip install flash-attn --no-build-isolation
|
||||
```
|
||||
|
||||
2. Install from github
|
||||
```bash
|
||||
git clone https://github.com/microsoft/VibeVoice.git
|
||||
cd VibeVoice/
|
||||
|
||||
pip install -e .[streamingtts]
|
||||
```
|
||||
|
||||
|
||||
## Usages
|
||||
|
||||
|
||||
### Usage 1: Launch real-time websocket demo
|
||||
Note: NVIDIA T4 / Mac M4 Pro achieve real-time performance in our tests; other devices with weaker inference capability may require further testing and speed optimizations.
|
||||
|
||||
Due to network latency, the time when audio playback is heard may exceed the ~300 ms first speech chunk generation latency.
|
||||
```bash
|
||||
python demo/vibevoice_realtime_demo.py --model_path microsoft/VibeVoice-Realtime-0.5B
|
||||
```
|
||||
|
||||
Tip: Just try it on [Colab](https://colab.research.google.com/github/microsoft/VibeVoice/blob/main/demo/vibevoice_realtime_colab.ipynb).
|
||||
|
||||
### Usage 2: Inference from files directly
|
||||
```bash
|
||||
# We provide some example scripts under demo/text_examples/ for demo
|
||||
python demo/realtime_model_inference_from_file.py --model_path microsoft/VibeVoice-Realtime-0.5B --txt_path demo/text_examples/1p_vibevoice.txt --speaker_name Carter
|
||||
```
|
||||
|
||||
### [Optional] More experimental voices
|
||||
Download additional experimental multi-lingual speakers before launching demo or inference from files.
|
||||
```bash
|
||||
bash demo/download_experimental_voices.sh
|
||||
```
|
||||
## Risks and limitations
|
||||
|
||||
While efforts have been made to optimize it through various techniques, it may still produce outputs that are unexpected, biased, or inaccurate. VibeVoice inherits any biases, errors, or omissions produced by its base model (specifically, Qwen2.5 0.5b in this release).
|
||||
|
||||
Potential for Deepfakes and Disinformation: High-quality synthetic speech can be misused to create convincing fake audio content for impersonation, fraud, or spreading disinformation. Users must ensure transcripts are reliable, check content accuracy, and avoid using generated content in misleading ways. Users are expected to use the generated content and to deploy the models in a lawful manner, in full compliance with all applicable laws and regulations in the relevant jurisdictions. It is best practice to disclose the use of AI when sharing AI-generated content.
|
||||
|
||||
English only: Transcripts in languages other than English may result in unexpected audio outputs.
|
||||
|
||||
Non-Speech Audio: The model focuses solely on speech synthesis and does not handle background noise, music, or other sound effects.
|
||||
|
||||
Code, formulas, and special symbols: The model does not currently support reading code, mathematical formulas, or uncommon symbols. Please pre‑process input text to remove or normalize such content to avoid unpredictable results.
|
||||
|
||||
Very short inputs: When the input text is extremely short (three words or fewer), the model’s stability may degrade.
|
||||
|
||||
We do not recommend using VibeVoice in commercial or real-world applications without further testing and development. This model is intended for research and development purposes only. Please use responsibly.
|
||||
@@ -0,0 +1,147 @@
|
||||
# VibeVoice-TTS
|
||||
|
||||
[](https://huggingface.co/microsoft/VibeVoice-1.5B)
|
||||
[](https://arxiv.org/pdf/2508.19205)
|
||||
|
||||
**VibeVoice-TTS** is a **long-form**, **multi-speaker** text-to-speech model designed to generate **expressive conversational audio** such as podcasts from text. It can synthesize speech up to **90 minutes** long with up to **4 distinct speakers**, surpassing the typical 1–2 speaker limits of many prior models.
|
||||
|
||||
|
||||
**Model:** [VibeVoice-1.5B](https://huggingface.co/microsoft/VibeVoice-1.5B)<br>
|
||||
**Report:** [Technical Report](https://arxiv.org/pdf/2508.19205)<br>
|
||||
|
||||
|
||||
<div align="center">
|
||||
|
||||
| Model | Context Length | Generation Length | Weight |
|
||||
|-------|----------------|-------------------|--------|
|
||||
| VibeVoice-1.5B | 64K | ~90 min | [HF link](https://huggingface.co/microsoft/VibeVoice-1.5B) |
|
||||
| VibeVoice-Large | 32K | ~45 min | Disabled |
|
||||
|
||||
</div>
|
||||
|
||||
## 🔥 Key Features
|
||||
|
||||
- **⏱️ 90-minute Long-form Generation**:
|
||||
Synthesizes conversational/single-speaker speech up to **90 minutes** in a single pass, maintaining speaker consistency and semantic coherence throughout.
|
||||
|
||||
- **👥 Multi-speaker Support**:
|
||||
Supports up to **4 distinct speakers** in a single conversation, with natural turn-taking and speaker consistency across long dialogues.
|
||||
|
||||
- **🎭 Expressive Speech**:
|
||||
Generates expressive, natural-sounding speech that captures conversational dynamics and emotional nuances.
|
||||
|
||||
- **🌐 Multi-lingual Support**:
|
||||
Supports English, Chinese and other languages.
|
||||
|
||||
## 🏗️ Model Architecture
|
||||
|
||||
VibeVoice-TTS employs a [next-token diffusion](https://arxiv.org/pdf/2508.19205) framework that combines:
|
||||
|
||||
- **Large Language Model (LLM)**: Based on Qwen2.5, understands textual context and dialogue flow
|
||||
- **Continuous Speech Tokenizers**: Acoustic and Semantic tokenizers operating at an ultra-low frame rate of **7.5 Hz**, efficiently preserving audio fidelity while boosting computational efficiency
|
||||
- **Diffusion Head**: Generates high-fidelity acoustic details through diffusion-based generation
|
||||
|
||||
<div align="center">
|
||||
<img src="../Figures/VibeVoice.jpg" alt="VibeVoice Overview" width="80%">
|
||||
</div>
|
||||
|
||||
|
||||
## 🎵 Demo Examples
|
||||
|
||||
**English**
|
||||
<div align="center">
|
||||
|
||||
https://github.com/user-attachments/assets/0967027c-141e-4909-bec8-091558b1b784
|
||||
|
||||
</div>
|
||||
|
||||
**Chinese**
|
||||
<div align="center">
|
||||
|
||||
https://github.com/user-attachments/assets/322280b7-3093-4c67-86e3-10be4746c88f
|
||||
|
||||
</div>
|
||||
|
||||
**Cross-Lingual**
|
||||
<div align="center">
|
||||
|
||||
https://github.com/user-attachments/assets/838d8ad9-a201-4dde-bb45-8cd3f59ce722
|
||||
|
||||
</div>
|
||||
|
||||
**Spontaneous Singing**
|
||||
<div align="center">
|
||||
|
||||
https://github.com/user-attachments/assets/6f27a8a5-0c60-4f57-87f3-7dea2e11c730
|
||||
|
||||
</div>
|
||||
|
||||
**Long Conversation with 4 people**
|
||||
<div align="center">
|
||||
|
||||
https://github.com/user-attachments/assets/a357c4b6-9768-495c-a576-1618f6275727
|
||||
|
||||
</div>
|
||||
|
||||
For more examples, see the [Project Page](https://microsoft.github.io/VibeVoice).
|
||||
|
||||
## Installation and Usage
|
||||
Disabled due to widespread misuse.
|
||||
|
||||
## Results
|
||||
|
||||
The model achieves state-of-the-art performance on long-form multi-speaker speech generation tasks. For detailed evaluation results, please refer to the [paper](https://arxiv.org/pdf/2508.19205).
|
||||
<div align="center">
|
||||
<img src="../Figures/VibeVoice-TTS-results.jpg" alt="VibeVoice Results" width="80%">
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
## 🚨 Tips
|
||||
We observed users may encounter occasional instability when synthesizing Chinese speech. We recommend:
|
||||
|
||||
- Using English punctuation even for Chinese text, preferably only commas and periods.
|
||||
- Using the Large model variant, which is considerably more stable.
|
||||
- If you find that the generated voice speaks too fast, try chunking your text into multiple turns with the same speaker label.
|
||||
|
||||
We'd like to thank [PsiPi](https://huggingface.co/PsiPi) for sharing an interesting way for emotion control. Details can be found via [discussion12](https://huggingface.co/microsoft/VibeVoice-1.5B/discussions/12).
|
||||
|
||||
|
||||
## FAQ
|
||||
#### Q1: Is this a pretrained model?
|
||||
**A:** Yes, it's a pretrained model without any post-training or benchmark-specific optimizations. In a way, this makes VibeVoice very versatile and fun to use.
|
||||
|
||||
#### Q2: Randomly trigger Sounds / Music / BGM.
|
||||
**A:** As you can see from our demo page, the background music or sounds are spontaneous. This means we can't directly control whether they are generated or not. The model is content-aware, and these sounds are triggered based on the input text and the chosen voice prompt.
|
||||
|
||||
Here are a few things we've noticed:
|
||||
* If the voice prompt you use contains background music, the generated speech is more likely to have it as well. (The Large model is quite stable and effective at this—give it a try on the demo!)
|
||||
* If the voice prompt is clean (no BGM), but the input text includes introductory words or phrases like "Welcome to," "Hello," or "However," background music might still appear.
|
||||
* Speaker voice related, using "Alice" results in random BGM than others (fixed).
|
||||
* In other scenarios, the Large model is more stable and has a lower probability of generating unexpected background music.
|
||||
|
||||
In fact, we intentionally decided not to denoise our training data because we think it's an interesting feature for BGM to show up at just the right moment. You can think of it as a little easter egg we left for you.
|
||||
|
||||
#### Q3: Text normalization?
|
||||
**A:** We don't perform any text normalization during training or inference. Our philosophy is that a large language model should be able to handle complex user inputs on its own. However, due to the nature of the training data, you might still run into some corner cases.
|
||||
|
||||
#### Q4: Singing Capability.
|
||||
**A:** Our training data **doesn't contain any music data**. The ability to sing is an emergent capability of the model (which is why it might sound off-key, even on a famous song like 'See You Again'). (The Large model is more likely to exhibit this than the 1.5B).
|
||||
|
||||
#### Q5: Some Chinese pronunciation errors.
|
||||
**A:** The volume of Chinese data in our training set is significantly smaller than the English data. Additionally, certain special characters (e.g., Chinese quotation marks) may occasionally cause pronunciation issues.
|
||||
|
||||
#### Q6: Instability of cross-lingual transfer.
|
||||
**A:** The model does exhibit strong cross-lingual transfer capabilities, including the preservation of accents, but its performance can be unstable. This is an emergent ability of the model that we have not specifically optimized. It's possible that a satisfactory result can be achieved through repeated sampling.
|
||||
|
||||
## Risks and Limitations
|
||||
|
||||
While efforts have been made to optimize it through various techniques, it may still produce outputs that are unexpected, biased, or inaccurate. VibeVoice inherits any biases, errors, or omissions produced by its base model (specifically, Qwen2.5 1.5b in this release). Potential for Deepfakes and Disinformation: High-quality synthetic speech can be misused to create convincing fake audio content for impersonation, fraud, or spreading disinformation. Users must ensure transcripts are reliable, check content accuracy, and avoid using generated content in misleading ways. Users are expected to use the generated content and to deploy the models in a lawful manner, in full compliance with all applicable laws and regulations in the relevant jurisdictions. It is best practice to disclose the use of AI when sharing AI-generated content.
|
||||
|
||||
English and Chinese only: Transcripts in languages other than English or Chinese may result in unexpected audio outputs.
|
||||
|
||||
Non-Speech Audio: The model focuses solely on speech synthesis and does not handle background noise, music, or other sound effects.
|
||||
|
||||
Overlapping Speech: The current model does not explicitly model or generate overlapping speech segments in conversations.
|
||||
|
||||
We do not recommend using VibeVoice in commercial or real-world applications without further testing and development. This model is intended for research and development purposes only. Please use responsibly.
|
||||
@@ -0,0 +1,189 @@
|
||||
# VibeVoice vLLM ASR Deployment
|
||||
|
||||
<a href="https://huggingface.co/microsoft/VibeVoice-ASR"><img alt="Huggingface" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-VibeVoice--ASR-blue"></a>
|
||||
|
||||
Deploy VibeVoice ASR model as a high-performance API service using [vLLM](https://github.com/vllm-project/vllm). This plugin provides OpenAI-compatible API endpoints for speech-to-text transcription with streaming support.
|
||||
|
||||
## 🔥 Key Features
|
||||
|
||||
- **🚀 High-Performance Serving**: Optimized for high-throughput ASR inference with vLLM's continuous batching
|
||||
- **📡 OpenAI-Compatible API**: Standard `/v1/chat/completions` endpoint with streaming support
|
||||
- **🎵 Long Audio Support**: Process up to 60+ minutes of audio in a single request
|
||||
- **🔌 Plugin Architecture**: No vLLM source code modification required - just install and run
|
||||
- **⚡ Data Parallel (DP)**: Run independent model replicas across multiple GPUs with automatic load balancing behind a single port
|
||||
|
||||
## 🛠️ Installation
|
||||
|
||||
Using Official vLLM Docker Image (Recommended)
|
||||
|
||||
1. Clone the repository
|
||||
```bash
|
||||
git clone https://github.com/microsoft/VibeVoice.git
|
||||
cd VibeVoice
|
||||
```
|
||||
|
||||
2. Launch the server (background mode)
|
||||
```bash
|
||||
docker run -d --gpus all --name vibevoice-vllm \
|
||||
--ipc=host \
|
||||
-p 8000:8000 \
|
||||
-e VIBEVOICE_FFMPEG_MAX_CONCURRENCY=64 \
|
||||
-e PYTORCH_ALLOC_CONF=expandable_segments:True \
|
||||
-v $(pwd):/app \
|
||||
-w /app \
|
||||
--entrypoint bash \
|
||||
vllm/vllm-openai:v0.14.1 \
|
||||
-c "python3 /app/vllm_plugin/scripts/start_server.py"
|
||||
```
|
||||
|
||||
## ⚡ Multi-GPU Deployment
|
||||
|
||||
The launcher supports two types of GPU parallelism via `--tp` and `--dp` flags:
|
||||
|
||||
| Flag | Name | What it does |
|
||||
|------|------|-------------|
|
||||
| `--tp N` | Tensor Parallel | Splits **one model** across N GPUs (for models too large for a single GPU) |
|
||||
| `--dp N` | Data Parallel | Runs **N independent replicas**, one per GPU, with automatic load balancing behind a single port |
|
||||
|
||||
### Data Parallel (Recommended for scaling throughput)
|
||||
|
||||
Run N independent replicas on N GPUs with automatic load balancing behind a single port.
|
||||
When `--dp N` is specified (N > 1), the launcher automatically starts N independent vLLM
|
||||
processes behind an nginx reverse proxy (2×N workers) for optimal throughput:
|
||||
|
||||
```bash
|
||||
docker run -d --gpus '"device=0,1,2,3"' --name vibevoice-vllm \
|
||||
--ipc=host \
|
||||
-p 8000:8000 \
|
||||
-e VIBEVOICE_FFMPEG_MAX_CONCURRENCY=64 \
|
||||
-e PYTORCH_ALLOC_CONF=expandable_segments:True \
|
||||
-v $(pwd):/app \
|
||||
-w /app \
|
||||
--entrypoint bash \
|
||||
vllm/vllm-openai:v0.14.1 \
|
||||
-c "python3 /app/vllm_plugin/scripts/start_server.py --dp 4"
|
||||
```
|
||||
|
||||
Run on all 8 GPUs:
|
||||
|
||||
```bash
|
||||
docker run -d --gpus all --name vibevoice-vllm \
|
||||
--ipc=host \
|
||||
-p 8000:8000 \
|
||||
-e VIBEVOICE_FFMPEG_MAX_CONCURRENCY=64 \
|
||||
-e PYTORCH_ALLOC_CONF=expandable_segments:True \
|
||||
-v $(pwd):/app \
|
||||
-w /app \
|
||||
--entrypoint bash \
|
||||
vllm/vllm-openai:v0.14.1 \
|
||||
-c "python3 /app/vllm_plugin/scripts/start_server.py --dp 8"
|
||||
```
|
||||
|
||||
### Tensor Parallel
|
||||
|
||||
Split a single model across 2 GPUs (useful if GPU memory is limited):
|
||||
|
||||
```bash
|
||||
docker run -d --gpus '"device=0,1"' --name vibevoice-vllm \
|
||||
--ipc=host \
|
||||
-p 8000:8000 \
|
||||
-e VIBEVOICE_FFMPEG_MAX_CONCURRENCY=64 \
|
||||
-e PYTORCH_ALLOC_CONF=expandable_segments:True \
|
||||
-v $(pwd):/app \
|
||||
-w /app \
|
||||
--entrypoint bash \
|
||||
vllm/vllm-openai:v0.14.1 \
|
||||
-c "python3 /app/vllm_plugin/scripts/start_server.py --tp 2"
|
||||
```
|
||||
|
||||
### Hybrid (DP × TP)
|
||||
|
||||
Combine both — e.g., 2 replicas, each split across 2 GPUs (4 GPUs total):
|
||||
|
||||
```bash
|
||||
docker run -d --gpus '"device=0,1,2,3"' --name vibevoice-vllm \
|
||||
--ipc=host \
|
||||
-p 8000:8000 \
|
||||
-v $(pwd):/app \
|
||||
-w /app \
|
||||
--entrypoint bash \
|
||||
vllm/vllm-openai:v0.14.1 \
|
||||
-c "python3 /app/vllm_plugin/scripts/start_server.py --dp 2 --tp 2"
|
||||
```
|
||||
|
||||
> **Note**: Total GPUs required = `dp × tp`. Make sure to expose enough GPU devices in the Docker `--gpus` flag.
|
||||
|
||||
3. View logs
|
||||
```bash
|
||||
docker logs -f vibevoice-vllm
|
||||
```
|
||||
|
||||
> **Note**:
|
||||
> - The `-d` flag runs the container in background (detached mode)
|
||||
> - Use `docker stop vibevoice-vllm` to stop the service
|
||||
> - The model will be downloaded to HuggingFace cache (`~/.cache/huggingface`) inside the container
|
||||
|
||||
## 🚀 Usages
|
||||
|
||||
### Test the API
|
||||
|
||||
Once the vLLM server is running, test it with the provided script:
|
||||
|
||||
```bash
|
||||
# Basic transcription
|
||||
docker exec -it vibevoice-vllm python3 vllm_plugin/tests/test_api.py /app/audio.wav
|
||||
|
||||
# With hotwords for better recognition of specific terms
|
||||
docker exec -it vibevoice-vllm python3 vllm_plugin/tests/test_api.py /app/audio.wav --hotwords "Microsoft,VibeVoice"
|
||||
|
||||
```
|
||||
|
||||
```bash
|
||||
# With auto-recovery from repetition loops (for long audio)
|
||||
docker exec -it vibevoice-vllm python3 vllm_plugin/tests/test_api_auto_recover.py /app/audio.wav
|
||||
|
||||
# Auto-recover with hotwords
|
||||
docker exec -it vibevoice-vllm python3 vllm_plugin/tests/test_api_auto_recover.py /app/audio.wav --hotwords "Microsoft,VibeVoice"
|
||||
```
|
||||
|
||||
> **Note**:
|
||||
> - The audio/video file must be inside the mounted directory (`/app` in the container). Copy your files to the VibeVoice folder before testing.
|
||||
> - Hotwords help improve recognition of domain-specific terms like proper nouns, technical terms, and speaker names.
|
||||
|
||||
### Environment Variables
|
||||
|
||||
| Variable | Description | Default |
|
||||
|----------|-------------|---------|
|
||||
| `VIBEVOICE_FFMPEG_MAX_CONCURRENCY` | Maximum FFmpeg processes for audio decoding | `64` |
|
||||
| `PYTORCH_ALLOC_CONF` | PyTorch memory allocator config | `expandable_segments:True` |
|
||||
|
||||
|
||||
|
||||
## 📊 Performance Tips
|
||||
|
||||
1. **GPU Memory**: Use `--gpu-memory-utilization 0.9` for maximum throughput if you have dedicated GPU
|
||||
2. **Batch Size**: Increase `--max-num-seqs` for higher concurrency (requires more GPU memory)
|
||||
3. **FFmpeg Concurrency**: Tune `VIBEVOICE_FFMPEG_MAX_CONCURRENCY` based on CPU cores
|
||||
|
||||
## 🚨 Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
1. **"CUDA out of memory"**
|
||||
- Reduce `--gpu-memory-utilization`
|
||||
- Reduce `--max-num-seqs`
|
||||
- Use smaller `--max-model-len`
|
||||
|
||||
2. **"Audio decoding failed"**
|
||||
- Ensure FFmpeg is installed: `ffmpeg -version`
|
||||
- Check audio file format is supported
|
||||
|
||||
3. **"Model not found"**
|
||||
- Ensure model path contains `config.json` and model weights
|
||||
- Generate tokenizer files if missing
|
||||
|
||||
4. **"Plugin not loaded"**
|
||||
- Verify installation: `pip show vibevoice`
|
||||
- Check entry point: `pip show -f vibevoice | grep entry`
|
||||
|
||||
|
||||
@@ -0,0 +1,154 @@
|
||||
# VibeVoice ASR LoRA Fine-tuning
|
||||
|
||||
This directory contains scripts for LoRA (Low-Rank Adaptation) fine-tuning of the VibeVoice ASR model.
|
||||
|
||||
## Requirements
|
||||
|
||||
```bash
|
||||
# Install vibevoice first
|
||||
pip install -e .
|
||||
|
||||
pip install peft
|
||||
```
|
||||
|
||||
## Toy Dataset
|
||||
|
||||
> **Note**: The `toy_dataset/` included in this directory contains **synthetic audio generated by VibeVoice TTS** for demonstration purposes only. It is NOT a full finetuning dataset.
|
||||
>
|
||||
> When using your own data, you should:
|
||||
> - Prepare real audio recordings with accurate transcriptions
|
||||
> - Adjust hyperparameters (learning rate, epochs, LoRA rank) based on your dataset size and domain
|
||||
> - Consider the audio quality and speaker diversity in your data
|
||||
|
||||
## Data Format
|
||||
|
||||
Training data should be organized as pairs of audio files and JSON labels in the same directory:
|
||||
|
||||
```
|
||||
toy_dataset/
|
||||
├── 0.mp3
|
||||
├── 0.json
|
||||
├── 1.mp3
|
||||
├── 1.json
|
||||
└── ...
|
||||
```
|
||||
|
||||
### JSON Label Format
|
||||
|
||||
Each JSON file should have the following structure:
|
||||
|
||||
```json
|
||||
{
|
||||
"audio_duration": 351.73,
|
||||
"audio_path": "0.mp3",
|
||||
"segments": [
|
||||
{
|
||||
"speaker": 0,
|
||||
"text": "Hey everyone, welcome back...",
|
||||
"start": 0.0,
|
||||
"end": 38.68
|
||||
},
|
||||
{
|
||||
"speaker": 1,
|
||||
"text": "Thanks for having me...",
|
||||
"start": 38.75,
|
||||
"end": 77.88
|
||||
}
|
||||
],
|
||||
"customized_context": ["Tea Brew", "Aiden Host", "The property is near Meter Street."] // optional, domain-specific terms or context sentences
|
||||
}
|
||||
```
|
||||
|
||||
## Training
|
||||
|
||||
### Basic
|
||||
|
||||
```bash
|
||||
# 1 GPU
|
||||
torchrun --nproc_per_node=1 lora_finetune.py \
|
||||
--model_path microsoft/VibeVoice-ASR \
|
||||
--data_dir ./toy_dataset \
|
||||
--output_dir ./output \
|
||||
--num_train_epochs 3 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--learning_rate 1e-4 \
|
||||
--bf16 \
|
||||
--report_to none
|
||||
|
||||
# Specific GPUs (e.g., GPU 0,1,2,3)
|
||||
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 lora_finetune.py \
|
||||
--model_path microsoft/VibeVoice-ASR \
|
||||
--data_dir ./toy_dataset \
|
||||
--output_dir ./output \
|
||||
--num_train_epochs 3 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--learning_rate 1e-4 \
|
||||
--bf16 \
|
||||
--report_to none
|
||||
```
|
||||
|
||||
### Full Options
|
||||
|
||||
The script uses HuggingFace's `TrainingArguments`, so all standard options are available:
|
||||
|
||||
```bash
|
||||
torchrun --nproc_per_node=4 lora_finetune.py \
|
||||
--model_path microsoft/VibeVoice-ASR \
|
||||
--data_dir ./toy_dataset \
|
||||
--output_dir ./output \
|
||||
--lora_r 16 \
|
||||
--lora_alpha 32 \
|
||||
--lora_dropout 0.05 \
|
||||
--num_train_epochs 3 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--gradient_accumulation_steps 4 \
|
||||
--learning_rate 1e-4 \
|
||||
--warmup_ratio 0.1 \
|
||||
--weight_decay 0.01 \
|
||||
--max_grad_norm 1.0 \
|
||||
--logging_steps 10 \
|
||||
--save_steps 100 \
|
||||
--gradient_checkpointing \
|
||||
--bf16 \
|
||||
--report_to none
|
||||
```
|
||||
|
||||
### Key Parameters
|
||||
|
||||
| Parameter | Default | Description |
|
||||
|-----------|---------|-------------|
|
||||
| `--lora_r` | 16 | LoRA rank (lower = fewer params, higher = more expressive) |
|
||||
| `--lora_alpha` | 32 | LoRA scaling factor (typically 2x rank) |
|
||||
| `--lora_dropout` | 0.05 | Dropout for LoRA layers |
|
||||
| `--per_device_train_batch_size` | 8 | Batch size per device |
|
||||
| `--gradient_accumulation_steps` | 1 | Effective batch size = batch_size × grad_accum |
|
||||
| `--learning_rate` | 5e-5 | Learning rate (1e-4 to 2e-4 typical for LoRA) |
|
||||
| `--gradient_checkpointing` | False | Enable to reduce memory usage |
|
||||
| `--use_customized_context` | True | Include customized_context from JSON as additional context |
|
||||
| `--max_audio_length` | None | Skip audio longer than this (seconds) |
|
||||
|
||||
## Inference with Fine-tuned Model
|
||||
|
||||
```bash
|
||||
python inference_lora.py \
|
||||
--base_model microsoft/VibeVoice-ASR \
|
||||
--lora_path ./output \
|
||||
--audio_file ./toy_dataset/0.mp3 \
|
||||
--context_info "Tea Brew, Aiden Host"
|
||||
```
|
||||
|
||||
## Merging LoRA Weights (Optional)
|
||||
|
||||
To merge LoRA weights into the base model for faster inference:
|
||||
|
||||
```python
|
||||
from peft import PeftModel
|
||||
|
||||
# Load base model + LoRA
|
||||
model = VibeVoiceASRForConditionalGeneration.from_pretrained("microsoft/VibeVoice-ASR", ...)
|
||||
model = PeftModel.from_pretrained(model, "./output")
|
||||
|
||||
# Merge and save
|
||||
model = model.merge_and_unload()
|
||||
model.save_pretrained("./merged_model")
|
||||
```
|
||||
@@ -0,0 +1,234 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
Inference with LoRA Fine-tuned VibeVoice ASR Model
|
||||
|
||||
This script loads a LoRA fine-tuned model and runs inference.
|
||||
|
||||
Usage:
|
||||
python inference_lora.py \
|
||||
--base_model microsoft/VibeVoice-ASR \
|
||||
--lora_path ./output \
|
||||
--audio_file ./toy_dataset/0.mp3
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import torch
|
||||
|
||||
from peft import PeftModel
|
||||
|
||||
from vibevoice.modular.modeling_vibevoice_asr import VibeVoiceASRForConditionalGeneration
|
||||
from vibevoice.processor.vibevoice_asr_processor import VibeVoiceASRProcessor
|
||||
|
||||
|
||||
def load_lora_model(
|
||||
base_model_path: str,
|
||||
lora_path: str,
|
||||
device: str = "cuda",
|
||||
dtype: torch.dtype = torch.bfloat16,
|
||||
):
|
||||
"""
|
||||
Load base model and merge with LoRA weights.
|
||||
|
||||
Args:
|
||||
base_model_path: Path to base pretrained model
|
||||
lora_path: Path to LoRA adapter weights
|
||||
device: Device to load model on
|
||||
dtype: Data type for model
|
||||
|
||||
Returns:
|
||||
Tuple of (model, processor)
|
||||
"""
|
||||
print(f"Loading base model from {base_model_path}")
|
||||
|
||||
# Load processor
|
||||
processor = VibeVoiceASRProcessor.from_pretrained(
|
||||
base_model_path,
|
||||
language_model_pretrained_name="Qwen/Qwen2.5-7B"
|
||||
)
|
||||
|
||||
# Load base model
|
||||
model = VibeVoiceASRForConditionalGeneration.from_pretrained(
|
||||
base_model_path,
|
||||
dtype=dtype,
|
||||
device_map=device if device == "auto" else None,
|
||||
attn_implementation="flash_attention_2",
|
||||
trust_remote_code=True,
|
||||
)
|
||||
|
||||
if device != "auto":
|
||||
model = model.to(device)
|
||||
|
||||
# Load LoRA adapter
|
||||
print(f"Loading LoRA adapter from {lora_path}")
|
||||
model = PeftModel.from_pretrained(model, lora_path)
|
||||
|
||||
# Optionally merge LoRA weights into base model for faster inference
|
||||
# model = model.merge_and_unload()
|
||||
|
||||
model.eval()
|
||||
print("Model loaded successfully")
|
||||
|
||||
return model, processor
|
||||
|
||||
|
||||
def transcribe(
|
||||
model,
|
||||
processor,
|
||||
audio_path: str,
|
||||
max_new_tokens: int = 4096,
|
||||
temperature: float = 0.0,
|
||||
context_info: str = None,
|
||||
device: str = "cuda",
|
||||
):
|
||||
"""
|
||||
Transcribe an audio file using the LoRA fine-tuned model.
|
||||
|
||||
Args:
|
||||
model: The LoRA fine-tuned model
|
||||
processor: The processor
|
||||
audio_path: Path to audio file
|
||||
max_new_tokens: Maximum tokens to generate
|
||||
temperature: Sampling temperature (0 = greedy)
|
||||
context_info: Optional context info (e.g., hotwords)
|
||||
device: Device
|
||||
|
||||
Returns:
|
||||
Transcription result
|
||||
"""
|
||||
print(f"\nTranscribing: {audio_path}")
|
||||
|
||||
# Process audio
|
||||
inputs = processor(
|
||||
audio=audio_path,
|
||||
sampling_rate=None,
|
||||
return_tensors="pt",
|
||||
padding=True,
|
||||
add_generation_prompt=True,
|
||||
context_info=context_info,
|
||||
)
|
||||
|
||||
# Move to device
|
||||
inputs = {k: v.to(device) if isinstance(v, torch.Tensor) else v
|
||||
for k, v in inputs.items()}
|
||||
|
||||
# Generation config
|
||||
gen_config = {
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"pad_token_id": processor.pad_id,
|
||||
"eos_token_id": processor.tokenizer.eos_token_id,
|
||||
"do_sample": temperature > 0,
|
||||
}
|
||||
if temperature > 0:
|
||||
gen_config["temperature"] = temperature
|
||||
gen_config["top_p"] = 0.9
|
||||
|
||||
# Generate
|
||||
with torch.no_grad():
|
||||
output_ids = model.generate(**inputs, **gen_config)
|
||||
|
||||
# Decode
|
||||
input_length = inputs['input_ids'].shape[1]
|
||||
generated_ids = output_ids[0, input_length:]
|
||||
generated_text = processor.decode(generated_ids, skip_special_tokens=True)
|
||||
|
||||
# Parse structured output
|
||||
try:
|
||||
segments = processor.post_process_transcription(generated_text)
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to parse structured output: {e}")
|
||||
segments = []
|
||||
|
||||
return {
|
||||
"raw_text": generated_text,
|
||||
"segments": segments,
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Inference with LoRA Fine-tuned VibeVoice ASR")
|
||||
|
||||
parser.add_argument(
|
||||
"--base_model",
|
||||
type=str,
|
||||
default="microsoft/VibeVoice-ASR",
|
||||
help="Path to base pretrained model"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--lora_path",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to LoRA adapter weights"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--audio_file",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to audio file to transcribe"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--context_info",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Optional context info (e.g., 'Hotwords: Tea Brew, Aiden Host')"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max_new_tokens",
|
||||
type=int,
|
||||
default=4096,
|
||||
help="Maximum tokens to generate"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--temperature",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="Sampling temperature (0 = greedy)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default="cuda" if torch.cuda.is_available() else "cpu",
|
||||
help="Device to use"
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Load model
|
||||
dtype = torch.bfloat16 if args.device != "cpu" else torch.float32
|
||||
model, processor = load_lora_model(
|
||||
base_model_path=args.base_model,
|
||||
lora_path=args.lora_path,
|
||||
device=args.device,
|
||||
dtype=dtype,
|
||||
)
|
||||
|
||||
# Transcribe
|
||||
result = transcribe(
|
||||
model=model,
|
||||
processor=processor,
|
||||
audio_path=args.audio_file,
|
||||
max_new_tokens=args.max_new_tokens,
|
||||
temperature=args.temperature,
|
||||
context_info=args.context_info,
|
||||
device=args.device,
|
||||
)
|
||||
|
||||
# Print results
|
||||
print("\n" + "="*60)
|
||||
print("Transcription Result")
|
||||
print("="*60)
|
||||
|
||||
print("\n--- Raw Output ---")
|
||||
raw_text = result['raw_text']
|
||||
print(raw_text[:2000] + "..." if len(raw_text) > 2000 else raw_text)
|
||||
|
||||
if result['segments']:
|
||||
print(f"\n--- Structured Output ({len(result['segments'])} segments) ---")
|
||||
for seg in result['segments'][:20]:
|
||||
print(f"[{seg.get('start_time', 'N/A')} - {seg.get('end_time', 'N/A')}] "
|
||||
f"Speaker {seg.get('speaker_id', 'N/A')}: {seg.get('text', '')[:80]}...")
|
||||
if len(result['segments']) > 20:
|
||||
print(f" ... and {len(result['segments']) - 20} more segments")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,556 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
VibeVoice ASR LoRA Fine-tuning Script
|
||||
|
||||
This script implements LoRA (Low-Rank Adaptation) fine-tuning for the VibeVoice ASR model.
|
||||
It uses PEFT (Parameter-Efficient Fine-Tuning) library for efficient training.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Any, Optional, Tuple
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.utils.data import Dataset, DataLoader
|
||||
import numpy as np
|
||||
|
||||
from transformers import (
|
||||
TrainingArguments,
|
||||
Trainer,
|
||||
HfArgumentParser,
|
||||
)
|
||||
from peft import (
|
||||
LoraConfig,
|
||||
get_peft_model,
|
||||
prepare_model_for_kbit_training,
|
||||
TaskType,
|
||||
)
|
||||
|
||||
from vibevoice.modular.modeling_vibevoice_asr import VibeVoiceASRForConditionalGeneration
|
||||
from vibevoice.processor.vibevoice_asr_processor import VibeVoiceASRProcessor
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
level=logging.INFO,
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelArguments:
|
||||
"""Arguments for model configuration."""
|
||||
model_path: str = field(
|
||||
default="microsoft/VibeVoice-ASR",
|
||||
metadata={"help": "Path to pretrained model (HuggingFace model ID or local path)"}
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class DataArguments:
|
||||
"""Arguments for data configuration."""
|
||||
data_dir: str = field(
|
||||
default="./toy_dataset",
|
||||
metadata={"help": "Directory containing training data"}
|
||||
)
|
||||
max_audio_length: Optional[float] = field(
|
||||
default=None,
|
||||
metadata={"help": "Maximum audio length in seconds (default: no limit)"}
|
||||
)
|
||||
use_customized_context: bool = field(
|
||||
default=True,
|
||||
metadata={"help": "Whether to use customized_context from JSON as additional context"}
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class LoraArguments:
|
||||
"""Arguments for LoRA configuration."""
|
||||
lora_r: int = field(
|
||||
default=16,
|
||||
metadata={"help": "LoRA rank"}
|
||||
)
|
||||
lora_alpha: int = field(
|
||||
default=32,
|
||||
metadata={"help": "LoRA alpha (scaling factor)"}
|
||||
)
|
||||
lora_dropout: float = field(
|
||||
default=0.05,
|
||||
metadata={"help": "LoRA dropout"}
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class VibeVoiceASRDataCollator:
|
||||
"""
|
||||
Data collator for VibeVoice ASR fine-tuning.
|
||||
Handles batching of variable-length audio and text sequences.
|
||||
"""
|
||||
processor: VibeVoiceASRProcessor
|
||||
pad_token_id: int
|
||||
label_pad_token_id: int = -100
|
||||
|
||||
def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
|
||||
"""
|
||||
Collate a batch of features into model inputs.
|
||||
"""
|
||||
# Separate inputs and labels
|
||||
input_ids_list = [f["input_ids"] for f in features]
|
||||
labels_list = [f["labels"] for f in features]
|
||||
acoustic_mask_list = [f["acoustic_input_mask"] for f in features]
|
||||
speech_list = [f["speech"] for f in features]
|
||||
vae_tok_lens = [f["vae_tok_len"] for f in features]
|
||||
|
||||
# Determine max lengths
|
||||
max_seq_len = max(len(ids) for ids in input_ids_list)
|
||||
max_speech_len = max(len(s) for s in speech_list)
|
||||
max_vae_len = max(vae_tok_lens)
|
||||
|
||||
batch_size = len(features)
|
||||
|
||||
# Initialize padded tensors
|
||||
input_ids = torch.full((batch_size, max_seq_len), self.pad_token_id, dtype=torch.long)
|
||||
attention_mask = torch.zeros((batch_size, max_seq_len), dtype=torch.long)
|
||||
labels = torch.full((batch_size, max_seq_len), self.label_pad_token_id, dtype=torch.long)
|
||||
acoustic_input_mask = torch.zeros((batch_size, max_seq_len), dtype=torch.bool)
|
||||
speech_tensors = torch.zeros((batch_size, max_speech_len), dtype=torch.float32)
|
||||
speech_masks = torch.zeros((batch_size, max_vae_len), dtype=torch.bool)
|
||||
|
||||
# Fill in the tensors (right padding for training)
|
||||
# Note: processor uses left padding for inference/generation, but training uses right padding
|
||||
for i, (ids, lbls, amask, speech, vae_len) in enumerate(
|
||||
zip(input_ids_list, labels_list, acoustic_mask_list, speech_list, vae_tok_lens)
|
||||
):
|
||||
seq_len = len(ids)
|
||||
|
||||
# Right padding for input_ids and labels
|
||||
input_ids[i, :seq_len] = torch.tensor(ids, dtype=torch.long)
|
||||
attention_mask[i, :seq_len] = 1
|
||||
labels[i, :seq_len] = torch.tensor(lbls, dtype=torch.long)
|
||||
acoustic_input_mask[i, :seq_len] = torch.tensor(amask, dtype=torch.bool)
|
||||
|
||||
# Speech tensors (right padding, zeros work as padding)
|
||||
speech_len = len(speech)
|
||||
speech_tensors[i, :speech_len] = torch.tensor(speech, dtype=torch.float32)
|
||||
speech_masks[i, :vae_len] = True
|
||||
|
||||
return {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"labels": labels,
|
||||
"acoustic_input_mask": acoustic_input_mask,
|
||||
"speech_tensors": speech_tensors,
|
||||
"speech_masks": speech_masks,
|
||||
}
|
||||
|
||||
|
||||
class VibeVoiceASRDataset(Dataset):
|
||||
"""
|
||||
Dataset for VibeVoice ASR fine-tuning.
|
||||
|
||||
Expected data format:
|
||||
- Audio files: .mp3, .wav, .flac, etc.
|
||||
- Label files: .json with matching name
|
||||
|
||||
JSON format:
|
||||
{
|
||||
"audio_path": "0.mp3",
|
||||
"audio_duration": 351.73,
|
||||
"segments": [
|
||||
{
|
||||
"speaker": 0,
|
||||
"text": "Hey everyone, welcome back...",
|
||||
"start": 0.0,
|
||||
"end": 38.68
|
||||
},
|
||||
...
|
||||
],
|
||||
"customized_context": ["Tea Brew", "The property is near Meter Street."] # optional
|
||||
}
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
data_dir: str,
|
||||
processor: VibeVoiceASRProcessor,
|
||||
max_audio_length: Optional[float] = None, # in seconds
|
||||
use_customized_context: bool = True,
|
||||
):
|
||||
"""
|
||||
Initialize the dataset.
|
||||
|
||||
Args:
|
||||
data_dir: Directory containing audio files and JSON labels
|
||||
processor: VibeVoice ASR processor
|
||||
max_audio_length: Maximum audio length in seconds (None = no limit)
|
||||
use_customized_context: Whether to include customized_context in prompt
|
||||
"""
|
||||
self.data_dir = Path(data_dir)
|
||||
self.processor = processor
|
||||
self.max_audio_length = max_audio_length
|
||||
self.use_customized_context = use_customized_context
|
||||
|
||||
# Find all JSON files
|
||||
self.samples = self._load_samples()
|
||||
logger.info(f"Loaded {len(self.samples)} samples from {data_dir}")
|
||||
|
||||
def _load_samples(self) -> List[Dict[str, Any]]:
|
||||
"""Load and validate all samples from data directory."""
|
||||
samples = []
|
||||
|
||||
for json_path in sorted(self.data_dir.glob("*.json")):
|
||||
try:
|
||||
with open(json_path, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
|
||||
# Get audio path from JSON
|
||||
audio_filename = data.get("audio_path")
|
||||
if not audio_filename:
|
||||
logger.warning(f"No audio_path specified in {json_path}")
|
||||
continue
|
||||
|
||||
audio_path = self.data_dir / audio_filename
|
||||
if not audio_path.exists():
|
||||
logger.warning(f"Audio file not found: {audio_path}")
|
||||
continue
|
||||
|
||||
# Optional: filter by duration
|
||||
if self.max_audio_length is not None:
|
||||
duration = data.get("audio_duration", float("inf"))
|
||||
if duration > self.max_audio_length:
|
||||
logger.info(f"Skipping {json_path.stem}: duration {duration:.1f}s > max {self.max_audio_length}s")
|
||||
continue
|
||||
|
||||
samples.append({
|
||||
"audio_path": str(audio_path),
|
||||
"json_path": str(json_path),
|
||||
"data": data,
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error loading {json_path}: {e}")
|
||||
continue
|
||||
|
||||
return samples
|
||||
|
||||
def _format_transcription(self, segments: List[Dict], audio_duration: float) -> str:
|
||||
"""
|
||||
Format transcription segments into JSON output format.
|
||||
|
||||
This matches the expected model output format used in training.
|
||||
"""
|
||||
formatted_segments = []
|
||||
|
||||
for seg in segments:
|
||||
formatted_seg = {}
|
||||
# Add timestamp
|
||||
formatted_seg["Start"] = round(seg['start'], 2)
|
||||
formatted_seg["End"] = round(seg['end'], 2)
|
||||
# Add speaker if available
|
||||
if "speaker" in seg:
|
||||
formatted_seg["Speaker"] = seg["speaker"]
|
||||
# Add content
|
||||
formatted_seg["Content"] = seg.get("text", "")
|
||||
formatted_segments.append(formatted_seg)
|
||||
|
||||
# Return as compact JSON string (no spaces after separators)
|
||||
return json.dumps(formatted_segments, ensure_ascii=False, separators=(',', ':'))
|
||||
|
||||
def __len__(self) -> int:
|
||||
return len(self.samples)
|
||||
|
||||
def __getitem__(self, idx: int) -> Dict[str, Any]:
|
||||
"""
|
||||
Get a single sample for training.
|
||||
|
||||
Returns:
|
||||
Dict with:
|
||||
- input_ids: Token IDs for input (system + user + assistant prompt)
|
||||
- labels: Token IDs for labels (-100 for non-predicted tokens)
|
||||
- acoustic_input_mask: Mask for speech token positions
|
||||
- speech: Raw audio array
|
||||
- vae_tok_len: Number of speech tokens
|
||||
"""
|
||||
sample = self.samples[idx]
|
||||
data = sample["data"]
|
||||
audio_path = sample["audio_path"]
|
||||
|
||||
# Prepare context info (customized_context)
|
||||
context_info = None
|
||||
if self.use_customized_context and "customized_context" in data:
|
||||
customized_context = data["customized_context"]
|
||||
if customized_context:
|
||||
context_info = "\n".join(customized_context)
|
||||
|
||||
# Process audio using the processor's internal method
|
||||
encoding = self.processor._process_single_audio(
|
||||
audio_path,
|
||||
sampling_rate=None,
|
||||
add_generation_prompt=True,
|
||||
use_streaming=True,
|
||||
context_info=context_info,
|
||||
)
|
||||
|
||||
# Get the input tokens (system + user + generation prompt)
|
||||
input_ids = encoding["input_ids"]
|
||||
acoustic_input_mask = encoding["acoustic_input_mask"]
|
||||
speech = encoding["speech"]
|
||||
vae_tok_len = encoding["vae_tok_len"]
|
||||
|
||||
# Format the target transcription
|
||||
target_text = self._format_transcription(
|
||||
data["segments"],
|
||||
data.get("audio_duration", len(speech) / 24000)
|
||||
)
|
||||
|
||||
# Encode target using apply_chat_template to match training format
|
||||
# This adds the assistant role tokens (e.g., <|im_start|>assistant\n...<|im_end|>)
|
||||
target_tokens = self.processor.tokenizer.apply_chat_template(
|
||||
[{"role": "assistant", "content": target_text}],
|
||||
tokenize=True,
|
||||
add_generation_prompt=False,
|
||||
)
|
||||
|
||||
# Combine input and target
|
||||
full_input_ids = input_ids + target_tokens
|
||||
full_acoustic_mask = acoustic_input_mask + [0] * len(target_tokens)
|
||||
|
||||
# Create labels: -100 for input tokens, actual tokens for target
|
||||
# We mask the input portion so loss is only computed on the response
|
||||
labels = [-100] * len(input_ids) + target_tokens
|
||||
|
||||
return {
|
||||
"input_ids": full_input_ids,
|
||||
"labels": labels,
|
||||
"acoustic_input_mask": full_acoustic_mask,
|
||||
"speech": speech,
|
||||
"vae_tok_len": vae_tok_len,
|
||||
}
|
||||
|
||||
|
||||
def get_lora_config(
|
||||
r: int = 16,
|
||||
lora_alpha: int = 32,
|
||||
lora_dropout: float = 0.05,
|
||||
target_modules: Optional[List[str]] = None,
|
||||
) -> LoraConfig:
|
||||
"""
|
||||
Create LoRA configuration for VibeVoice ASR model.
|
||||
|
||||
We apply LoRA to the language model's attention layers and MLP,
|
||||
following common practices for LLM fine-tuning.
|
||||
|
||||
Args:
|
||||
r: LoRA rank
|
||||
lora_alpha: LoRA scaling factor
|
||||
lora_dropout: Dropout for LoRA layers
|
||||
target_modules: List of module names to apply LoRA to
|
||||
|
||||
Returns:
|
||||
LoraConfig object
|
||||
"""
|
||||
if target_modules is None:
|
||||
# Target Qwen2 attention and MLP layers
|
||||
# These are the common targets for language model fine-tuning
|
||||
target_modules = [
|
||||
"q_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
"o_proj",
|
||||
"gate_proj",
|
||||
"up_proj",
|
||||
"down_proj",
|
||||
]
|
||||
|
||||
return LoraConfig(
|
||||
r=r,
|
||||
lora_alpha=lora_alpha,
|
||||
target_modules=target_modules,
|
||||
lora_dropout=lora_dropout,
|
||||
bias="none",
|
||||
task_type=TaskType.CAUSAL_LM,
|
||||
)
|
||||
|
||||
|
||||
def setup_model_for_training(
|
||||
model_path: str,
|
||||
lora_config: LoraConfig,
|
||||
device: str = "cuda",
|
||||
dtype: torch.dtype = torch.bfloat16,
|
||||
gradient_checkpointing: bool = True,
|
||||
) -> Tuple[nn.Module, VibeVoiceASRProcessor]:
|
||||
"""
|
||||
Load and prepare model for LoRA training.
|
||||
|
||||
Args:
|
||||
model_path: Path to pretrained model
|
||||
lora_config: LoRA configuration
|
||||
device: Device to use
|
||||
dtype: Data type for model
|
||||
gradient_checkpointing: Whether to use gradient checkpointing
|
||||
|
||||
Returns:
|
||||
Tuple of (model, processor)
|
||||
"""
|
||||
logger.info(f"Loading model from {model_path}")
|
||||
|
||||
# Load processor
|
||||
processor = VibeVoiceASRProcessor.from_pretrained(
|
||||
model_path,
|
||||
language_model_pretrained_name="Qwen/Qwen2.5-7B"
|
||||
)
|
||||
|
||||
# Load model
|
||||
model = VibeVoiceASRForConditionalGeneration.from_pretrained(
|
||||
model_path,
|
||||
dtype=dtype,
|
||||
device_map=device if device == "auto" else None,
|
||||
attn_implementation="flash_attention_2",
|
||||
trust_remote_code=True,
|
||||
)
|
||||
|
||||
if device != "auto":
|
||||
model = model.to(device)
|
||||
|
||||
# Freeze speech tokenizers (we only want to fine-tune the language model)
|
||||
for name, param in model.named_parameters():
|
||||
if "acoustic_tokenizer" in name or "semantic_tokenizer" in name:
|
||||
param.requires_grad = False
|
||||
logger.debug(f"Frozen: {name}")
|
||||
|
||||
# Apply LoRA
|
||||
logger.info(f"Applying LoRA with config: r={lora_config.r}, alpha={lora_config.lora_alpha}")
|
||||
model = get_peft_model(model, lora_config)
|
||||
|
||||
# Print trainable parameters
|
||||
model.print_trainable_parameters()
|
||||
|
||||
# Enable gradient checkpointing if requested
|
||||
if gradient_checkpointing:
|
||||
model.enable_input_require_grads()
|
||||
model.gradient_checkpointing_enable()
|
||||
|
||||
return model, processor
|
||||
|
||||
|
||||
def train(
|
||||
model_args: ModelArguments,
|
||||
data_args: DataArguments,
|
||||
lora_args: LoraArguments,
|
||||
training_args: TrainingArguments,
|
||||
gradient_checkpointing: bool = True,
|
||||
):
|
||||
"""
|
||||
Main training function for LoRA fine-tuning.
|
||||
|
||||
Args:
|
||||
model_args: Model configuration arguments
|
||||
data_args: Data configuration arguments
|
||||
lora_args: LoRA configuration arguments
|
||||
training_args: HuggingFace TrainingArguments
|
||||
gradient_checkpointing: Whether to use gradient checkpointing
|
||||
"""
|
||||
# Set seed
|
||||
torch.manual_seed(training_args.seed)
|
||||
np.random.seed(training_args.seed)
|
||||
|
||||
# Setup LoRA config
|
||||
lora_config = get_lora_config(
|
||||
r=lora_args.lora_r,
|
||||
lora_alpha=lora_args.lora_alpha,
|
||||
lora_dropout=lora_args.lora_dropout,
|
||||
)
|
||||
|
||||
# Determine device
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
|
||||
# Load model and processor
|
||||
dtype = torch.bfloat16 if device != "cpu" else torch.float32
|
||||
model, processor = setup_model_for_training(
|
||||
model_path=model_args.model_path,
|
||||
lora_config=lora_config,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
gradient_checkpointing=gradient_checkpointing,
|
||||
)
|
||||
|
||||
# Create dataset
|
||||
train_dataset = VibeVoiceASRDataset(
|
||||
data_dir=data_args.data_dir,
|
||||
processor=processor,
|
||||
max_audio_length=data_args.max_audio_length,
|
||||
use_customized_context=data_args.use_customized_context,
|
||||
)
|
||||
|
||||
if len(train_dataset) == 0:
|
||||
logger.error("No training samples found!")
|
||||
return
|
||||
|
||||
# Create data collator
|
||||
data_collator = VibeVoiceASRDataCollator(
|
||||
processor=processor,
|
||||
pad_token_id=processor.pad_id,
|
||||
)
|
||||
|
||||
# Set some sensible defaults for audio training
|
||||
training_args.dataloader_num_workers = 0 # Audio loading can be tricky with multiprocessing
|
||||
training_args.remove_unused_columns = False # Keep all columns
|
||||
|
||||
# Create trainer
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=train_dataset,
|
||||
data_collator=data_collator,
|
||||
)
|
||||
|
||||
# Train
|
||||
logger.info("Starting training...")
|
||||
logger.info(f" Num samples = {len(train_dataset)}")
|
||||
logger.info(f" Num epochs = {training_args.num_train_epochs}")
|
||||
logger.info(f" Batch size = {training_args.per_device_train_batch_size}")
|
||||
logger.info(f" Gradient accumulation steps = {training_args.gradient_accumulation_steps}")
|
||||
total_steps = len(train_dataset) * int(training_args.num_train_epochs) // (
|
||||
training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps
|
||||
)
|
||||
logger.info(f" Total optimization steps = {total_steps}")
|
||||
|
||||
train_result = trainer.train()
|
||||
|
||||
# Save final model
|
||||
logger.info(f"Saving model to {training_args.output_dir}")
|
||||
trainer.save_model(training_args.output_dir)
|
||||
|
||||
# Save training metrics
|
||||
metrics = train_result.metrics
|
||||
trainer.log_metrics("train", metrics)
|
||||
trainer.save_metrics("train", metrics)
|
||||
|
||||
# Save processor config
|
||||
processor.save_pretrained(training_args.output_dir)
|
||||
|
||||
logger.info("Training complete!")
|
||||
|
||||
return model, processor
|
||||
|
||||
|
||||
def main():
|
||||
# Use HfArgumentParser to parse all argument dataclasses
|
||||
parser = HfArgumentParser((ModelArguments, DataArguments, LoraArguments, TrainingArguments))
|
||||
model_args, data_args, lora_args, training_args = parser.parse_args_into_dataclasses()
|
||||
|
||||
# Run training
|
||||
train(
|
||||
model_args=model_args,
|
||||
data_args=data_args,
|
||||
lora_args=lora_args,
|
||||
training_args=training_args,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Executable
+73
@@ -0,0 +1,73 @@
|
||||
{
|
||||
"audio_duration": 351.73333333333335,
|
||||
"audio_path": "0.mp3",
|
||||
"segments": [
|
||||
{
|
||||
"speaker": 0,
|
||||
"text": "Hey everyone, welcome back to Tea Brew. I’m Aidan Host, and, uh, sorry we’re a tad late today. Travel was a bit wild on the roads and our guest came in from near Meeter Street, so, you know, life happens. But we’re here now and buzzing to talk property tech. We’ve had loads of messages from landlords about spiraling energy costs in HMOs and, at the same time, a bunch of questions on streamlining tenant recruitment. So today we’re kinda merging both worlds: how a smart heating setup can cut bills, and how a software assistant like Rent Byte can take the grind out of advertising, onboarding, and maintenance reporting. And sitting with me is Sayid Guest, who’s, uh, a landlord turned gadget builder—haha—ready to demystify the thermostatic stuff that tenants can actually live with.",
|
||||
"start": 0.0,
|
||||
"end": 38.68
|
||||
},
|
||||
{
|
||||
"speaker": 1,
|
||||
"text": "Thanks, Aidan Host, and, yeah, hi everyone. I’m Sayid Guest, and this topic is personal for me. I started out managing a handful of HMOs, and—um—the classic scene was radiators roaring away with nobody home, windows cracked wide open. You pay for the heat and watch it pour right out. Not fun. There’s also that line we can’t cross: you can’t lock tenants out of controls. So I began tinkering, looking for a way to respect tenant comfort while, uh, controlling the waste. The device I built evolved from a scrappy prototype to a solid system, and the surprising bit was how immediate the savings were. Like, right from the first month, bills were trending down hard, and I thought, wow, landlords need something like this across the board.",
|
||||
"start": 38.75,
|
||||
"end": 77.88
|
||||
},
|
||||
{
|
||||
"speaker": 0,
|
||||
"text": "Exactly. And, actually, on the software side, we’ve seen a similar DIY-to-pro evolution. Rent Byte started because landlords were fed up with juggling spreadsheets, emails, and random listings. It’s, um, designed by landlords with tenants in mind—trust and transparency baked in. With Rent Byte, you can push your property ads out fast, track leads, run checks, and glide folks through the onboarding without the headache. Then, when people move in, it doesn’t stop; you’ve got in-app maintenance reporting, job tracking, and clear timelines, so tenants don’t feel, you know, ignored. Tea Brew listeners keep telling us the pain isn’t just finding residents, it’s keeping the whole machine humming. And if you pair that workflow with smart heating controls, you’re hitting cost, comfort, and communication all in one go.",
|
||||
"start": 77.92,
|
||||
"end": 115.18
|
||||
},
|
||||
{
|
||||
"speaker": 2,
|
||||
"text": "Hold on, can I jump in? So, um, I manage two HMOs over by Meeter Street, and the energy bills last winter were brutal. Everyone online was shouting tips—weather compensation, schedule tweaks, the whole shebang—but tenants still cranked it up when they felt chilly. We can’t, like, lock the thermostat, right? So how does your approach, Sayid Guest, keep the system fair? Tenants get access, but landlords don’t get burned—haha, terrible pun. Does it do occupancy sensing, or is it just a timer? And how do you stop the classic boost button from becoming a permanent ‘on’?",
|
||||
"start": 115.19,
|
||||
"end": 147.56
|
||||
},
|
||||
{
|
||||
"speaker": 1,
|
||||
"text": "Great questions. So it’s a combo. Think of it as a comfort-first schedule with protections. You set reasonable heating windows—morning and evening, say—and tenants can press a boost for extra time. But the boost is capped and resets, so it won’t, uh, run the boiler all day. For empty rooms and the, you know, window-wide-open scenario, if you add sensors, the system detects rapid drops or no movement and tapers the heat until conditions make sense. It’s not about denying warmth; it’s about stopping waste that literally no one benefits from. With HMOs you need communal logic—landings and kitchens matter—so multi-zone control helps keep spaces balanced. And landlords, like me, get analytics: you can see where energy is leaking and fine-tune settings. When I first tested it, I saw 30–50% reductions. That’s not a promise for every building—each setup is its own puzzle—but the pattern has been, um, consistently strong.",
|
||||
"start": 147.56,
|
||||
"end": 193.57
|
||||
},
|
||||
{
|
||||
"speaker": 0,
|
||||
"text": "Yeah, that resonates. I walked into a shared house once—near Meeter Street—and the thermostat was at 29, like sauna levels, with the window propped open. The tenants weren’t being malicious, they were just coping: drafty room, quick fix, crank the dial. So, as Tea Brew keeps saying, we need systems that, uh, balance tenant agency with sensible guardrails. And the nice thing with Rent Byte is that it keeps the conversation flowing. Tenants can raise an issue through maintenance reporting, and as soon as the ticket is created, the timeline starts. If it’s a cold spot or a radiator fault, you’ve got the history at your fingertips. That way, you don’t blame behavior when it’s actually a hardware problem.",
|
||||
"start": 193.57,
|
||||
"end": 225.73
|
||||
},
|
||||
{
|
||||
"speaker": 2,
|
||||
"text": "Okay, so picture this: I advertise a new room, get flooded with inquiries, then I’m buried in emails. The software side—like Rent Byte—would pull those leads into a pipeline, right? And if someone mentions the room feels chilly during a viewing, I could, uh, flag that straight away. Here’s the kicker: can your device talk to the platform? Like, if there’s a temp anomaly or a sensor alert, could it auto-create a maintenance ticket so I don’t miss it? And, sorry, I’m thinking out loud here, but could Rent Byte show those energy graphs inside the tenant portal without freaking people out—more like, you know, helpful nudges than lecturing?",
|
||||
"start": 225.74,
|
||||
"end": 258.74
|
||||
},
|
||||
{
|
||||
"speaker": 1,
|
||||
"text": "Haha, I love the way you think. Yes, integration is the future. We’ve built an API so platforms like Rent Byte can pull summary data—no one needs to drown in charts, just the, um, useful stuff. For example, you can surface a gentle insight: “Heating is already scheduled; boost is available for 30 minutes.” Tenants see options, not rules. If a sensor flags a stuck valve or a window open for ages, Rent Byte can spin up a maintenance ticket, assign it, and track the fix. And because Tea Brew listeners keep asking about transparency, we’ve found tenants appreciate seeing that there’s a fair schedule in place. By the way, thanks again for the invite, Aidan Host; getting this across without jargon is half the battle.",
|
||||
"start": 258.74,
|
||||
"end": 292.52
|
||||
},
|
||||
{
|
||||
"speaker": 0,
|
||||
"text": "Totally. And, uh, we’ve noticed another benefit: advertising feels cleaner when you can tell prospects, right up front, that the home uses sensible heating with tenant-controlled boosts. It sounds small, but it sets expectations and, you know, avoids future friction. On the admin side, Rent Byte keeps everything documented—from viewing notes to audit trails—so if someone on Meeter Street says their radiator’s been weird for weeks, you can point to the timeline and fix history quickly. Um, we’ve had a bunch of landlords message Tea Brew after implementing this kind of setup, saying the combo of software plus smart heating saved money and, honestly, reduced arguments. That’s the vibe we want.",
|
||||
"start": 292.53,
|
||||
"end": 323.63
|
||||
},
|
||||
{
|
||||
"speaker": 2,
|
||||
"text": "Same here. To wrap, uh, I’m thinking: start with clear, humane policies, pair them with tech that respects tenant comfort, and keep the communication channel open. Aidan Host, if folks want to try Rent Byte, what’s the first step? And, Sayid Guest, for the device, is there like a starter kit guide so, um, non-tech landlords don’t panic? Maybe put links under the episode—sorry—under Tea Brew show notes. I’ve got two more rooms to fill near Meeter Street, and it’d be great to kick this off before the cold snaps hit again.",
|
||||
"start": 323.64,
|
||||
"end": 351.73
|
||||
}
|
||||
],
|
||||
"customized_context": [
|
||||
"Tea Brew",
|
||||
"Aiden Host",
|
||||
"Saeed Guest",
|
||||
"Rent Byte",
|
||||
"The property is located near Meter Street."
|
||||
]
|
||||
}
|
||||
Executable
BIN
Binary file not shown.
Executable
+79
@@ -0,0 +1,79 @@
|
||||
{
|
||||
"audio_duration": 328.26666666666665,
|
||||
"audio_path": "1.mp3",
|
||||
"segments": [
|
||||
{
|
||||
"speaker": 0,
|
||||
"text": "Welcome back to our Youth Month special. Um, before we dive in, Tandi, you ready? We’re honoring young folks who, like, you know, shook the ground. The day itself marks those anti–language policy protests in the 70s—students across the country, campuses, townships—standing up. And today, we’re asking you all on the WhatsApp line to share who inspired you: a teacher, a cousin, someone you admire. Also, we’ll talk about campaigns like Crown Wrights, because identity’s not cosmetic; it’s, uh, core.",
|
||||
"start": 0.0,
|
||||
"end": 33.15
|
||||
},
|
||||
{
|
||||
"speaker": 1,
|
||||
"text": "Yeah, yeah, I’m totally in, haha. Thanks, Leila. So, um, I’ve been thinking about how the current youth still carry heavy stuff—joblessness, violence against women, and, heartbreakingly, queer kids being targeted. It’s, like, gutting. Yet they keep going. I mean, this is Tandi speaking from the heart here. The person I want to celebrate is Zahra. She stood up at Coyl High when policies tried to tame her natural hair, and she, uh, didn’t flinch. She even wrote a children’s story as part of Crown Wrights, to help little ones see their coils as power instead of problem.",
|
||||
"start": 33.24,
|
||||
"end": 71.33
|
||||
},
|
||||
{
|
||||
"speaker": 2,
|
||||
"text": "Wow, okay, that hits hard. Zahra at Coyl High—I remember, like, seeing clips where she just, you know, stood there calmly while adults were telling her to “fix” herself. It gave me chills. Identity isn’t some minor detail; it’s a major, uh, principle—wait, I always mix that with principal, haha. Anyway, the bravery at Crown Wrights events has ripple effects. And Leila, you’ve talked about how hair, especially coily textures, can be policed as a way to, um, shrink someone’s confidence.",
|
||||
"start": 71.52,
|
||||
"end": 102.9
|
||||
},
|
||||
{
|
||||
"speaker": 0,
|
||||
"text": "Exactly! And thanks, Tandi, for, like, naming the hard stuff. The way that activist reframed “acceptable” appearance shows young people don’t need permission to be whole. When you own your look, you walk into boardrooms you never imagined. You, uh, sit at tables, you speak up. It’s the same energy that wins pageants and policies—like, a confidence that shifts rooms. And for everyone listening, send a voice note about your own champion and share stories about reclaiming a “crown.”",
|
||||
"start": 102.9,
|
||||
"end": 132.14
|
||||
},
|
||||
{
|
||||
"speaker": 1,
|
||||
"text": "Hold on—just to paint the scene. At Coyl High, Zahra didn’t wait for a senior to intervene; she, um, chose the moment. No hesitation. She knew the risk: future opportunities, social backlash, being labeled “difficult.” And she still stood up. For me, Tandi, that shows the lesson the old protests tried to teach—use your voice now, because silence, like, steals time. And you always say confidence is contagious; when one girl lifts her chin, dozens follow.",
|
||||
"start": 132.17,
|
||||
"end": 161.49
|
||||
},
|
||||
{
|
||||
"speaker": 2,
|
||||
"text": "Yeah, yes—exactly. And, mm, the thing that gets me is the dignity piece. People act like hair is trivial, but for Black girls, policing curls is a way to control presence. That campaign reframes it: from “problem” to “pride.” When that flip happens, you start applying for roles you thought were off-limits, you choose your course, you don’t break—uh, brake—just because someone says you don’t fit. And, you mentioned queer youth; the intersections matter. That stand speaks beyond curls, into, like, the right to be fully yourself.",
|
||||
"start": 161.52,
|
||||
"end": 196.53
|
||||
},
|
||||
{
|
||||
"speaker": 0,
|
||||
"text": "Speaking of which, Leila, could you share a listener shout-out? We got a bunch of WhatsApps about teachers, neighbors, and, uh, nurses who kept kids afloat. Also there’s a note praising the book in the Crown Wrights series—apparently the illustrations made a little one feel seen at school.",
|
||||
"start": 196.91,
|
||||
"end": 212.93
|
||||
},
|
||||
{
|
||||
"speaker": 2,
|
||||
"text": "This is Leila—oh yeah, totally. So, um, there’s a message from a parent who says their child used to hide her coils under a cap, and after reading that Crown Wrights story she walked into class with no fear. And another listener’s like, “my teacher changed my life,” which—haha—yes. The past protests weren’t just about language policy; they set a template: organize, insist, repeat. Honestly, Tandi, I feel like honoring Zahra honors every student who refuses to be edited at Coyl High-type schools, you know?",
|
||||
"start": 213.59,
|
||||
"end": 245.25
|
||||
},
|
||||
{
|
||||
"speaker": 1,
|
||||
"text": "Exactly! And, um, can I just say: elders sometimes tell us to wait for leaders to fix things. But the lesson, from then till now, is don’t outsource your voice. The moment you speak, the course of the moment changes—like, the crowd pivots from idle to action. And for anyone feeling alone, tag us and the campaign; you’ll find community fast.",
|
||||
"start": 245.25,
|
||||
"end": 266.64
|
||||
},
|
||||
{
|
||||
"speaker": 0,
|
||||
"text": "Right, and, wow, we’ve barely scratched the surface. I want to circle back because someone asked if honoring hair is, um, frivolous compared to, say, jobs or safety. My answer: it’s linked. Coyl High-style rules are part of a bigger system that decides who is welcome. Undo that, and you expand opportunity. And if you’re looking for names, Leila suggested a list: local mentors, teacher heroes, youth organizers. That point about resilience—phew—needed.",
|
||||
"start": 266.64,
|
||||
"end": 297.57
|
||||
},
|
||||
{
|
||||
"speaker": 2,
|
||||
"text": "Mhm, and one more thought, then we’ll wrap. Zahra didn’t become a symbol because she wanted fame; she became one because she refused to break. That stubborn joy? It’s, like, fuel. I’m Leila, and I’m grateful we still celebrate that day in June as a reminder to keep going. To every listener—send your piece, um, peace—haha—on the line. And thanks, Tandi, for bringing this story, and thanks to campaigns like Crown Wrights for keeping the flame on.",
|
||||
"start": 297.78,
|
||||
"end": 328.27
|
||||
}
|
||||
],
|
||||
"customized_context": [
|
||||
"Thandie",
|
||||
"Leila",
|
||||
"Zara",
|
||||
"The story takes place at Coyle High school.",
|
||||
"Crown Rites is a campaign for hair rights."
|
||||
]
|
||||
}
|
||||
Executable
BIN
Binary file not shown.
@@ -0,0 +1,56 @@
|
||||
[build-system]
|
||||
requires = ["setuptools>=61.0"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "vibevoice"
|
||||
version = "1.0.0"
|
||||
authors = [
|
||||
{ name="vibevoice team", email="VibeVoice@microsoft.com" },
|
||||
]
|
||||
description = "Open-Source Frontier Voice AI."
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
classifiers = [
|
||||
"Programming Language :: Python :: 3",
|
||||
# "License :: OSI Approved :: MIT License",
|
||||
"Operating System :: OS Independent",
|
||||
]
|
||||
dependencies = [
|
||||
"torch",
|
||||
"transformers>=4.51.3,<5.0.0",
|
||||
"accelerate",
|
||||
"llvmlite>=0.40.0",
|
||||
"numba>=0.57.0",
|
||||
"diffusers",
|
||||
"tqdm",
|
||||
"numpy",
|
||||
"scipy",
|
||||
"librosa",
|
||||
"ml-collections",
|
||||
"absl-py",
|
||||
"gradio",
|
||||
"av",
|
||||
"aiortc",
|
||||
"uvicorn[standard]",
|
||||
"fastapi",
|
||||
"pydub",
|
||||
"requests",
|
||||
]
|
||||
|
||||
|
||||
[project.optional-dependencies]
|
||||
streamingtts = [
|
||||
"transformers==4.51.3",
|
||||
]
|
||||
|
||||
[project.entry-points."vllm.general_plugins"]
|
||||
vibevoice = "vllm_plugin:register_vibevoice"
|
||||
|
||||
[project.urls]
|
||||
"Homepage" = "https://github.com/microsoft/VibeVoice"
|
||||
"Bug Tracker" = "https://github.com/microsoft/VibeVoice/issues"
|
||||
|
||||
[tool.setuptools.packages.find]
|
||||
where = ["."]
|
||||
include = ["vibevoice*", "vllm_plugin*"]
|
||||
@@ -0,0 +1,16 @@
|
||||
# vibevoice/__init__.py
|
||||
from vibevoice.modular import (
|
||||
VibeVoiceStreamingForConditionalGenerationInference,
|
||||
VibeVoiceStreamingConfig,
|
||||
)
|
||||
from vibevoice.processor import (
|
||||
VibeVoiceStreamingProcessor,
|
||||
VibeVoiceTokenizerProcessor,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceStreamingForConditionalGenerationInference",
|
||||
"VibeVoiceStreamingConfig",
|
||||
"VibeVoiceStreamingProcessor",
|
||||
"VibeVoiceTokenizerProcessor",
|
||||
]
|
||||
@@ -0,0 +1,112 @@
|
||||
{
|
||||
"_attn_implementation_autoset": true,
|
||||
"acoustic_vae_dim": 64,
|
||||
"acoustic_tokenizer_config": {
|
||||
"causal": true,
|
||||
"channels": 1,
|
||||
"conv_bias": true,
|
||||
"conv_norm": "none",
|
||||
"corpus_normalize": 0.0,
|
||||
"decoder_depths": null,
|
||||
"decoder_n_filters": 32,
|
||||
"decoder_ratios": [
|
||||
8,
|
||||
5,
|
||||
5,
|
||||
4,
|
||||
2,
|
||||
2
|
||||
],
|
||||
"disable_last_norm": true,
|
||||
"encoder_depths": "3-3-3-3-3-3-8",
|
||||
"encoder_n_filters": 32,
|
||||
"encoder_ratios": [
|
||||
8,
|
||||
5,
|
||||
5,
|
||||
4,
|
||||
2,
|
||||
2
|
||||
],
|
||||
"fix_std": 0.5,
|
||||
"layer_scale_init_value": 1e-06,
|
||||
"layernorm": "RMSNorm",
|
||||
"layernorm_elementwise_affine": true,
|
||||
"layernorm_eps": 1e-05,
|
||||
"mixer_layer": "depthwise_conv",
|
||||
"model_type": "vibepod_acoustic_tokenizer",
|
||||
"pad_mode": "constant",
|
||||
"std_dist_type": "gaussian",
|
||||
"vae_dim": 64,
|
||||
"weight_init_value": 0.01
|
||||
},
|
||||
"decoder_config": {
|
||||
"attention_dropout": 0.0,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 1536,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 8960,
|
||||
"max_position_embeddings": 65536,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 12,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 2,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
},
|
||||
"diffusion_head_config": {
|
||||
"ddpm_batch_mul": 4,
|
||||
"ddpm_beta_schedule": "cosine",
|
||||
"ddpm_num_inference_steps": 20,
|
||||
"ddpm_num_steps": 1000,
|
||||
"diffusion_type": "ddpm",
|
||||
"head_ffn_ratio": 3.0,
|
||||
"head_layers": 4,
|
||||
"hidden_size": 1536,
|
||||
"latent_size": 64,
|
||||
"model_type": "vibepod_diffusion_head",
|
||||
"prediction_type": "v_prediction",
|
||||
"rms_norm_eps": 1e-05,
|
||||
"speech_vae_dim": 64
|
||||
},
|
||||
"model_type": "vibepod",
|
||||
"semantic_tokenizer_config": {
|
||||
"causal": true,
|
||||
"channels": 1,
|
||||
"conv_bias": true,
|
||||
"conv_norm": "none",
|
||||
"corpus_normalize": 0.0,
|
||||
"disable_last_norm": true,
|
||||
"encoder_depths": "3-3-3-3-3-3-8",
|
||||
"encoder_n_filters": 32,
|
||||
"encoder_ratios": [
|
||||
8,
|
||||
5,
|
||||
5,
|
||||
4,
|
||||
2,
|
||||
2
|
||||
],
|
||||
"fix_std": 0,
|
||||
"layer_scale_init_value": 1e-06,
|
||||
"layernorm": "RMSNorm",
|
||||
"layernorm_elementwise_affine": true,
|
||||
"layernorm_eps": 1e-05,
|
||||
"mixer_layer": "depthwise_conv",
|
||||
"model_type": "vibepod_semantic_tokenizer",
|
||||
"pad_mode": "constant",
|
||||
"std_dist_type": "none",
|
||||
"vae_dim": 128,
|
||||
"weight_init_value": 0.01
|
||||
},
|
||||
"semantic_vae_dim": 128,
|
||||
"torch_dtype": "bfloat16"
|
||||
}
|
||||
@@ -0,0 +1,113 @@
|
||||
{
|
||||
"_attn_implementation_autoset": true,
|
||||
"acoustic_vae_dim": 64,
|
||||
"acoustic_tokenizer_config": {
|
||||
"causal": true,
|
||||
"channels": 1,
|
||||
"conv_bias": true,
|
||||
"conv_norm": "none",
|
||||
"corpus_normalize": 0.0,
|
||||
"decoder_depths": null,
|
||||
"decoder_n_filters": 32,
|
||||
"decoder_ratios": [
|
||||
8,
|
||||
5,
|
||||
5,
|
||||
4,
|
||||
2,
|
||||
2
|
||||
],
|
||||
"disable_last_norm": true,
|
||||
"encoder_depths": "3-3-3-3-3-3-8",
|
||||
"encoder_n_filters": 32,
|
||||
"encoder_ratios": [
|
||||
8,
|
||||
5,
|
||||
5,
|
||||
4,
|
||||
2,
|
||||
2
|
||||
],
|
||||
"fix_std": 0.5,
|
||||
"layer_scale_init_value": 1e-06,
|
||||
"layernorm": "RMSNorm",
|
||||
"layernorm_elementwise_affine": true,
|
||||
"layernorm_eps": 1e-05,
|
||||
"mixer_layer": "depthwise_conv",
|
||||
"model_type": "vibepod_acoustic_tokenizer",
|
||||
"pad_mode": "constant",
|
||||
"std_dist_type": "gaussian",
|
||||
"vae_dim": 64,
|
||||
"weight_init_value": 0.01
|
||||
},
|
||||
"decoder_config": {
|
||||
"attention_dropout": 0.0,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3584,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 18944,
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.40.1",
|
||||
"use_cache": true,
|
||||
"use_mrope": false,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 152064
|
||||
},
|
||||
"diffusion_head_config": {
|
||||
"ddpm_batch_mul": 4,
|
||||
"ddpm_beta_schedule": "cosine",
|
||||
"ddpm_num_inference_steps": 20,
|
||||
"ddpm_num_steps": 1000,
|
||||
"diffusion_type": "ddpm",
|
||||
"head_ffn_ratio": 3.0,
|
||||
"head_layers": 4,
|
||||
"hidden_size": 3584,
|
||||
"latent_size": 64,
|
||||
"model_type": "vibepod_diffusion_head",
|
||||
"prediction_type": "v_prediction",
|
||||
"rms_norm_eps": 1e-05,
|
||||
"speech_vae_dim": 64
|
||||
},
|
||||
"model_type": "vibepod",
|
||||
"semantic_tokenizer_config": {
|
||||
"causal": true,
|
||||
"channels": 1,
|
||||
"conv_bias": true,
|
||||
"conv_norm": "none",
|
||||
"corpus_normalize": 0.0,
|
||||
"disable_last_norm": true,
|
||||
"encoder_depths": "3-3-3-3-3-3-8",
|
||||
"encoder_n_filters": 32,
|
||||
"encoder_ratios": [
|
||||
8,
|
||||
5,
|
||||
5,
|
||||
4,
|
||||
2,
|
||||
2
|
||||
],
|
||||
"fix_std": 0,
|
||||
"layer_scale_init_value": 1e-06,
|
||||
"layernorm": "RMSNorm",
|
||||
"layernorm_elementwise_affine": true,
|
||||
"layernorm_eps": 1e-05,
|
||||
"mixer_layer": "depthwise_conv",
|
||||
"model_type": "vibepod_semantic_tokenizer",
|
||||
"pad_mode": "constant",
|
||||
"std_dist_type": "none",
|
||||
"vae_dim": 128,
|
||||
"weight_init_value": 0.01
|
||||
},
|
||||
"semantic_vae_dim": 128,
|
||||
"torch_dtype": "bfloat16"
|
||||
}
|
||||
@@ -0,0 +1,14 @@
|
||||
# vibevoice/modular/__init__.py
|
||||
from .modeling_vibevoice_streaming_inference import VibeVoiceStreamingForConditionalGenerationInference
|
||||
from .configuration_vibevoice_streaming import VibeVoiceStreamingConfig
|
||||
from .modeling_vibevoice_streaming import VibeVoiceStreamingModel, VibeVoiceStreamingPreTrainedModel
|
||||
from .streamer import AudioStreamer, AsyncAudioStreamer
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceStreamingForConditionalGenerationInference",
|
||||
"VibeVoiceStreamingConfig",
|
||||
"VibeVoiceStreamingModel",
|
||||
"VibeVoiceStreamingPreTrainedModel",
|
||||
"AudioStreamer",
|
||||
"AsyncAudioStreamer",
|
||||
]
|
||||
@@ -0,0 +1,406 @@
|
||||
""" VibeVoice_AcousticTokenizer model configuration"""
|
||||
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import torch
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import logging
|
||||
|
||||
from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
def _convert_dtype_to_string(config_dict: dict) -> dict:
|
||||
"""
|
||||
Convert torch.dtype objects to their string representation for JSON serialization.
|
||||
|
||||
This fixes the "Object of type dtype is not JSON serializable" error that occurs
|
||||
when transformers tries to log/serialize the config with torch_dtype as a torch.dtype object.
|
||||
|
||||
See: https://github.com/microsoft/VibeVoice/issues/199
|
||||
"""
|
||||
if "torch_dtype" in config_dict and config_dict["torch_dtype"] is not None:
|
||||
dtype = config_dict["torch_dtype"]
|
||||
if isinstance(dtype, torch.dtype):
|
||||
# Convert torch.dtype to string (e.g., torch.bfloat16 -> "bfloat16")
|
||||
config_dict["torch_dtype"] = str(dtype).replace("torch.", "")
|
||||
return config_dict
|
||||
|
||||
|
||||
class VibeVoiceAcousticTokenizerConfig(PretrainedConfig):
|
||||
model_type = "vibevoice_acoustic_tokenizer"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
channels: int = 1,
|
||||
corpus_normalize: float = 0.0,
|
||||
causal: bool = True,
|
||||
vae_dim: int = 64,
|
||||
fix_std: float = 0.5,
|
||||
std_dist_type: str = 'gaussian',
|
||||
# common
|
||||
mixer_layer: str = 'depthwise_conv',
|
||||
conv_norm: str = 'none',
|
||||
pad_mode: str = 'constant',
|
||||
disable_last_norm: bool = True,
|
||||
layernorm: str = 'RMSNorm',
|
||||
layernorm_eps: float = 1e-5,
|
||||
layernorm_elementwise_affine: bool = True,
|
||||
conv_bias: bool = True,
|
||||
layer_scale_init_value: float = 1e-6,
|
||||
weight_init_value: float = 1e-2,
|
||||
# encoder specific
|
||||
encoder_n_filters: int = 32,
|
||||
encoder_ratios: Optional[List[int]] = [8,5,5,4,2,2],
|
||||
encoder_depths: str = "3-3-3-3-3-3-8",
|
||||
# decoder specific
|
||||
decoder_n_filters: int = 32,
|
||||
decoder_ratios: Optional[List[int]] = None, # if None, same as encoder
|
||||
decoder_depths: Optional[str] = None,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.channels = channels
|
||||
self.corpus_normalize = corpus_normalize
|
||||
self.causal = causal
|
||||
self.vae_dim = vae_dim
|
||||
self.fix_std = fix_std
|
||||
self.std_dist_type = std_dist_type
|
||||
|
||||
# common parameters
|
||||
self.conv_norm = conv_norm
|
||||
self.pad_mode = pad_mode
|
||||
self.layernorm_eps = layernorm_eps
|
||||
self.disable_last_norm = disable_last_norm
|
||||
self.layernorm = layernorm
|
||||
self.layernorm_elementwise_affine = layernorm_elementwise_affine
|
||||
self.conv_bias = conv_bias
|
||||
self.layer_scale_init_value = layer_scale_init_value
|
||||
self.weight_init_value = weight_init_value
|
||||
self.mixer_layer = mixer_layer
|
||||
|
||||
# encoder specific parameters
|
||||
self.encoder_n_filters = encoder_n_filters
|
||||
self.encoder_ratios = encoder_ratios
|
||||
self.encoder_depths = encoder_depths
|
||||
|
||||
# decoder specific parameters
|
||||
self.decoder_ratios = decoder_ratios if decoder_ratios is not None else encoder_ratios
|
||||
self.decoder_n_filters = decoder_n_filters
|
||||
self.decoder_depths = decoder_depths
|
||||
|
||||
|
||||
class VibeVoiceSemanticTokenizerConfig(PretrainedConfig):
|
||||
model_type = "vibevoice_semantic_tokenizer"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
channels: int = 1,
|
||||
corpus_normalize: float = 0.0,
|
||||
causal: bool = True,
|
||||
vae_dim: int = 64,
|
||||
fix_std: float = 0,
|
||||
std_dist_type: str = 'none',
|
||||
# common
|
||||
mixer_layer: str = 'depthwise_conv',
|
||||
conv_norm: str = 'none',
|
||||
pad_mode: str = 'constant',
|
||||
disable_last_norm: bool = True,
|
||||
layernorm: str = 'RMSNorm',
|
||||
layernorm_eps: float = 1e-5,
|
||||
layernorm_elementwise_affine: bool = True,
|
||||
conv_bias: bool = True,
|
||||
layer_scale_init_value: float = 1e-6,
|
||||
weight_init_value: float = 1e-2,
|
||||
# encoder specific
|
||||
encoder_n_filters: int = 32,
|
||||
encoder_ratios: Optional[List[int]] = [8,5,5,4,2,2],
|
||||
encoder_depths: str = "3-3-3-3-3-3-8",
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.channels = channels
|
||||
self.corpus_normalize = corpus_normalize
|
||||
self.causal = causal
|
||||
self.vae_dim = vae_dim
|
||||
self.fix_std = fix_std
|
||||
self.std_dist_type = std_dist_type
|
||||
|
||||
# common parameters
|
||||
self.conv_norm = conv_norm
|
||||
self.pad_mode = pad_mode
|
||||
self.layernorm_eps = layernorm_eps
|
||||
self.disable_last_norm = disable_last_norm
|
||||
self.layernorm = layernorm
|
||||
self.layernorm_elementwise_affine = layernorm_elementwise_affine
|
||||
self.conv_bias = conv_bias
|
||||
self.layer_scale_init_value = layer_scale_init_value
|
||||
self.weight_init_value = weight_init_value
|
||||
self.mixer_layer = mixer_layer
|
||||
|
||||
# encoder specific parameters
|
||||
self.encoder_n_filters = encoder_n_filters
|
||||
self.encoder_ratios = encoder_ratios
|
||||
self.encoder_depths = encoder_depths
|
||||
|
||||
|
||||
class VibeVoiceDiffusionHeadConfig(PretrainedConfig):
|
||||
model_type = "vibevoice_diffusion_head"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size=768,
|
||||
head_layers=4,
|
||||
head_ffn_ratio=3.0,
|
||||
rms_norm_eps=1e-5,
|
||||
latent_size=64,
|
||||
speech_vae_dim=None,
|
||||
prediction_type="v_prediction",
|
||||
diffusion_type="ddpm",
|
||||
ddpm_num_steps=1000,
|
||||
ddpm_num_inference_steps=20,
|
||||
ddpm_beta_schedule="cosine",
|
||||
ddpm_batch_mul=4,
|
||||
**kwargs
|
||||
):
|
||||
self.hidden_size = hidden_size
|
||||
self.head_layers = head_layers
|
||||
self.head_ffn_ratio = head_ffn_ratio
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.latent_size = latent_size
|
||||
self.speech_vae_dim = speech_vae_dim
|
||||
self.prediction_type = prediction_type
|
||||
self.diffusion_type = diffusion_type
|
||||
self.ddpm_num_steps = ddpm_num_steps
|
||||
self.ddpm_num_inference_steps = ddpm_num_inference_steps
|
||||
self.ddpm_beta_schedule = ddpm_beta_schedule
|
||||
self.ddpm_batch_mul = ddpm_batch_mul
|
||||
|
||||
super().__init__(**kwargs)
|
||||
|
||||
class VibeVoiceConfig(PretrainedConfig):
|
||||
model_type = "vibevoice"
|
||||
is_composition = True
|
||||
sub_configs = {
|
||||
"acoustic_tokenizer_config": VibeVoiceAcousticTokenizerConfig,
|
||||
"semantic_tokenizer_config": VibeVoiceSemanticTokenizerConfig,
|
||||
"decoder_config": Qwen2Config,
|
||||
"diffusion_head_config": VibeVoiceDiffusionHeadConfig,
|
||||
}
|
||||
# keys_to_ignore_at_inference = ["past_key_values"]
|
||||
# Default tensor parallel plan for base model `Qwen2`
|
||||
base_model_tp_plan = {
|
||||
"layers.*.self_attn.q_proj": "colwise",
|
||||
"layers.*.self_attn.k_proj": "colwise",
|
||||
"layers.*.self_attn.v_proj": "colwise",
|
||||
"layers.*.self_attn.o_proj": "rowwise",
|
||||
"layers.*.mlp.gate_proj": "colwise",
|
||||
"layers.*.mlp.up_proj": "colwise",
|
||||
"layers.*.mlp.down_proj": "rowwise",
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
acoustic_tokenizer_config=None,
|
||||
semantic_tokenizer_config=None,
|
||||
decoder_config=None,
|
||||
diffusion_head_config=None,
|
||||
**kwargs
|
||||
):
|
||||
|
||||
# kwargs["_attn_implementation"] = "flash_attention_2"
|
||||
kwargs["_attn_implementation_autoset"] = False
|
||||
|
||||
if acoustic_tokenizer_config is None:
|
||||
self.acoustic_tokenizer_config = self.sub_configs["acoustic_tokenizer_config"]()
|
||||
elif isinstance(acoustic_tokenizer_config, dict):
|
||||
acoustic_tokenizer_config["model_type"] = "vibevoice_acoustic_tokenizer"
|
||||
self.acoustic_tokenizer_config = self.sub_configs["acoustic_tokenizer_config"](**acoustic_tokenizer_config)
|
||||
elif isinstance(acoustic_tokenizer_config, VibeVoiceAcousticTokenizerConfig):
|
||||
# If an instance of the config class is provided
|
||||
self.acoustic_tokenizer_config = acoustic_tokenizer_config
|
||||
|
||||
if semantic_tokenizer_config is None:
|
||||
self.semantic_tokenizer_config = self.sub_configs["semantic_tokenizer_config"]()
|
||||
elif isinstance(semantic_tokenizer_config, dict):
|
||||
semantic_tokenizer_config["model_type"] = "vibevoice_semantic_tokenizer"
|
||||
self.semantic_tokenizer_config = self.sub_configs["semantic_tokenizer_config"](**semantic_tokenizer_config)
|
||||
elif isinstance(semantic_tokenizer_config, VibeVoiceSemanticTokenizerConfig):
|
||||
# If an instance of the config class is provided
|
||||
self.semantic_tokenizer_config = semantic_tokenizer_config
|
||||
|
||||
if decoder_config is None:
|
||||
self.decoder_config = self.sub_configs["decoder_config"]()
|
||||
elif isinstance(decoder_config, dict):
|
||||
# If a dictionary is provided, instantiate the config class with it
|
||||
# self.decoder_config = self.sub_configs["decoder_config"](**decoder_config)
|
||||
if decoder_config.get("model_type", '') == "qwen2":
|
||||
self.decoder_config = Qwen2Config(**decoder_config)
|
||||
else:
|
||||
raise ValueError(f"Unsupported decoder model type: {decoder_config.get('model_type', '')}")
|
||||
elif isinstance(decoder_config, (Qwen2Config,)):
|
||||
# If an instance of the config class is provided
|
||||
self.decoder_config = decoder_config
|
||||
|
||||
if diffusion_head_config is None:
|
||||
self.diffusion_head_config = self.sub_configs["diffusion_head_config"]()
|
||||
elif isinstance(diffusion_head_config, dict):
|
||||
diffusion_head_config["model_type"] = "vibevoice_diffusion_head"
|
||||
self.diffusion_head_config = self.sub_configs["diffusion_head_config"](**diffusion_head_config)
|
||||
elif isinstance(diffusion_head_config, VibeVoiceDiffusionHeadConfig):
|
||||
# If an instance of the config class is provided
|
||||
self.diffusion_head_config = diffusion_head_config
|
||||
|
||||
# other parameters
|
||||
self.acoustic_vae_dim = getattr(self.acoustic_tokenizer_config, 'vae_dim', 64)
|
||||
self.semantic_vae_dim = getattr(self.semantic_tokenizer_config, 'vae_dim', 128)
|
||||
|
||||
super().__init__(**kwargs)
|
||||
|
||||
def get_text_config(self, decoder=False):
|
||||
"""
|
||||
Returns the text config for this model.
|
||||
|
||||
vLLM uses this method to get the text configuration from multimodal models.
|
||||
This allows vLLM to correctly determine hidden_size, num_attention_heads,
|
||||
and other properties needed for memory profiling and model execution.
|
||||
|
||||
For VibeVoice, the "text config" is the decoder_config (Qwen2Config).
|
||||
|
||||
Args:
|
||||
decoder: If True, return the decoder config (for encoder-decoder models).
|
||||
For VibeVoice, this is always the decoder_config.
|
||||
|
||||
Returns:
|
||||
The decoder configuration (Qwen2Config) which contains hidden_size, etc.
|
||||
"""
|
||||
return self.decoder_config
|
||||
|
||||
def to_dict(self):
|
||||
"""
|
||||
Override to_dict to handle torch.dtype serialization.
|
||||
|
||||
Fixes: https://github.com/microsoft/VibeVoice/issues/199
|
||||
"""
|
||||
output = super().to_dict()
|
||||
return _convert_dtype_to_string(output)
|
||||
|
||||
class VibeVoiceASRConfig(PretrainedConfig):
|
||||
model_type = "vibevoice"
|
||||
is_composition = True
|
||||
sub_configs = {
|
||||
"acoustic_tokenizer_config": VibeVoiceAcousticTokenizerConfig,
|
||||
"semantic_tokenizer_config": VibeVoiceSemanticTokenizerConfig,
|
||||
"decoder_config": Qwen2Config,
|
||||
}
|
||||
# keys_to_ignore_at_inference = ["past_key_values"]
|
||||
# Default tensor parallel plan for base model `Qwen2`
|
||||
base_model_tp_plan = {
|
||||
"layers.*.self_attn.q_proj": "colwise",
|
||||
"layers.*.self_attn.k_proj": "colwise",
|
||||
"layers.*.self_attn.v_proj": "colwise",
|
||||
"layers.*.self_attn.o_proj": "rowwise",
|
||||
"layers.*.mlp.gate_proj": "colwise",
|
||||
"layers.*.mlp.up_proj": "colwise",
|
||||
"layers.*.mlp.down_proj": "rowwise",
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
acoustic_tokenizer_config=None,
|
||||
semantic_tokenizer_config=None,
|
||||
decoder_config=None,
|
||||
**kwargs
|
||||
):
|
||||
|
||||
# kwargs["_attn_implementation"] = "flash_attention_2"
|
||||
kwargs["_attn_implementation_autoset"] = False
|
||||
|
||||
if acoustic_tokenizer_config is None:
|
||||
self.acoustic_tokenizer_config = self.sub_configs["acoustic_tokenizer_config"]()
|
||||
elif isinstance(acoustic_tokenizer_config, dict):
|
||||
acoustic_tokenizer_config["model_type"] = "vibevoice_acoustic_tokenizer"
|
||||
self.acoustic_tokenizer_config = self.sub_configs["acoustic_tokenizer_config"](**acoustic_tokenizer_config)
|
||||
elif isinstance(acoustic_tokenizer_config, VibeVoiceAcousticTokenizerConfig):
|
||||
# If an instance of the config class is provided
|
||||
self.acoustic_tokenizer_config = acoustic_tokenizer_config
|
||||
|
||||
if semantic_tokenizer_config is None:
|
||||
self.semantic_tokenizer_config = self.sub_configs["semantic_tokenizer_config"]()
|
||||
elif isinstance(semantic_tokenizer_config, dict):
|
||||
semantic_tokenizer_config["model_type"] = "vibevoice_semantic_tokenizer"
|
||||
self.semantic_tokenizer_config = self.sub_configs["semantic_tokenizer_config"](**semantic_tokenizer_config)
|
||||
elif isinstance(semantic_tokenizer_config, VibeVoiceSemanticTokenizerConfig):
|
||||
# If an instance of the config class is provided
|
||||
self.semantic_tokenizer_config = semantic_tokenizer_config
|
||||
|
||||
if decoder_config is None:
|
||||
self.decoder_config = self.sub_configs["decoder_config"]()
|
||||
elif isinstance(decoder_config, dict):
|
||||
# If a dictionary is provided, instantiate the config class with it
|
||||
# self.decoder_config = self.sub_configs["decoder_config"](**decoder_config)
|
||||
if decoder_config.get("model_type", '') == "qwen2":
|
||||
self.decoder_config = Qwen2Config(**decoder_config)
|
||||
else:
|
||||
raise ValueError(f"Unsupported decoder model type: {decoder_config.get('model_type', '')}")
|
||||
elif isinstance(decoder_config, Qwen2Config):
|
||||
# If an instance of the config class is provided
|
||||
self.decoder_config = decoder_config
|
||||
|
||||
# other parameters
|
||||
self.acoustic_vae_dim = getattr(self.acoustic_tokenizer_config, 'vae_dim', 64)
|
||||
self.semantic_vae_dim = getattr(self.semantic_tokenizer_config, 'vae_dim', 128)
|
||||
|
||||
super().__init__(**kwargs)
|
||||
|
||||
def to_dict(self):
|
||||
"""
|
||||
Override to_dict to handle torch.dtype serialization.
|
||||
|
||||
Fixes: https://github.com/microsoft/VibeVoice/issues/199
|
||||
"""
|
||||
output = super().to_dict()
|
||||
return _convert_dtype_to_string(output)
|
||||
|
||||
def get_text_config(self, decoder: bool = False):
|
||||
"""Return the text (decoder) config for generation."""
|
||||
return self.decoder_config
|
||||
|
||||
@property
|
||||
def vocab_size(self):
|
||||
"""Return vocab_size from decoder config for generation compatibility."""
|
||||
return self.decoder_config.vocab_size
|
||||
|
||||
@property
|
||||
def num_attention_heads(self):
|
||||
"""Return num_attention_heads from decoder config for Ulysses SP compatibility."""
|
||||
return self.decoder_config.num_attention_heads
|
||||
|
||||
@property
|
||||
def num_key_value_heads(self):
|
||||
"""Return num_key_value_heads from decoder config for Ulysses SP compatibility."""
|
||||
return self.decoder_config.num_key_value_heads
|
||||
|
||||
@property
|
||||
def hidden_size(self):
|
||||
"""Return hidden_size from decoder config for model compatibility."""
|
||||
return self.decoder_config.hidden_size
|
||||
|
||||
@property
|
||||
def num_hidden_layers(self):
|
||||
"""Return num_hidden_layers from decoder config for Ulysses SP compatibility."""
|
||||
return self.decoder_config.num_hidden_layers
|
||||
|
||||
@property
|
||||
def head_dim(self):
|
||||
"""Return head_dim from decoder config for Ulysses SP compatibility."""
|
||||
return getattr(self.decoder_config, 'head_dim', self.hidden_size // self.num_attention_heads)
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceAcousticTokenizerConfig",
|
||||
"VibeVoiceSemanticTokenizerConfig",
|
||||
"VibeVoiceDiffusionHeadConfig",
|
||||
"VibeVoiceConfig",
|
||||
"VibeVoiceASRConfig"
|
||||
]
|
||||
@@ -0,0 +1,104 @@
|
||||
""" VibeVoice Streaming model configuration"""
|
||||
|
||||
import torch
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import logging
|
||||
|
||||
from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
|
||||
|
||||
from .configuration_vibevoice import VibeVoiceAcousticTokenizerConfig, VibeVoiceDiffusionHeadConfig, _convert_dtype_to_string
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class VibeVoiceStreamingConfig(PretrainedConfig):
|
||||
model_type = "vibevoice_streaming"
|
||||
is_composition = True
|
||||
sub_configs = {
|
||||
"acoustic_tokenizer_config": VibeVoiceAcousticTokenizerConfig,
|
||||
"decoder_config": Qwen2Config,
|
||||
"diffusion_head_config": VibeVoiceDiffusionHeadConfig,
|
||||
}
|
||||
# keys_to_ignore_at_inference = ["past_key_values"]
|
||||
# Default tensor parallel plan for base model `Qwen2`
|
||||
base_model_tp_plan = {
|
||||
"layers.*.self_attn.q_proj": "colwise",
|
||||
"layers.*.self_attn.k_proj": "colwise",
|
||||
"layers.*.self_attn.v_proj": "colwise",
|
||||
"layers.*.self_attn.o_proj": "rowwise",
|
||||
"layers.*.mlp.gate_proj": "colwise",
|
||||
"layers.*.mlp.up_proj": "colwise",
|
||||
"layers.*.mlp.down_proj": "rowwise",
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
acoustic_tokenizer_config=None,
|
||||
decoder_config=None,
|
||||
diffusion_head_config=None,
|
||||
tts_backbone_num_hidden_layers=20,
|
||||
**kwargs
|
||||
):
|
||||
|
||||
# kwargs["_attn_implementation"] = "flash_attention_2"
|
||||
kwargs["_attn_implementation_autoset"] = False
|
||||
|
||||
if acoustic_tokenizer_config is None:
|
||||
self.acoustic_tokenizer_config = self.sub_configs["acoustic_tokenizer_config"]()
|
||||
elif isinstance(acoustic_tokenizer_config, dict):
|
||||
acoustic_tokenizer_config["model_type"] = "vibevoice_acoustic_tokenizer"
|
||||
self.acoustic_tokenizer_config = self.sub_configs["acoustic_tokenizer_config"](**acoustic_tokenizer_config)
|
||||
elif isinstance(acoustic_tokenizer_config, VibeVoiceAcousticTokenizerConfig):
|
||||
# If an instance of the config class is provided
|
||||
self.acoustic_tokenizer_config = acoustic_tokenizer_config
|
||||
|
||||
if decoder_config is None:
|
||||
self.decoder_config = self.sub_configs["decoder_config"]()
|
||||
elif isinstance(decoder_config, dict):
|
||||
# If a dictionary is provided, instantiate the config class with it
|
||||
# self.decoder_config = self.sub_configs["decoder_config"](**decoder_config)
|
||||
if decoder_config.get("model_type", '') == "qwen2":
|
||||
self.decoder_config = Qwen2Config(**decoder_config)
|
||||
else:
|
||||
raise ValueError(f"Unsupported decoder model type: {decoder_config.get('model_type', '')}")
|
||||
elif isinstance(decoder_config, (Qwen2Config,)):
|
||||
# If an instance of the config class is provided
|
||||
self.decoder_config = decoder_config
|
||||
|
||||
if diffusion_head_config is None:
|
||||
self.diffusion_head_config = self.sub_configs["diffusion_head_config"]()
|
||||
elif isinstance(diffusion_head_config, dict):
|
||||
diffusion_head_config["model_type"] = "vibevoice_diffusion_head"
|
||||
self.diffusion_head_config = self.sub_configs["diffusion_head_config"](**diffusion_head_config)
|
||||
elif isinstance(diffusion_head_config, VibeVoiceDiffusionHeadConfig):
|
||||
# If an instance of the config class is provided
|
||||
self.diffusion_head_config = diffusion_head_config
|
||||
|
||||
# other parameters
|
||||
self.acoustic_vae_dim = getattr(self.acoustic_tokenizer_config, 'vae_dim', 64)
|
||||
# The decoder of the model is divided into two components. The lower Transformer layers are only used for encoding text, while the upper Transformer layers are used for encoding text and generating speech. `tts_backbone_num_hidden_layers` indicates the number of upper layers used for TTS.
|
||||
self.tts_backbone_num_hidden_layers = tts_backbone_num_hidden_layers
|
||||
|
||||
super().__init__(**kwargs)
|
||||
|
||||
def get_text_config(self, decoder=False):
|
||||
"""Returns the decoder config (required for transformers >= 4.57 cache compatibility)."""
|
||||
return self.decoder_config
|
||||
|
||||
@property
|
||||
def num_hidden_layers(self):
|
||||
"""Proxy to decoder_config.num_hidden_layers (required for transformers >= 4.57)."""
|
||||
return self.decoder_config.num_hidden_layers
|
||||
|
||||
def to_dict(self):
|
||||
"""
|
||||
Override to_dict to handle torch.dtype serialization.
|
||||
|
||||
Fixes: https://github.com/microsoft/VibeVoice/issues/199
|
||||
"""
|
||||
output = super().to_dict()
|
||||
return _convert_dtype_to_string(output)
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceStreamingConfig"
|
||||
]
|
||||
@@ -0,0 +1,496 @@
|
||||
# copied from https://github.com/vibevoice-community/VibeVoice/blob/main/vibevoice/modular/modeling_vibevoice.py
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict, List, Optional, Tuple, Union, Callable
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.distributed as dist
|
||||
|
||||
from transformers.models.auto import AutoModel, AutoModelForCausalLM
|
||||
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.modeling_outputs import CausalLMOutput, BaseModelOutputWithPast, ModelOutput
|
||||
from transformers.models.llama.modeling_llama import LlamaRMSNorm
|
||||
from transformers import modeling_utils
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
from .modular_vibevoice_tokenizer import VibeVoiceTokenizerStreamingCache, VibeVoiceAcousticTokenizerModel, VibeVoiceSemanticTokenizerModel
|
||||
from .modular_vibevoice_diffusion_head import VibeVoiceDiffusionHead
|
||||
from vibevoice.schedule.dpm_solver import DPMSolverMultistepScheduler
|
||||
|
||||
from .configuration_vibevoice import VibeVoiceConfig
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
if not hasattr(modeling_utils, "ALL_PARALLEL_STYLES") or modeling_utils.ALL_PARALLEL_STYLES is None:
|
||||
modeling_utils.ALL_PARALLEL_STYLES = ["tp", "none", "colwise", "rowwise"]
|
||||
|
||||
@dataclass
|
||||
class VibeVoiceCausalLMOutputWithPast(ModelOutput):
|
||||
loss: Optional[torch.FloatTensor] = None
|
||||
diffusion_loss: Optional[torch.FloatTensor] = None
|
||||
speech_token_num: Optional[int] = None
|
||||
logits: torch.FloatTensor = None
|
||||
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
|
||||
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
||||
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class VibeVoiceGenerationOutput(ModelOutput):
|
||||
"""
|
||||
Output type for VibeVoice generation.
|
||||
|
||||
Args:
|
||||
sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
||||
The generated sequences.
|
||||
speech_outputs (`List[torch.FloatTensor]`, *optional*):
|
||||
List of generated speech waveforms or latents for each speech segment.
|
||||
"""
|
||||
sequences: torch.LongTensor = None
|
||||
speech_outputs: Optional[List[torch.FloatTensor]] = None
|
||||
|
||||
|
||||
class SpeechConnector(nn.Module):
|
||||
def __init__(self, input_dim, output_dim):
|
||||
super().__init__()
|
||||
self.fc1 = nn.Linear(input_dim, output_dim)
|
||||
self.norm = LlamaRMSNorm(output_dim, eps=1e-6)
|
||||
self.fc2 = nn.Linear(output_dim, output_dim)
|
||||
|
||||
def forward(self, features, **kwargs):
|
||||
x = self.fc1(features)
|
||||
x = self.norm(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
# @auto_docstring
|
||||
class VibeVoicePreTrainedModel(PreTrainedModel):
|
||||
config_class = VibeVoiceConfig
|
||||
base_model_prefix = "model"
|
||||
supports_gradient_checkpointing = True
|
||||
_skip_keys_device_placement = "past_key_values"
|
||||
_supports_cache_class = True
|
||||
_supports_flash_attn_2 = True
|
||||
_supports_sdpa = True
|
||||
_supports_quantized_cache = True
|
||||
_supports_static_cache = True
|
||||
_supports_attention_backend = True
|
||||
|
||||
def _init_weights(self, module):
|
||||
if isinstance(module, VibeVoiceDiffusionHead):
|
||||
module.initialize_weights()
|
||||
return
|
||||
|
||||
# Use the language model's initializer_range if available
|
||||
if hasattr(self.config, 'language_model_config') and hasattr(self.config.language_model_config, 'initializer_range'):
|
||||
std = self.config.language_model_config.initializer_range
|
||||
elif hasattr(self.config, 'decoder_config') and hasattr(self.config.decoder_config, 'initializer_range'):
|
||||
std = self.config.decoder_config.initializer_range
|
||||
else:
|
||||
std = 0.02 # Default value
|
||||
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.LayerNorm):
|
||||
module.weight.data.fill_(1.0)
|
||||
module.bias.data.zero_()
|
||||
|
||||
# @auto_docstring
|
||||
class VibeVoiceModel(VibeVoicePreTrainedModel):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
|
||||
if hasattr(config, 'torch_dtype') and config.torch_dtype is not None:
|
||||
if isinstance(config.torch_dtype, str):
|
||||
dtype = getattr(torch, config.torch_dtype)
|
||||
else:
|
||||
dtype = config.torch_dtype
|
||||
else:
|
||||
dtype = torch.float32
|
||||
|
||||
# Initialize Qwen2 model for language modeling
|
||||
lm_config = config.decoder_config
|
||||
self.language_model = AutoModel.from_config(lm_config)
|
||||
|
||||
# Initialize speech components if needed
|
||||
self.acoustic_tokenizer = AutoModel.from_config(config.acoustic_tokenizer_config).to(dtype)
|
||||
self.semantic_tokenizer = AutoModel.from_config(config.semantic_tokenizer_config).to(dtype)
|
||||
|
||||
self.acoustic_connector = SpeechConnector(config.acoustic_vae_dim, lm_config.hidden_size).to(dtype)
|
||||
self.semantic_connector = SpeechConnector(config.semantic_vae_dim, lm_config.hidden_size).to(dtype)
|
||||
|
||||
# Register scaling factors as buffers - use 1D tensors for FSDP compatibility
|
||||
self.register_buffer('speech_scaling_factor', torch.tensor(float('nan')))
|
||||
self.register_buffer('speech_bias_factor', torch.tensor(float('nan')))
|
||||
|
||||
# Initialize prediction head for speech generation
|
||||
self.prediction_head = AutoModel.from_config(config.diffusion_head_config).to(dtype)
|
||||
|
||||
# Initialize noise scheduler
|
||||
self.noise_scheduler = DPMSolverMultistepScheduler(
|
||||
num_train_timesteps=config.diffusion_head_config.ddpm_num_steps,
|
||||
beta_schedule=config.diffusion_head_config.ddpm_beta_schedule,
|
||||
prediction_type=config.diffusion_head_config.prediction_type
|
||||
)
|
||||
|
||||
def get_input_embeddings(self):
|
||||
if hasattr(self.language_model, 'embed_tokens'):
|
||||
# If the language model has an embed_tokens attribute, return it
|
||||
return self.language_model.embed_tokens
|
||||
|
||||
for name, attr in self.language_model.fullmap.items(): # parallel by nnscaler, the name is changed
|
||||
if attr.orig_name == 'embed_tokens.weight':
|
||||
return getattr(self.language_model, name)
|
||||
assert False, 'should not arrive here'
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.language_model.embed_tokens = value
|
||||
|
||||
def set_speech_tokenizers(self, acoustic_tokenizer=None, semantic_tokenizer=None):
|
||||
"""Set the speech tokenizers used for encoding and decoding speech."""
|
||||
self.acoustic_tokenizer = acoustic_tokenizer
|
||||
self.semantic_tokenizer = semantic_tokenizer
|
||||
|
||||
# Reset the encoder to evaluation mode
|
||||
if self.acoustic_tokenizer is not None:
|
||||
self.acoustic_tokenizer.eval()
|
||||
|
||||
if self.semantic_tokenizer is not None:
|
||||
self.semantic_tokenizer.eval()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs,
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# Forward through language model
|
||||
outputs = self.language_model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
if not return_dict:
|
||||
return outputs
|
||||
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=outputs.last_hidden_state,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
|
||||
class VibeVoiceForConditionalGeneration(VibeVoicePreTrainedModel):
|
||||
_tied_weights_keys = ["lm_head.weight"]
|
||||
_tp_plan = {"lm_head": "colwise_rep"}
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.model = VibeVoiceModel(config)
|
||||
self.vocab_size = config.decoder_config.vocab_size
|
||||
self.lm_head = nn.Linear(config.decoder_config.hidden_size, self.vocab_size, bias=False)
|
||||
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.model.get_input_embeddings()
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.model.set_input_embeddings(value)
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.model.language_model = decoder
|
||||
|
||||
def get_decoder(self):
|
||||
return self.model.language_model
|
||||
|
||||
def tie_weights(self):
|
||||
"""
|
||||
Tie the weights between the input embeddings and the output embeddings.
|
||||
"""
|
||||
if getattr(self.config.decoder_config, 'tie_word_embeddings', False):
|
||||
# The standard PreTrainedModel method will handle the tying.
|
||||
# It typically does a simple parameter object assignment, which is
|
||||
# CORRECT to do BEFORE FSDP wraps the model.
|
||||
output_embeddings = self.get_output_embeddings()
|
||||
input_embeddings = self.get_input_embeddings()
|
||||
if hasattr(input_embeddings, 'weight'):
|
||||
output_embeddings.weight = input_embeddings.weight
|
||||
else:
|
||||
# maybe returned input_embeddings a tensor directly
|
||||
output_embeddings.weight = input_embeddings
|
||||
|
||||
if getattr(output_embeddings, "bias", None) is not None:
|
||||
output_embeddings.bias.data = nn.functional.pad(
|
||||
output_embeddings.bias.data,
|
||||
(0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0]),
|
||||
"constant",
|
||||
0,
|
||||
)
|
||||
print("Tied input and output embeddings using standard assignment.")
|
||||
else:
|
||||
print("tie_word_embeddings is False, not tying weights.")
|
||||
|
||||
# Also, ensure set_output_embeddings is safe, though your implementation looks okay.
|
||||
# The key is to avoid calling it after accelerator.prepare().
|
||||
def set_output_embeddings(self, new_embeddings):
|
||||
# Your current implementation using data.copy_ is good practice,
|
||||
# but the best way is to not call this after prepare().
|
||||
self.lm_head = new_embeddings
|
||||
|
||||
def forward_speech_features(
|
||||
self,
|
||||
speech_tensors=None,
|
||||
speech_masks=None,
|
||||
speech_type="audio",
|
||||
return_unmask=False
|
||||
):
|
||||
if speech_tensors is None:
|
||||
# Use config to get vae_dim instead of non-existent self.args
|
||||
vae_dim = self.config.acoustic_tokenizer_config.vae_dim
|
||||
audio_features = torch.zeros(1, 1, vae_dim).to(self.get_input_embeddings().weight)
|
||||
connect_features = self.model.acoustic_connector(audio_features)
|
||||
return audio_features, connect_features
|
||||
else:
|
||||
with torch.no_grad():
|
||||
if speech_type == "audio":
|
||||
with torch.no_grad():
|
||||
frames = self.model.acoustic_tokenizer.encode(speech_tensors.unsqueeze(1))[0][0]
|
||||
audio_tokens = frames.sample(self.model.acoustic_tokenizer.std_dist_type)[0]
|
||||
|
||||
elif speech_type == "vae":
|
||||
# Use config to get vae_dim instead of non-existent self.args
|
||||
vae_dim = self.config.acoustic_tokenizer_config.vae_dim
|
||||
speech_mode = speech_tensors.reshape(speech_tensors.size(0), -1, vae_dim)
|
||||
|
||||
# gaussian sample from the speech_mode
|
||||
batch_size = speech_mode.size(0)
|
||||
value = self.model.acoustic_tokenizer.fix_std / 0.8
|
||||
std = torch.randn(batch_size, dtype=speech_mode.dtype, device=speech_mode.device) * value
|
||||
std = std.view(-1, *[1] * (speech_mode.dim() - 1))
|
||||
audio_tokens = speech_mode + std * torch.randn(speech_mode.shape).to(speech_mode)
|
||||
else:
|
||||
raise NotImplementedError(f"Speech type {speech_type} not implemented")
|
||||
|
||||
if torch.isnan(self.model.speech_scaling_factor) or torch.isnan(self.model.speech_bias_factor):
|
||||
scaling_factor = 1. / audio_tokens[speech_masks].flatten().std()
|
||||
bias_factor = -audio_tokens[speech_masks].flatten().mean()
|
||||
|
||||
# Only use distributed operations if the process group is initialized
|
||||
if dist.is_available() and dist.is_initialized():
|
||||
dist.all_reduce(scaling_factor, op=dist.ReduceOp.SUM)
|
||||
dist.all_reduce(bias_factor, op=dist.ReduceOp.SUM)
|
||||
world_size = dist.get_world_size()
|
||||
self.model.speech_scaling_factor.copy_(scaling_factor / world_size)
|
||||
self.model.speech_bias_factor.copy_(bias_factor / world_size)
|
||||
print(f"Speech scaling factor (distributed): {self.model.speech_scaling_factor}, bias factor: {self.model.speech_bias_factor}", flush=True)
|
||||
else:
|
||||
# Single process case
|
||||
self.model.speech_scaling_factor.copy_(scaling_factor)
|
||||
self.model.speech_bias_factor.copy_(bias_factor)
|
||||
print(f"Speech scaling factor (single process): {self.model.speech_scaling_factor}, bias factor: {self.model.speech_bias_factor}", flush=True)
|
||||
|
||||
audio_features = (audio_tokens + self.model.speech_bias_factor) * self.model.speech_scaling_factor
|
||||
|
||||
connect_features = self.model.acoustic_connector(audio_features)
|
||||
if return_unmask:
|
||||
return audio_features, connect_features
|
||||
return audio_features[speech_masks], connect_features[speech_masks]
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = False,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
# New arguments for speech processing and loss calculation
|
||||
speech_tensors: Optional[torch.FloatTensor] = None,
|
||||
speech_masks: Optional[torch.BoolTensor] = None,
|
||||
speeches_loss_input: Optional[torch.FloatTensor] = None,
|
||||
speech_semantic_tensors: Optional[torch.FloatTensor] = None,
|
||||
acoustic_input_mask: Optional[torch.BoolTensor] = None,
|
||||
acoustic_loss_mask: Optional[torch.BoolTensor] = None,
|
||||
ddpm_batch_mul: int = 1,
|
||||
**kwargs: Optional[Dict[str, Union[torch.Tensor, str]]],
|
||||
) -> Union[Tuple, VibeVoiceCausalLMOutputWithPast]:
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
x = self.get_input_embeddings()(input_ids)
|
||||
|
||||
semantic_speech_all_connect_features = self.model.semantic_connector(speech_semantic_tensors)
|
||||
if speeches_loss_input is not None:
|
||||
# only part audio need diffuse
|
||||
speech_all_features, speech_all_connect_features = self.forward_speech_features(
|
||||
speech_tensors=speech_tensors.type_as(x) if speech_tensors is not None else None,
|
||||
speech_masks=speech_masks,
|
||||
speech_type=kwargs.get("speech_type", "audio"),
|
||||
return_unmask=True
|
||||
)
|
||||
if speech_tensors is not None:
|
||||
if semantic_speech_all_connect_features is not None:
|
||||
x[acoustic_input_mask] = (
|
||||
speech_all_connect_features[speech_masks]
|
||||
+ semantic_speech_all_connect_features[speech_masks]
|
||||
)
|
||||
else:
|
||||
x[acoustic_input_mask] = speech_all_connect_features[speech_masks]
|
||||
|
||||
# Select only the target segments' latents for diffusion loss.
|
||||
# Both masks are [num_segments, max_latent_len]; using 2D mask on [B,T,D] selects [N_true, D].
|
||||
target_latent_mask = speeches_loss_input & speech_masks
|
||||
speech_features = speech_all_features[target_latent_mask]
|
||||
speech_connect_features = speech_all_connect_features[target_latent_mask]
|
||||
else:
|
||||
speech_features, speech_connect_features = self.forward_speech_features(
|
||||
speech_tensors=speech_tensors.type_as(x) if speech_tensors is not None else None,
|
||||
speech_masks=speech_masks,
|
||||
speech_type=kwargs.get("speech_type", "audio"),
|
||||
)
|
||||
if speech_tensors is not None:
|
||||
x[acoustic_input_mask] = speech_connect_features
|
||||
|
||||
outputs = self.model(
|
||||
input_ids=None,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=x,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=False,
|
||||
return_dict=return_dict,
|
||||
cache_position=cache_position,
|
||||
)
|
||||
|
||||
hidden_states = outputs.last_hidden_state
|
||||
logits = self.lm_head(hidden_states)
|
||||
# logits = logits.float()
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
# The custom CE loss with masking is calculated in the training script.
|
||||
# We leave the standard loss calculation here as None.
|
||||
pass
|
||||
|
||||
# --- Diffusion Loss Calculation ---
|
||||
diffusion_loss = None
|
||||
# This block is executed only if we are in a context that involves speech.
|
||||
if speech_tensors is not None and acoustic_loss_mask.sum().item() > 0:
|
||||
condition_features = hidden_states[acoustic_loss_mask]
|
||||
|
||||
speech_len, latent_size = speech_features.shape
|
||||
|
||||
noise = torch.randn(
|
||||
(speech_len * ddpm_batch_mul, latent_size),
|
||||
device=hidden_states.device,
|
||||
dtype=hidden_states.dtype
|
||||
)
|
||||
|
||||
timesteps = torch.multinomial(
|
||||
torch.ones(self.config.diffusion_head_config.ddpm_num_steps),
|
||||
speech_len * ddpm_batch_mul,
|
||||
replacement=True,
|
||||
).to(hidden_states.device)
|
||||
|
||||
speech_features_repeated = speech_features.repeat_interleave(ddpm_batch_mul, dim=0)
|
||||
condition_features_repeated = condition_features.repeat_interleave(ddpm_batch_mul, dim=0)
|
||||
|
||||
noisy_speech_features = self.model.noise_scheduler.add_noise(
|
||||
speech_features_repeated, noise, timesteps
|
||||
)
|
||||
|
||||
model_output = self.model.prediction_head(
|
||||
noisy_speech_features,
|
||||
timesteps.type_as(x),
|
||||
condition_features_repeated
|
||||
)
|
||||
|
||||
prediction_type = self.config.diffusion_head_config.prediction_type
|
||||
if prediction_type == "epsilon":
|
||||
target_for_loss = noise
|
||||
elif prediction_type == "v_prediction":
|
||||
target_for_loss = self.model.noise_scheduler.get_velocity(
|
||||
speech_features_repeated, noise, timesteps
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError(f"Prediction type {prediction_type} not implemented")
|
||||
|
||||
diffusion_loss = F.mse_loss(model_output.float(), target_for_loss.float(), reduction='sum')
|
||||
if latent_size > 0 and ddpm_batch_mul > 0:
|
||||
diffusion_loss = diffusion_loss / latent_size / ddpm_batch_mul
|
||||
else:
|
||||
diffusion_loss = torch.tensor(0.0, device=diffusion_loss.device)
|
||||
|
||||
else:
|
||||
# Dummy loss for DDP to work when there are no speech samples in a batch,
|
||||
# but we are in a speech context.
|
||||
diffusion_loss = sum(p.sum() for p in self.model.prediction_head.parameters()) * 0.0
|
||||
diffusion_loss += sum(p.sum() for p in self.model.acoustic_connector.parameters()) * 0.0
|
||||
diffusion_loss += sum(p.sum() for p in self.model.semantic_connector.parameters()) * 0.0
|
||||
# --- End Diffusion Loss Calculation ---
|
||||
|
||||
if not return_dict:
|
||||
output = (logits, speech_len) + outputs.to_tuple()[1:]
|
||||
return (loss, diffusion_loss) + output
|
||||
|
||||
return VibeVoiceCausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
diffusion_loss=diffusion_loss,
|
||||
speech_token_num=speech_len if speech_tensors is not None else 0,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
AutoModel.register(VibeVoiceConfig, VibeVoiceModel)
|
||||
AutoModelForCausalLM.register(VibeVoiceConfig, VibeVoiceForConditionalGeneration)
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceModel",
|
||||
"VibeVoicePreTrainedModel",
|
||||
"VibeVoiceForConditionalGeneration",
|
||||
"VibeVoiceCausalLMOutputWithPast",
|
||||
"VibeVoiceGenerationOutput",
|
||||
]
|
||||
@@ -0,0 +1,522 @@
|
||||
from typing import List, Optional, Tuple, Union
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from transformers.models.auto import AutoModel, AutoModelForCausalLM
|
||||
|
||||
from transformers.modeling_outputs import CausalLMOutput, BaseModelOutputWithPast
|
||||
from transformers import modeling_utils
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
from transformers.utils import logging
|
||||
from transformers.generation import GenerationMixin
|
||||
|
||||
from .modular_vibevoice_tokenizer import (
|
||||
VibeVoiceTokenizerStreamingCache,
|
||||
VibeVoiceTokenizerEncoderOutput
|
||||
)
|
||||
|
||||
from .configuration_vibevoice import VibeVoiceASRConfig
|
||||
from .modeling_vibevoice import (
|
||||
VibeVoiceCausalLMOutputWithPast,
|
||||
SpeechConnector
|
||||
)
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
if not hasattr(modeling_utils, "ALL_PARALLEL_STYLES") or modeling_utils.ALL_PARALLEL_STYLES is None:
|
||||
modeling_utils.ALL_PARALLEL_STYLES = ["tp", "none", "colwise", "rowwise"]
|
||||
|
||||
# @auto_docstring
|
||||
class VibeVoiceASRPreTrainedModel(PreTrainedModel):
|
||||
config_class = VibeVoiceASRConfig
|
||||
base_model_prefix = "model"
|
||||
supports_gradient_checkpointing = True
|
||||
_skip_keys_device_placement = "past_key_values"
|
||||
_supports_cache_class = True
|
||||
_supports_flash_attn = True
|
||||
_supports_flash_attn_2 = True
|
||||
_supports_sdpa = True
|
||||
_supports_quantized_cache = True
|
||||
_supports_static_cache = True
|
||||
_supports_attention_backend = True
|
||||
|
||||
def _init_weights(self, module):
|
||||
|
||||
# Use the language model's initializer_range if available
|
||||
if hasattr(self.config, 'language_model_config') and hasattr(self.config.language_model_config, 'initializer_range'):
|
||||
std = self.config.language_model_config.initializer_range
|
||||
elif hasattr(self.config, 'decoder_config') and hasattr(self.config.decoder_config, 'initializer_range'):
|
||||
std = self.config.decoder_config.initializer_range
|
||||
else:
|
||||
std = 0.02 # Default value
|
||||
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.LayerNorm):
|
||||
module.weight.data.fill_(1.0)
|
||||
module.bias.data.zero_()
|
||||
|
||||
# @auto_docstring
|
||||
class VibeVoiceASRModel(VibeVoiceASRPreTrainedModel):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
|
||||
if hasattr(config, 'torch_dtype') and config.torch_dtype is not None:
|
||||
if isinstance(config.torch_dtype, str):
|
||||
dtype = getattr(torch, config.torch_dtype)
|
||||
else:
|
||||
dtype = config.torch_dtype
|
||||
else:
|
||||
dtype = torch.float32
|
||||
|
||||
# Initialize Qwen2 model for language modeling
|
||||
lm_config = config.decoder_config
|
||||
self.language_model = AutoModel.from_config(lm_config)
|
||||
|
||||
# Initialize speech components if needed
|
||||
self.acoustic_tokenizer = AutoModel.from_config(config.acoustic_tokenizer_config).to(dtype)
|
||||
self.semantic_tokenizer = AutoModel.from_config(config.semantic_tokenizer_config).to(dtype)
|
||||
|
||||
self.acoustic_connector = SpeechConnector(config.acoustic_vae_dim, lm_config.hidden_size).to(dtype)
|
||||
self.semantic_connector = SpeechConnector(config.semantic_vae_dim, lm_config.hidden_size).to(dtype)
|
||||
|
||||
def get_input_embeddings(self):
|
||||
if hasattr(self.language_model, 'embed_tokens'):
|
||||
# If the language model has an embed_tokens attribute, return it
|
||||
return self.language_model.embed_tokens
|
||||
|
||||
for name, attr in self.language_model.fullmap.items(): # parallel by nnscaler, the name is changed
|
||||
if attr.orig_name == 'embed_tokens.weight':
|
||||
return getattr(self.language_model, name)
|
||||
assert False, 'should not arrive here'
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.language_model.embed_tokens = value
|
||||
|
||||
def set_speech_tokenizers(self, acoustic_tokenizer=None, semantic_tokenizer=None):
|
||||
"""Set the speech tokenizers used for encoding and decoding speech."""
|
||||
self.acoustic_tokenizer = acoustic_tokenizer
|
||||
self.semantic_tokenizer = semantic_tokenizer
|
||||
|
||||
# Reset the encoder to evaluation mode
|
||||
if self.acoustic_tokenizer is not None:
|
||||
self.acoustic_tokenizer.eval()
|
||||
|
||||
if self.semantic_tokenizer is not None:
|
||||
self.semantic_tokenizer.eval()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs,
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# Forward through language model
|
||||
outputs = self.language_model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
if not return_dict:
|
||||
return outputs
|
||||
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=outputs.last_hidden_state,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
class VibeVoiceASRForConditionalGeneration(VibeVoiceASRPreTrainedModel, GenerationMixin):
|
||||
"""
|
||||
VibeVoice model for Automatic Speech Recognition (ASR) with language modeling head for conditional generation.
|
||||
This class is designed for inference and generation tasks.
|
||||
"""
|
||||
_tied_weights_keys = ["lm_head.weight"]
|
||||
_tp_plan = {"lm_head": "colwise_rep"}
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.model = VibeVoiceASRModel(config)
|
||||
self.vocab_size = config.decoder_config.vocab_size
|
||||
|
||||
# Determine the dtype to use
|
||||
if hasattr(config, 'torch_dtype') and config.torch_dtype is not None:
|
||||
if isinstance(config.torch_dtype, str):
|
||||
dtype = getattr(torch, config.torch_dtype)
|
||||
else:
|
||||
dtype = config.torch_dtype
|
||||
else:
|
||||
dtype = torch.float32
|
||||
|
||||
# Initialize lm_head with the correct dtype
|
||||
self.lm_head = nn.Linear(config.decoder_config.hidden_size, self.vocab_size, bias=False).to(dtype)
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.model.get_input_embeddings()
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.model.set_input_embeddings(value)
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def set_output_embeddings(self, new_embeddings):
|
||||
self.lm_head = new_embeddings
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.model.language_model = decoder
|
||||
|
||||
def get_decoder(self):
|
||||
return self.model.language_model
|
||||
|
||||
def tie_weights(self):
|
||||
"""Tie the weights between the input embeddings and the output embeddings."""
|
||||
if getattr(self.config.decoder_config, 'tie_word_embeddings', False):
|
||||
output_embeddings = self.get_output_embeddings()
|
||||
input_embeddings = self.get_input_embeddings()
|
||||
if hasattr(input_embeddings, 'weight'):
|
||||
output_embeddings.weight = input_embeddings.weight
|
||||
else:
|
||||
output_embeddings.weight = input_embeddings
|
||||
|
||||
def encode_speech(
|
||||
self,
|
||||
speech_tensors: torch.FloatTensor,
|
||||
speech_masks: Optional[torch.BoolTensor] = None,
|
||||
speech_semantic_tensors: Optional[torch.FloatTensor] = None,
|
||||
streaming_segment_duration: float = 60.0, # seconds
|
||||
):
|
||||
"""
|
||||
Encode speech input into features that can be used by the language model.
|
||||
This method is called once before generation to process the speech input.
|
||||
|
||||
For long audio (>600s by default), uses streaming processing to avoid conv overflow (>2^32).
|
||||
Segments are processed independently, then concatenated before final sampling.
|
||||
|
||||
Args:
|
||||
speech_tensors: Input audio tensor [batch_size, samples]
|
||||
speech_masks: Optional mask for speech features
|
||||
speech_semantic_tensors: Optional pre-computed semantic tokens
|
||||
streaming_segment_duration: Segment duration in seconds for streaming processing (default: 60s)
|
||||
"""
|
||||
if hasattr(self.config, 'torch_dtype') and self.config.torch_dtype is not None:
|
||||
if isinstance(self.config.torch_dtype, str):
|
||||
dtype = getattr(torch, self.config.torch_dtype)
|
||||
else:
|
||||
dtype = self.config.torch_dtype
|
||||
else:
|
||||
dtype = torch.float32
|
||||
|
||||
speech_tensors = speech_tensors.to(dtype)
|
||||
|
||||
# Ensure proper shape: (batch, samples)
|
||||
if speech_tensors.ndim == 1:
|
||||
speech_tensors = speech_tensors.unsqueeze(0)
|
||||
|
||||
batch_size, total_samples = speech_tensors.shape
|
||||
sample_rate = 24000 # fix 24kHz sample rate
|
||||
|
||||
# Calculate segment size in samples
|
||||
segment_samples = int(streaming_segment_duration * sample_rate)
|
||||
|
||||
# Decide whether to use streaming based on audio length
|
||||
use_streaming = total_samples > segment_samples
|
||||
|
||||
with torch.no_grad():
|
||||
if not use_streaming:
|
||||
# Short audio: direct processing (original behavior)
|
||||
encoder_output = self.model.acoustic_tokenizer.encode(speech_tensors.unsqueeze(1))
|
||||
audio_tokens = encoder_output.sample(dist_type=self.model.acoustic_tokenizer.std_dist_type)[0]
|
||||
acoustic_features = self.model.acoustic_connector(audio_tokens)
|
||||
|
||||
# Encode semantic features
|
||||
if speech_semantic_tensors is not None:
|
||||
semantic_features = self.model.semantic_connector(speech_semantic_tensors)
|
||||
else:
|
||||
semantic_tokens = self.model.semantic_tokenizer.encode(speech_tensors.unsqueeze(1)).mean
|
||||
semantic_features = self.model.semantic_connector(semantic_tokens)
|
||||
else:
|
||||
# Long audio: streaming processing
|
||||
# print(f"Using streaming processing for long audio: {total_samples/sample_rate:.1f}s "
|
||||
# f"(segment size: {streaming_segment_duration}s)")
|
||||
|
||||
# Initialize caches for both tokenizers
|
||||
acoustic_encoder_cache = VibeVoiceTokenizerStreamingCache()
|
||||
semantic_encoder_cache = VibeVoiceTokenizerStreamingCache()
|
||||
acoustic_mean_segments = []
|
||||
semantic_mean_segments = []
|
||||
sample_indices = torch.arange(batch_size, device=speech_tensors.device)
|
||||
|
||||
# Helper function from batch_asr_sft_cache.py
|
||||
def _iter_segments(total_length: int, segment_length: int):
|
||||
"""Iterate over audio segments with a given segment length."""
|
||||
if segment_length <= 0:
|
||||
raise ValueError("segment_length must be positive")
|
||||
for start in range(0, total_length, segment_length):
|
||||
end = min(start + segment_length, total_length)
|
||||
if end > start:
|
||||
yield start, end
|
||||
|
||||
# Process each segment for both acoustic and semantic tokenizers
|
||||
segments = list(_iter_segments(total_samples, segment_samples))
|
||||
num_segments = len(segments)
|
||||
for seg_idx, (start, end) in enumerate(segments):
|
||||
chunk = speech_tensors[:, start:end].contiguous()
|
||||
if chunk.numel() == 0:
|
||||
continue
|
||||
|
||||
# Check if this is the final segment
|
||||
is_final = (seg_idx == num_segments - 1)
|
||||
|
||||
# Encode chunk for acoustic tokenizer (don't sample yet)
|
||||
acoustic_encoder_output = self.model.acoustic_tokenizer.encode(
|
||||
chunk.unsqueeze(1),
|
||||
cache=acoustic_encoder_cache,
|
||||
sample_indices=sample_indices,
|
||||
use_cache=True,
|
||||
is_final_chunk=is_final,
|
||||
)
|
||||
acoustic_mean_segments.append(acoustic_encoder_output.mean)
|
||||
|
||||
# Encode chunk for semantic tokenizer (take mean directly)
|
||||
semantic_encoder_output = self.model.semantic_tokenizer.encode(
|
||||
chunk.unsqueeze(1),
|
||||
cache=semantic_encoder_cache,
|
||||
sample_indices=sample_indices,
|
||||
use_cache=True,
|
||||
is_final_chunk=is_final,
|
||||
)
|
||||
semantic_mean_segments.append(semantic_encoder_output.mean)
|
||||
|
||||
# print(f"Processed {len(acoustic_mean_segments)} segments.")
|
||||
# Concatenate all acoustic means and sample once
|
||||
acoustic_mean_full = torch.cat(acoustic_mean_segments, dim=1).contiguous()
|
||||
acoustic_encoder_output = VibeVoiceTokenizerEncoderOutput(
|
||||
mean=acoustic_mean_full,
|
||||
std=self.model.acoustic_tokenizer.fix_std
|
||||
)
|
||||
audio_tokens = acoustic_encoder_output.sample(
|
||||
dist_type=self.model.acoustic_tokenizer.std_dist_type
|
||||
)[0]
|
||||
acoustic_features = self.model.acoustic_connector(audio_tokens)
|
||||
|
||||
# Concatenate all semantic means
|
||||
semantic_tokens = torch.cat(semantic_mean_segments, dim=1).contiguous()
|
||||
semantic_features = self.model.semantic_connector(semantic_tokens)
|
||||
|
||||
# Combine acoustic and semantic features
|
||||
if speech_masks is not None:
|
||||
combined_features = acoustic_features[speech_masks] + semantic_features[speech_masks]
|
||||
else:
|
||||
combined_features = acoustic_features + semantic_features
|
||||
|
||||
return combined_features
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
# Speech-specific arguments
|
||||
speech_tensors: Optional[torch.FloatTensor] = None,
|
||||
speech_masks: Optional[torch.BoolTensor] = None,
|
||||
speech_semantic_tensors: Optional[torch.FloatTensor] = None,
|
||||
acoustic_input_mask: Optional[torch.BoolTensor] = None,
|
||||
**kwargs,
|
||||
) -> Union[Tuple, CausalLMOutput]:
|
||||
"""
|
||||
Forward pass for the model. Handles both training and generation scenarios.
|
||||
"""
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
use_cache = use_cache if use_cache is not None else getattr(self.config, 'use_cache', False)
|
||||
|
||||
# Process inputs
|
||||
if inputs_embeds is None and input_ids is not None:
|
||||
inputs_embeds = self.get_input_embeddings()(input_ids)
|
||||
|
||||
# If we have speech input and acoustic_input_mask, encode and insert speech features
|
||||
if speech_tensors is not None and acoustic_input_mask is not None:
|
||||
speech_features = self.encode_speech(
|
||||
speech_tensors=speech_tensors,
|
||||
speech_masks=speech_masks,
|
||||
speech_semantic_tensors=speech_semantic_tensors,
|
||||
)
|
||||
# Clone to avoid in-place operation on leaf variable during training
|
||||
inputs_embeds = inputs_embeds.clone()
|
||||
inputs_embeds[acoustic_input_mask] = speech_features
|
||||
|
||||
# Forward through the model
|
||||
outputs = self.model(
|
||||
input_ids=None,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
cache_position=cache_position,
|
||||
)
|
||||
|
||||
hidden_states = outputs[0] if not return_dict else outputs.last_hidden_state
|
||||
logits = self.lm_head(hidden_states)
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
# Shift so that tokens < n predict n
|
||||
shift_logits = logits[..., :-1, :].contiguous()
|
||||
shift_labels = labels[..., 1:].contiguous()
|
||||
# Flatten the tokens
|
||||
loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
|
||||
shift_logits = shift_logits.view(-1, self.vocab_size)
|
||||
shift_labels = shift_labels.view(-1)
|
||||
# Enable model parallelism
|
||||
shift_labels = shift_labels.to(shift_logits.device)
|
||||
loss = loss_fct(shift_logits, shift_labels)
|
||||
|
||||
if not return_dict:
|
||||
output = (logits,) + outputs[1:]
|
||||
return (loss,) + output if loss is not None else output
|
||||
|
||||
return VibeVoiceCausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
def prepare_inputs_for_generation(
|
||||
self,
|
||||
input_ids,
|
||||
past_key_values=None,
|
||||
attention_mask=None,
|
||||
inputs_embeds=None,
|
||||
cache_position=None,
|
||||
position_ids=None,
|
||||
use_cache=True,
|
||||
speech_tensors=None,
|
||||
speech_masks=None,
|
||||
speech_semantic_tensors=None,
|
||||
acoustic_input_mask=None,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Prepare inputs for generation step. This method is called by generate()
|
||||
for each token generation step.
|
||||
|
||||
Following Qwen2-VL's approach: speech inputs are only forwarded on the first pass
|
||||
(when cache_position[0] == 0), and are excluded in subsequent generation steps.
|
||||
"""
|
||||
# If we have past key values, we only need to process the new tokens
|
||||
if past_key_values is not None:
|
||||
if isinstance(past_key_values, tuple):
|
||||
past_length = past_key_values[0][0].shape[2]
|
||||
else:
|
||||
past_length = past_key_values.get_seq_length()
|
||||
|
||||
# Keep only the new tokens
|
||||
if input_ids is not None and input_ids.shape[1] > past_length:
|
||||
input_ids = input_ids[:, past_length:]
|
||||
|
||||
# Prepare position ids
|
||||
if position_ids is None and attention_mask is not None:
|
||||
position_ids = attention_mask.long().cumsum(-1) - 1
|
||||
position_ids.masked_fill_(attention_mask == 0, 1)
|
||||
if past_key_values is not None and input_ids is not None:
|
||||
position_ids = position_ids[:, -input_ids.shape[1]:]
|
||||
|
||||
# Prepare cache position
|
||||
if cache_position is None:
|
||||
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||||
cache_position = torch.arange(
|
||||
past_seen_tokens,
|
||||
past_seen_tokens + (input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]),
|
||||
device=input_ids.device if input_ids is not None else inputs_embeds.device
|
||||
)
|
||||
|
||||
# Prepare model inputs
|
||||
if inputs_embeds is not None and past_key_values is None:
|
||||
model_inputs = {"inputs_embeds": inputs_embeds}
|
||||
else:
|
||||
model_inputs = {"input_ids": input_ids}
|
||||
|
||||
model_inputs.update(
|
||||
{
|
||||
"position_ids": position_ids,
|
||||
"cache_position": cache_position,
|
||||
"past_key_values": past_key_values,
|
||||
"use_cache": use_cache,
|
||||
"attention_mask": attention_mask,
|
||||
}
|
||||
)
|
||||
|
||||
# Following Qwen2-VL pattern: only include speech inputs on the first forward pass
|
||||
# (when cache_position[0] == 0), exclude them in subsequent generation steps
|
||||
if cache_position is not None and len(cache_position) > 0 and cache_position[0] == 0:
|
||||
# First forward pass - include speech inputs if provided
|
||||
model_inputs.update({
|
||||
"speech_tensors": speech_tensors,
|
||||
"speech_masks": speech_masks,
|
||||
"speech_semantic_tensors": speech_semantic_tensors,
|
||||
"acoustic_input_mask": acoustic_input_mask,
|
||||
})
|
||||
else:
|
||||
# Subsequent generation steps - exclude speech inputs
|
||||
model_inputs.update({
|
||||
"speech_tensors": None,
|
||||
"speech_masks": None,
|
||||
"speech_semantic_tensors": None,
|
||||
"acoustic_input_mask": None,
|
||||
})
|
||||
|
||||
# Include any remaining kwargs that might be needed
|
||||
model_inputs.update(kwargs)
|
||||
|
||||
return model_inputs
|
||||
|
||||
AutoModel.register(VibeVoiceASRConfig, VibeVoiceASRModel)
|
||||
AutoModelForCausalLM.register(VibeVoiceASRConfig, VibeVoiceASRForConditionalGeneration)
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceASRPreTrainedModel",
|
||||
"VibeVoiceASRModel",
|
||||
"VibeVoiceASRForConditionalGeneration",
|
||||
]
|
||||
@@ -0,0 +1,190 @@
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict, List, Optional, Tuple, Union, Callable
|
||||
from tqdm import tqdm
|
||||
import copy
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.distributed as dist
|
||||
|
||||
from transformers.models.auto import AutoModel, AutoModelForCausalLM
|
||||
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.modeling_outputs import CausalLMOutput, BaseModelOutputWithPast, ModelOutput
|
||||
from transformers.models.llama.modeling_llama import LlamaRMSNorm
|
||||
from transformers import modeling_utils
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
|
||||
from transformers.utils import logging
|
||||
|
||||
from .modular_vibevoice_diffusion_head import VibeVoiceDiffusionHead
|
||||
from vibevoice.schedule.dpm_solver import DPMSolverMultistepScheduler
|
||||
|
||||
from .configuration_vibevoice_streaming import VibeVoiceStreamingConfig
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
if not hasattr(modeling_utils, "ALL_PARALLEL_STYLES") or modeling_utils.ALL_PARALLEL_STYLES is None:
|
||||
modeling_utils.ALL_PARALLEL_STYLES = ["tp", "none", "colwise", "rowwise"]
|
||||
|
||||
|
||||
class BinaryClassifier(nn.Module):
|
||||
def __init__(self, hidden_size):
|
||||
super(BinaryClassifier, self).__init__()
|
||||
self.fc1 = nn.Linear(hidden_size, hidden_size)
|
||||
self.fc2 = nn.Linear(hidden_size, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = torch.relu(self.fc1(x))
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
class SpeechConnector(nn.Module):
|
||||
def __init__(self, input_dim, output_dim):
|
||||
super().__init__()
|
||||
self.fc1 = nn.Linear(input_dim, output_dim)
|
||||
self.norm = LlamaRMSNorm(output_dim, eps=1e-6)
|
||||
self.fc2 = nn.Linear(output_dim, output_dim)
|
||||
|
||||
def forward(self, features, **kwargs):
|
||||
x = self.fc1(features)
|
||||
x = self.norm(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
# @auto_docstring
|
||||
class VibeVoiceStreamingPreTrainedModel(PreTrainedModel):
|
||||
config_class = VibeVoiceStreamingConfig
|
||||
base_model_prefix = "model"
|
||||
supports_gradient_checkpointing = True
|
||||
_skip_keys_device_placement = "past_key_values"
|
||||
_supports_cache_class = True
|
||||
_supports_flash_attn_2 = True
|
||||
_supports_sdpa = True
|
||||
_supports_quantized_cache = True
|
||||
_supports_static_cache = True
|
||||
_supports_attention_backend = True
|
||||
|
||||
def _init_weights(self, module):
|
||||
if isinstance(module, VibeVoiceDiffusionHead):
|
||||
module.initialize_weights()
|
||||
return
|
||||
|
||||
# Use the language model's initializer_range if available
|
||||
if hasattr(self.config, 'language_model_config') and hasattr(self.config.language_model_config, 'initializer_range'):
|
||||
std = self.config.language_model_config.initializer_range
|
||||
elif hasattr(self.config, 'decoder_config') and hasattr(self.config.decoder_config, 'initializer_range'):
|
||||
std = self.config.decoder_config.initializer_range
|
||||
else:
|
||||
std = 0.02 # Default value
|
||||
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.LayerNorm):
|
||||
module.weight.data.fill_(1.0)
|
||||
module.bias.data.zero_()
|
||||
|
||||
|
||||
# @auto_docstring
|
||||
class VibeVoiceStreamingModel(VibeVoiceStreamingPreTrainedModel):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
|
||||
if hasattr(config, 'torch_dtype') and config.torch_dtype is not None:
|
||||
if isinstance(config.torch_dtype, str):
|
||||
dtype = getattr(torch, config.torch_dtype)
|
||||
else:
|
||||
dtype = config.torch_dtype
|
||||
else:
|
||||
dtype = torch.float32
|
||||
|
||||
# Initialize Qwen2 model for language modeling.
|
||||
# The lower Transformer layers are only used for encoding text, while the upper Transformer layers are used for encoding text and generating speech.
|
||||
# To keep the code clean, we constructs two language models.
|
||||
# The final norm layer of the first language_model is set to identity and will not be used in inference.
|
||||
lm_config = copy.deepcopy(config.decoder_config)
|
||||
lm_backbone_num_hidden_layers = getattr(lm_config, 'num_hidden_layers', 24) - config.tts_backbone_num_hidden_layers
|
||||
lm_config.num_hidden_layers = lm_backbone_num_hidden_layers
|
||||
self.language_model = AutoModel.from_config(lm_config)
|
||||
self.language_model.norm = nn.Identity()
|
||||
|
||||
# We only need the Transformer layers here. Note that embed_tokens in tts_language_model is unused
|
||||
tts_lm_config = copy.deepcopy(lm_config)
|
||||
tts_lm_config.num_hidden_layers = config.tts_backbone_num_hidden_layers
|
||||
self.tts_language_model = AutoModel.from_config(tts_lm_config)
|
||||
|
||||
# Marks the text that needs to be spoken by the TTS model.
|
||||
self.tts_input_types = nn.Embedding(num_embeddings=2, embedding_dim=config.decoder_config.hidden_size)
|
||||
|
||||
# Initialize speech components if needed
|
||||
self.acoustic_tokenizer = AutoModel.from_config(config.acoustic_tokenizer_config).to(dtype)
|
||||
self.acoustic_connector = SpeechConnector(config.acoustic_vae_dim, lm_config.hidden_size).to(dtype)
|
||||
|
||||
# Register scaling factors as buffers - use 1D tensors for FSDP compatibility
|
||||
self.register_buffer('speech_scaling_factor', torch.tensor(float('nan')))
|
||||
self.register_buffer('speech_bias_factor', torch.tensor(float('nan')))
|
||||
|
||||
# Initialize prediction head for speech generation
|
||||
self.prediction_head = AutoModel.from_config(config.diffusion_head_config).to(dtype)
|
||||
|
||||
# Initialize noise scheduler
|
||||
self.noise_scheduler = DPMSolverMultistepScheduler(
|
||||
num_train_timesteps=config.diffusion_head_config.ddpm_num_steps,
|
||||
beta_schedule=config.diffusion_head_config.ddpm_beta_schedule,
|
||||
prediction_type=config.diffusion_head_config.prediction_type
|
||||
)
|
||||
|
||||
def get_input_embeddings(self):
|
||||
if hasattr(self.language_model, 'embed_tokens'):
|
||||
# If the language model has an embed_tokens attribute, return it
|
||||
return self.language_model.embed_tokens
|
||||
|
||||
for name, attr in self.language_model.fullmap.items(): # parallel by nnscaler, the name is changed
|
||||
if attr.orig_name == 'embed_tokens.weight':
|
||||
return getattr(self.language_model, name)
|
||||
assert False, 'should not arrive here'
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.language_model.embed_tokens = value
|
||||
|
||||
def set_speech_tokenizers(self, acoustic_tokenizer=None):
|
||||
"""Set the speech tokenizers used for encoding and decoding speech."""
|
||||
self.acoustic_tokenizer = acoustic_tokenizer
|
||||
|
||||
# Reset the encoder to evaluation mode
|
||||
if self.acoustic_tokenizer is not None:
|
||||
self.acoustic_tokenizer.eval()
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
"""
|
||||
Intentionally not implemented.
|
||||
|
||||
This streaming model is split into two explicit submodules:
|
||||
- `language_model` for plain text processing (lower layers).
|
||||
- `tts_language_model` for TTS-related upper layers.
|
||||
|
||||
We deliberately avoid a unified `forward` to prevent accidental calls
|
||||
that mix responsibilities.
|
||||
|
||||
To use the model:
|
||||
- Call `self.language_model(...)` for text embeddings / hidden states.
|
||||
- Call `self.tts_language_model(...)` for the TTS portion.
|
||||
- Use the dedicated inference class for combined generation logic.
|
||||
"""
|
||||
raise RuntimeError(
|
||||
"VibeVoiceStreamingModel.forward is intentionally disabled. "
|
||||
"Use `model.language_model(...)` or `model.tts_language_model(...)` instead."
|
||||
)
|
||||
|
||||
|
||||
AutoModel.register(VibeVoiceStreamingConfig, VibeVoiceStreamingModel)
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceStreamingPreTrainedModel",
|
||||
"VibeVoiceStreamingModel",
|
||||
]
|
||||
@@ -0,0 +1,906 @@
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union, Callable
|
||||
from tqdm import tqdm
|
||||
import inspect
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from transformers.models.auto import AutoModel, AutoModelForCausalLM
|
||||
from transformers.generation import GenerationMixin, GenerationConfig, LogitsProcessor, LogitsProcessorList, StoppingCriteriaList
|
||||
from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput
|
||||
from transformers import modeling_utils
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
|
||||
from transformers.utils import logging
|
||||
|
||||
from .modular_vibevoice_tokenizer import VibeVoiceTokenizerStreamingCache
|
||||
from .modular_vibevoice_diffusion_head import VibeVoiceDiffusionHead
|
||||
from vibevoice.schedule.dpm_solver import DPMSolverMultistepScheduler
|
||||
from .configuration_vibevoice_streaming import VibeVoiceStreamingConfig
|
||||
from .modular_vibevoice_text_tokenizer import VibeVoiceTextTokenizer, VibeVoiceTextTokenizerFast
|
||||
from .modeling_vibevoice_streaming import VibeVoiceStreamingPreTrainedModel, VibeVoiceStreamingModel, BinaryClassifier
|
||||
from .streamer import AudioStreamer, AsyncAudioStreamer
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
if not hasattr(modeling_utils, "ALL_PARALLEL_STYLES") or modeling_utils.ALL_PARALLEL_STYLES is None:
|
||||
modeling_utils.ALL_PARALLEL_STYLES = ["tp", "none", "colwise", "rowwise"]
|
||||
|
||||
TTS_TEXT_WINDOW_SIZE = 5
|
||||
TTS_SPEECH_WINDOW_SIZE = 6
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Transformers >= 4.57 Compatibility Layer
|
||||
# The cache system was refactored in transformers 4.57, requiring these helpers.
|
||||
# ============================================================================
|
||||
|
||||
class MockCacheLayer:
|
||||
"""
|
||||
Mock cache layer for transformers >= 4.57 compatibility.
|
||||
Provides the `layers` interface expected by DynamicCache in newer versions.
|
||||
"""
|
||||
|
||||
def __init__(self, key_cache, value_cache, parent_cache=None, layer_idx=0):
|
||||
self.key_cache = key_cache
|
||||
self.value_cache = value_cache
|
||||
self._parent_cache = parent_cache
|
||||
self._layer_idx = layer_idx
|
||||
|
||||
def get_mask_sizes(self, cache_position):
|
||||
"""Return KV length and offset for mask creation."""
|
||||
seq_length = self.key_cache.shape[2] if self.key_cache is not None else 0
|
||||
query_length = cache_position.shape[0]
|
||||
return seq_length + query_length, 0
|
||||
|
||||
def update(self, key_states, value_states, cache_kwargs=None):
|
||||
"""Update the cache with new key/value states."""
|
||||
if self._parent_cache is None:
|
||||
return self.key_cache, self.value_cache
|
||||
|
||||
parent = self._parent_cache
|
||||
idx = self._layer_idx
|
||||
|
||||
# Extend cache lists if needed
|
||||
while len(parent.key_cache) <= idx:
|
||||
parent.key_cache.append(None)
|
||||
parent.value_cache.append(None)
|
||||
|
||||
# Concatenate or initialize cache
|
||||
if parent.key_cache[idx] is not None:
|
||||
parent.key_cache[idx] = torch.cat([parent.key_cache[idx], key_states], dim=2)
|
||||
parent.value_cache[idx] = torch.cat([parent.value_cache[idx], value_states], dim=2)
|
||||
else:
|
||||
parent.key_cache[idx] = key_states
|
||||
parent.value_cache[idx] = value_states
|
||||
|
||||
# Update local references
|
||||
self.key_cache = parent.key_cache[idx]
|
||||
self.value_cache = parent.value_cache[idx]
|
||||
return self.key_cache, self.value_cache
|
||||
|
||||
|
||||
def _ensure_cache_has_layers(cache):
|
||||
"""
|
||||
Ensure the cache has all required attributes for transformers >= 4.57.
|
||||
Creates MockCacheLayer wrappers to provide the expected `layers` interface.
|
||||
"""
|
||||
if cache is None:
|
||||
return cache
|
||||
|
||||
# Add required attributes (skip if read-only)
|
||||
for attr, default in [('layer_class_to_replicate', None), ('offloading', False), ('is_compileable', False)]:
|
||||
if not hasattr(cache, attr):
|
||||
try:
|
||||
setattr(cache, attr, default)
|
||||
except AttributeError:
|
||||
pass
|
||||
|
||||
# Build layers list from key_cache/value_cache
|
||||
if hasattr(cache, 'key_cache') and hasattr(cache, 'value_cache'):
|
||||
try:
|
||||
cache.layers = [
|
||||
MockCacheLayer(cache.key_cache[i], cache.value_cache[i], parent_cache=cache, layer_idx=i)
|
||||
for i in range(len(cache.key_cache))
|
||||
]
|
||||
except AttributeError:
|
||||
pass
|
||||
elif not hasattr(cache, 'layers'):
|
||||
try:
|
||||
cache.layers = []
|
||||
except AttributeError:
|
||||
pass
|
||||
|
||||
return cache
|
||||
|
||||
|
||||
def _update_model_kwargs_for_generation(
|
||||
outputs: ModelOutput,
|
||||
model_kwargs: Dict[str, Any],
|
||||
num_new_tokens: int = 1,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Update model_kwargs after adding new tokens (supports multi-token windows).
|
||||
|
||||
Updates past_key_values, attention_mask, and cache_position for the next forward pass.
|
||||
"""
|
||||
model_kwargs["past_key_values"] = _ensure_cache_has_layers(outputs.past_key_values)
|
||||
|
||||
attention_mask = model_kwargs["attention_mask"]
|
||||
model_kwargs["attention_mask"] = torch.cat(
|
||||
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], num_new_tokens))], dim=-1
|
||||
)
|
||||
|
||||
cache_pos = model_kwargs["cache_position"]
|
||||
model_kwargs["cache_position"] = torch.arange(
|
||||
cache_pos[-1] + 1, cache_pos[-1] + num_new_tokens + 1, device=cache_pos.device
|
||||
)
|
||||
|
||||
return model_kwargs
|
||||
|
||||
|
||||
@dataclass
|
||||
class VibeVoiceCausalLMOutputWithPast(BaseModelOutputWithPast):
|
||||
logits: Optional[torch.FloatTensor] = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class VibeVoiceGenerationOutput(ModelOutput):
|
||||
"""
|
||||
Output type for VibeVoice generation.
|
||||
|
||||
Args:
|
||||
sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
||||
The generated sequences.
|
||||
speech_outputs (`List[torch.FloatTensor]`, *optional*):
|
||||
List of generated speech waveforms or latents for each speech segment.
|
||||
"""
|
||||
sequences: torch.LongTensor = None
|
||||
speech_outputs: Optional[List[torch.FloatTensor]] = None
|
||||
reach_max_step_sample: Optional[torch.BoolTensor] = None
|
||||
|
||||
|
||||
class VibeVoiceStreamingForConditionalGenerationInference(VibeVoiceStreamingPreTrainedModel, GenerationMixin):
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
|
||||
# Initialize the base model
|
||||
self.model = VibeVoiceStreamingModel(config)
|
||||
|
||||
# TTS generation EOS classifier
|
||||
self.tts_eos_classifier = BinaryClassifier(config.decoder_config.hidden_size)
|
||||
|
||||
# inference configuration
|
||||
self.ddpm_inference_steps = config.diffusion_head_config.ddpm_num_inference_steps
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
@property
|
||||
def noise_scheduler(self):
|
||||
return self.model.noise_scheduler
|
||||
|
||||
@property
|
||||
def prediction_head(self):
|
||||
return self.model.prediction_head
|
||||
|
||||
@property
|
||||
def speech_scaling_factor(self):
|
||||
return self.model.speech_scaling_factor
|
||||
|
||||
@property
|
||||
def speech_bias_factor(self):
|
||||
return self.model.speech_bias_factor
|
||||
|
||||
@property
|
||||
def acoustic_tokenizer(self):
|
||||
return self.model.acoustic_tokenizer
|
||||
|
||||
@property
|
||||
def acoustic_connector(self):
|
||||
return self.model.acoustic_connector
|
||||
|
||||
def tie_weights(self):
|
||||
"""
|
||||
Tie the weights between the input embeddings and the output embeddings.
|
||||
"""
|
||||
# Tie lm_head.weight to language_model.embed_tokens.weight
|
||||
if not getattr(self.config, 'tie_word_embeddings', False):
|
||||
return
|
||||
|
||||
if hasattr(self, 'lm_head') and hasattr(self.model.language_model, 'embed_tokens'):
|
||||
self.lm_head.weight = self.model.language_model.embed_tokens.weight
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.model.get_input_embeddings()
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.model.set_input_embeddings(value)
|
||||
|
||||
def get_output_embeddings(self):
|
||||
"""
|
||||
This model does not define an `lm_head` (vocabulary projection).
|
||||
"""
|
||||
return None
|
||||
|
||||
def set_output_embeddings(self, new_embeddings):
|
||||
"""
|
||||
No-op because there is no `lm_head`. Provided only to satisfy optional API calls.
|
||||
To enable, first create `self.lm_head` then allow assignment.
|
||||
"""
|
||||
raise RuntimeError("Output embeddings (lm_head) are not defined for this model. "
|
||||
"Create one before calling set_output_embeddings if needed.")
|
||||
|
||||
def set_speech_tokenizers(self, acoustic_tokenizer=None):
|
||||
"""Set the speech tokenizers used for encoding and decoding speech."""
|
||||
self.model.set_speech_tokenizers(acoustic_tokenizer)
|
||||
|
||||
def set_ddpm_inference_steps(self, num_steps=None):
|
||||
self.ddpm_inference_steps = num_steps or self.config.diffusion_head_config.ddpm_num_inference_steps
|
||||
|
||||
def prepare_inputs_for_generation(
|
||||
self,
|
||||
input_ids: torch.LongTensor,
|
||||
past_key_values=None,
|
||||
attention_mask=None,
|
||||
inputs_embeds=None,
|
||||
cache_position=None,
|
||||
**kwargs,
|
||||
):
|
||||
"""Prepare model inputs for generation (transformers >= 4.57 compatible)."""
|
||||
model_inputs = {"cache_position": cache_position}
|
||||
|
||||
# Slice inputs when using cache
|
||||
if past_key_values is not None:
|
||||
model_inputs["past_key_values"] = past_key_values
|
||||
if inputs_embeds is not None and input_ids.shape[1] == 0:
|
||||
inputs_embeds = inputs_embeds[:, -cache_position.shape[0]:]
|
||||
elif inputs_embeds is not None or (cache_position is not None and cache_position[-1] >= input_ids.shape[1]):
|
||||
input_ids = input_ids[:, -cache_position.shape[0]:]
|
||||
elif cache_position is not None and input_ids.shape[1] != cache_position.shape[0]:
|
||||
input_ids = input_ids[:, cache_position]
|
||||
|
||||
# Set input_ids or inputs_embeds
|
||||
use_embeds = inputs_embeds is not None and (
|
||||
past_key_values is None or (cache_position is not None and len(cache_position) == inputs_embeds.shape[1])
|
||||
)
|
||||
if use_embeds:
|
||||
model_inputs["input_ids"] = None
|
||||
model_inputs["inputs_embeds"] = inputs_embeds
|
||||
else:
|
||||
model_inputs["input_ids"] = input_ids.clone(memory_format=torch.contiguous_format) if input_ids is not None else None
|
||||
model_inputs["inputs_embeds"] = None
|
||||
|
||||
if attention_mask is not None:
|
||||
model_inputs["attention_mask"] = attention_mask
|
||||
|
||||
# Create position_ids from attention_mask
|
||||
if attention_mask is not None and kwargs.get("position_ids") is None:
|
||||
position_ids = attention_mask.long().cumsum(-1) - 1
|
||||
position_ids.masked_fill_(attention_mask == 0, 1)
|
||||
kwargs["position_ids"] = position_ids
|
||||
|
||||
# Slice position_ids when using cache
|
||||
if kwargs.get("position_ids") is not None:
|
||||
if past_key_values is not None:
|
||||
seq_len = model_inputs["inputs_embeds"].shape[1] if model_inputs.get("inputs_embeds") is not None else model_inputs["input_ids"].shape[1]
|
||||
model_inputs["position_ids"] = kwargs["position_ids"][:, -seq_len:].clone(memory_format=torch.contiguous_format)
|
||||
else:
|
||||
model_inputs["position_ids"] = kwargs.pop("position_ids").clone(memory_format=torch.contiguous_format)
|
||||
|
||||
# Forward remaining kwargs
|
||||
for key, value in kwargs.items():
|
||||
if key not in model_inputs:
|
||||
model_inputs[key] = value
|
||||
|
||||
model_inputs.pop("labels", None)
|
||||
return model_inputs
|
||||
|
||||
def _update_model_kwargs_for_generation(
|
||||
self,
|
||||
outputs,
|
||||
model_kwargs,
|
||||
is_encoder_decoder=False,
|
||||
num_new_tokens=1,
|
||||
):
|
||||
"""Override to ensure cache compatibility with transformers >= 4.57."""
|
||||
model_kwargs = super()._update_model_kwargs_for_generation(
|
||||
outputs, model_kwargs, is_encoder_decoder=is_encoder_decoder, num_new_tokens=num_new_tokens
|
||||
)
|
||||
if "past_key_values" in model_kwargs:
|
||||
model_kwargs["past_key_values"] = _ensure_cache_has_layers(model_kwargs["past_key_values"])
|
||||
return model_kwargs
|
||||
|
||||
def _init_cache_for_generation(self, generation_config, model_kwargs, batch_size, max_cache_length, device):
|
||||
"""
|
||||
Initialize cache for generation, handling different transformers versions.
|
||||
For transformers >= 4.57, returns None to let the model create the cache dynamically.
|
||||
"""
|
||||
try:
|
||||
from transformers.cache_utils import DynamicCache
|
||||
sig = inspect.signature(DynamicCache.__init__)
|
||||
if 'config' in sig.parameters:
|
||||
# transformers >= 4.57: let model handle cache creation
|
||||
return None
|
||||
else:
|
||||
# Older versions: use parent method
|
||||
prep_sig = inspect.signature(self._prepare_cache_for_generation)
|
||||
if 'device' in prep_sig.parameters:
|
||||
self._prepare_cache_for_generation(generation_config, model_kwargs, None, batch_size, max_cache_length, device)
|
||||
else:
|
||||
self._prepare_cache_for_generation(generation_config, model_kwargs, None, batch_size, max_cache_length)
|
||||
return model_kwargs.get("past_key_values")
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
# @can_return_tuple
|
||||
def forward_lm(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs,
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
"""
|
||||
Single pass of the base text LM.
|
||||
|
||||
- Builds embeddings if `inputs_embeds` not provided.
|
||||
- Uses (and returns) `past_key_values` when `use_cache=True`.
|
||||
- No loss / no lm_head / no speech logic.
|
||||
|
||||
Args:
|
||||
input_ids: (B, S) token ids.
|
||||
attention_mask: (B, S) mask.
|
||||
past_key_values: cache from previous steps.
|
||||
cache_position: positions for cached tokens.
|
||||
labels: unsupported (will raise).
|
||||
|
||||
Returns:
|
||||
BaseModelOutputWithPast with `last_hidden_state` and `past_key_values`.
|
||||
"""
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# Get embeddings
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.model.get_input_embeddings()(input_ids)
|
||||
|
||||
outputs = self.model.language_model(
|
||||
inputs_embeds=inputs_embeds,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
hidden_states = outputs[0] if not return_dict else outputs.last_hidden_state
|
||||
|
||||
if labels is not None:
|
||||
raise NotImplementedError("Loss computation is not implemented in this version.")
|
||||
|
||||
return BaseModelOutputWithPast(
|
||||
past_key_values=outputs.past_key_values,
|
||||
last_hidden_state=hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
# @can_return_tuple
|
||||
def forward_tts_lm(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
lm_last_hidden_state: Optional[torch.FloatTensor] = None,
|
||||
tts_text_masks: Optional[torch.BoolTensor] = None,
|
||||
**kwargs,
|
||||
) -> Union[Tuple, VibeVoiceCausalLMOutputWithPast]:
|
||||
"""
|
||||
Single pass of the TTS LM.
|
||||
|
||||
- Overwrites tail embeddings with `lm_last_hidden_state`.
|
||||
- Adds type embedding via `tts_text_masks` (1=text, 0=speech).
|
||||
- Predicts EOS from last hidden state (binary classifier).
|
||||
- No loss / no full acoustic decoding here.
|
||||
|
||||
Args:
|
||||
input_ids: (B, S) token ids.
|
||||
attention_mask: (B, S) mask.
|
||||
lm_last_hidden_state: (B, K, H) hidden states to splice into the tail.
|
||||
tts_text_masks: (B, 1) mask marking current position as text(1)/speech(0).
|
||||
past_key_values: cache from previous TTS steps.
|
||||
cache_position: positions for cached tokens.
|
||||
labels: unsupported (will raise).
|
||||
|
||||
Returns:
|
||||
VibeVoiceCausalLMOutputWithPast with `logits` (EOS), `last_hidden_state`, `past_key_values`.
|
||||
"""
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# Get embeddings
|
||||
if inputs_embeds is None:
|
||||
# Will be replaced with lm_last_hidden_state
|
||||
inputs_embeds = self.model.get_input_embeddings()(input_ids)
|
||||
|
||||
# Replace the last part of inputs_embeds with lm_last_hidden_state
|
||||
start_idx = inputs_embeds.shape[1] - lm_last_hidden_state.shape[1]
|
||||
inputs_embeds[:, start_idx:, :] = lm_last_hidden_state
|
||||
|
||||
# Adds type embedding via `tts_text_masks`.
|
||||
inputs_embeds = inputs_embeds + self.model.tts_input_types(tts_text_masks.long())
|
||||
|
||||
outputs = self.model.tts_language_model(
|
||||
inputs_embeds=inputs_embeds,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
hidden_states = outputs[0] if not return_dict else outputs.last_hidden_state
|
||||
logits = self.tts_eos_classifier(hidden_states[:, -1, :])
|
||||
|
||||
if labels is not None:
|
||||
raise NotImplementedError("Loss computation is not implemented in this version.")
|
||||
|
||||
return VibeVoiceCausalLMOutputWithPast(
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
last_hidden_state=hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
"""
|
||||
Unified forward is intentionally disabled.
|
||||
|
||||
Reasons:
|
||||
1. The inference pipeline is staged: base text LM, then TTS LM, plus streaming & diffusion handled in `generate`.
|
||||
2. A monolithic call would hide required sequencing (prefill, window stepping, speech diffusion sampling).
|
||||
|
||||
Use instead:
|
||||
- self.forward_lm(...) for a base text LM step (prefill or incremental).
|
||||
- self.forward_tts_lm(...) for a single TTS LM step (needs LM hidden states).
|
||||
- self.generate(...) for full streaming (text + speech + diffusion + audio assembly).
|
||||
|
||||
Raises:
|
||||
RuntimeError: Always (by design).
|
||||
"""
|
||||
raise RuntimeError(
|
||||
"Unified forward is disabled. Use `forward_lm`, `forward_tts_lm`, or `generate` instead."
|
||||
)
|
||||
|
||||
def _build_generate_config_model_kwargs(self, generation_config, inputs, tokenizer, return_processors=False, **kwargs):
|
||||
if generation_config is None:
|
||||
generation_config = GenerationConfig(
|
||||
bos_token_id=tokenizer.bos_token_id,
|
||||
eos_token_id=tokenizer.eos_token_id,
|
||||
pad_token_id = tokenizer.pad_token_id
|
||||
)
|
||||
else:
|
||||
generation_config = GenerationConfig(
|
||||
**generation_config,
|
||||
bos_token_id=tokenizer.bos_token_id,
|
||||
eos_token_id=tokenizer.eos_token_id,
|
||||
pad_token_id = tokenizer.pad_token_id
|
||||
)
|
||||
|
||||
generation_config, model_kwargs = self._prepare_generation_config(
|
||||
generation_config,
|
||||
True,
|
||||
speech_start_id=tokenizer.speech_start_id,
|
||||
speech_end_id=tokenizer.speech_end_id,
|
||||
speech_diffusion_id=tokenizer.speech_diffusion_id,
|
||||
**kwargs
|
||||
)
|
||||
generation_config.speech_start_id = tokenizer.speech_start_id
|
||||
generation_config.speech_end_id = tokenizer.speech_end_id
|
||||
generation_config.speech_diffusion_id = tokenizer.speech_diffusion_id
|
||||
|
||||
inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(inputs, generation_config.bos_token_id, model_kwargs)
|
||||
batch_size = inputs_tensor.shape[0]
|
||||
device = self.device
|
||||
|
||||
self._prepare_special_tokens(generation_config, True, device=device)
|
||||
generation_config.use_cache = True
|
||||
model_kwargs["use_cache"] = generation_config.use_cache
|
||||
input_ids = inputs_tensor.to(self.device)
|
||||
|
||||
input_ids_length = input_ids.shape[1]
|
||||
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
|
||||
has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
|
||||
generation_config = self._prepare_generated_length(
|
||||
generation_config=generation_config,
|
||||
has_default_max_length=has_default_max_length,
|
||||
has_default_min_length=has_default_min_length,
|
||||
model_input_name=model_input_name,
|
||||
inputs_tensor=inputs_tensor,
|
||||
input_ids_length=input_ids_length,
|
||||
)
|
||||
|
||||
max_cache_length = generation_config.max_length - 1
|
||||
# Handle cache initialization for different transformers versions
|
||||
model_kwargs["past_key_values"] = self._init_cache_for_generation(
|
||||
generation_config, model_kwargs, batch_size, max_cache_length, device
|
||||
)
|
||||
model_kwargs['cache_position'] = torch.arange(input_ids_length, device=device, dtype=torch.long)
|
||||
for k, v in model_kwargs.items():
|
||||
if isinstance(v, torch.Tensor):
|
||||
model_kwargs[k] = v.to(device=device)
|
||||
|
||||
if return_processors:
|
||||
logits_processor = self._get_logits_processor(
|
||||
generation_config=generation_config,
|
||||
input_ids_seq_length=input_ids_length,
|
||||
encoder_input_ids=inputs_tensor,
|
||||
prefix_allowed_tokens_fn=None,
|
||||
logits_processor=LogitsProcessorList(),
|
||||
device=inputs_tensor.device,
|
||||
model_kwargs=model_kwargs,
|
||||
)
|
||||
|
||||
stopping_criteria = self._get_stopping_criteria(generation_config=generation_config, stopping_criteria=StoppingCriteriaList())
|
||||
|
||||
return generation_config, model_kwargs, input_ids, logits_processor, stopping_criteria
|
||||
else:
|
||||
return generation_config, model_kwargs, input_ids
|
||||
|
||||
@torch.no_grad()
|
||||
def generate(
|
||||
self,
|
||||
inputs: Optional[torch.Tensor] = None,
|
||||
generation_config: Optional[GenerationConfig] = None,
|
||||
logits_processor: Optional[LogitsProcessorList] = None,
|
||||
stopping_criteria: Optional[StoppingCriteriaList] = None,
|
||||
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
|
||||
synced_gpus: Optional[bool] = None,
|
||||
assistant_model: Optional["PreTrainedModel"] = None,
|
||||
audio_streamer: Optional[Union[AudioStreamer, AsyncAudioStreamer]] = None,
|
||||
negative_prompt_ids: Optional[torch.Tensor] = None,
|
||||
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
|
||||
speech_tensors: Optional[torch.FloatTensor] = None,
|
||||
speech_masks: Optional[torch.BoolTensor] = None,
|
||||
speech_input_mask: Optional[torch.BoolTensor] = None,
|
||||
tts_text_ids: Optional[torch.LongTensor] = None,
|
||||
return_speech: bool = True,
|
||||
cfg_scale: float = 1.0,
|
||||
stop_check_fn: Optional[Callable[[], bool]] = None,
|
||||
**kwargs,
|
||||
) -> Union[torch.LongTensor, VibeVoiceGenerationOutput]:
|
||||
"""
|
||||
Text is fed in small windows (dynamic slicing of `tts_text_ids`), which enables streaming text input: you don’t need the full text upfront. After each text window, a loop samples several speech latents (diffusion). The interleaved text encoding + speech generation enables streaming text input and realtime speech output.
|
||||
The function only supports batch size = 1 currently.
|
||||
|
||||
- Windowed text prefill → incremental LM + TTS LM updates.
|
||||
- Interleave speech token diffusion sampling (`sample_speech_tokens`).
|
||||
- Stops on EOS (binary classifier) or max length / external `stop_check_fn`.
|
||||
- Returns final token `sequences` and (optionally) concatenated speech audio.
|
||||
|
||||
Args (selected):
|
||||
tts_text_ids: Full text tokens to stream in windows.
|
||||
audio_streamer: If provided, emits audio chunks during generation.
|
||||
cfg_scale: Classifier-free guidance scale for speech diffusion.
|
||||
return_speech: If False, skips audio decode concatenation.
|
||||
stop_check_fn: External early-stop hook (returns True to halt).
|
||||
|
||||
Returns:
|
||||
VibeVoiceGenerationOutput with:
|
||||
- sequences: final token ids
|
||||
- speech_outputs: list of concatenated audio tensors (or None)
|
||||
- reach_max_step_sample: flags for samples stopped by max length
|
||||
"""
|
||||
# 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
|
||||
tokenizer = kwargs.pop("tokenizer", None)
|
||||
neg_text_input_id = tokenizer.convert_tokens_to_ids("<|image_pad|>")
|
||||
|
||||
tts_lm_input_ids = kwargs.pop("tts_lm_input_ids", None)
|
||||
tts_lm_attention_mask = kwargs.pop("tts_lm_attention_mask", None)
|
||||
# all_prefilled_outputs: cached prefilled prompt outputs for lm, tts_lm, neg_lm, neg_tts_lm
|
||||
all_prefilled_outputs = kwargs.pop("all_prefilled_outputs", None)
|
||||
tts_text_ids = tts_text_ids.to(self.device)
|
||||
|
||||
if kwargs.get('max_new_tokens', None) is None:
|
||||
kwargs['max_new_tokens'] = self.config.decoder_config.max_position_embeddings - tts_lm_input_ids.shape[-1]
|
||||
|
||||
generation_config, model_kwargs, input_ids, logits_processor, stopping_criteria = self._build_generate_config_model_kwargs(
|
||||
generation_config, inputs, tokenizer, return_processors=True, **kwargs
|
||||
)
|
||||
|
||||
negative_kwargs = {
|
||||
'input_ids': torch.full((kwargs['input_ids'].shape[0], 1), neg_text_input_id, dtype=torch.long, device=kwargs['input_ids'].device),
|
||||
'attention_mask': torch.ones((kwargs['input_ids'].shape[0], 1), dtype=torch.long, device=kwargs['input_ids'].device),
|
||||
'max_new_tokens': kwargs.get('max_new_tokens', 100)
|
||||
}
|
||||
negative_generation_config, negative_model_kwargs, negative_input_ids = self._build_generate_config_model_kwargs(
|
||||
None, None, tokenizer, return_processors=False, **negative_kwargs
|
||||
)
|
||||
|
||||
tts_lm_kwargs = {
|
||||
'input_ids': tts_lm_input_ids,
|
||||
'attention_mask': tts_lm_attention_mask,
|
||||
'max_new_tokens': kwargs.get('max_new_tokens', 100)
|
||||
}
|
||||
tts_lm_generation_config, tts_lm_model_kwargs, tts_lm_input_ids = self._build_generate_config_model_kwargs(
|
||||
None, None, tokenizer, return_processors=False, **tts_lm_kwargs
|
||||
)
|
||||
|
||||
tts_lm_negative_kwargs = {
|
||||
'input_ids': torch.full((kwargs['input_ids'].shape[0], 1), neg_text_input_id, dtype=torch.long, device=kwargs['input_ids'].device),
|
||||
'attention_mask': torch.ones((kwargs['input_ids'].shape[0], 1), dtype=torch.long, device=kwargs['input_ids'].device),
|
||||
'max_new_tokens': kwargs.get('max_new_tokens', 100)
|
||||
}
|
||||
tts_lm_negative_generation_config, tts_lm_negative_model_kwargs, tts_lm_negative_input_ids = self._build_generate_config_model_kwargs(
|
||||
None, None, tokenizer, return_processors=False, **tts_lm_negative_kwargs
|
||||
)
|
||||
|
||||
acoustic_cache = VibeVoiceTokenizerStreamingCache()
|
||||
batch_size = input_ids.shape[0]
|
||||
assert batch_size == 1, "Currently only supports batch size == 1"
|
||||
device = input_ids.device
|
||||
finished_tags = torch.zeros(batch_size, dtype=torch.bool, device=device)
|
||||
verbose = kwargs.get("verbose", False)
|
||||
|
||||
# Initialize audio chunks storage for each sample
|
||||
audio_chunks = [[] for _ in range(batch_size)]
|
||||
tts_text_window_index = 0
|
||||
reach_max_step_sample = torch.zeros(batch_size, dtype=torch.bool, device=device)
|
||||
first_text_window_size = TTS_TEXT_WINDOW_SIZE if tts_text_ids.shape[1] >= TTS_TEXT_WINDOW_SIZE else tts_text_ids.shape[1]
|
||||
|
||||
outputs = all_prefilled_outputs["lm"]
|
||||
tts_lm_outputs = all_prefilled_outputs["tts_lm"]
|
||||
negative_outputs = all_prefilled_outputs["neg_lm"]
|
||||
tts_lm_negative_outputs = all_prefilled_outputs["neg_tts_lm"]
|
||||
|
||||
model_kwargs = _update_model_kwargs_for_generation(
|
||||
outputs, model_kwargs, num_new_tokens=first_text_window_size,
|
||||
)
|
||||
tts_lm_model_kwargs = _update_model_kwargs_for_generation(
|
||||
tts_lm_outputs, tts_lm_model_kwargs, num_new_tokens=first_text_window_size,
|
||||
)
|
||||
negative_model_kwargs = self._update_model_kwargs_for_generation(
|
||||
negative_outputs, negative_model_kwargs, is_encoder_decoder=False,
|
||||
)
|
||||
tts_lm_negative_model_kwargs = self._update_model_kwargs_for_generation(
|
||||
tts_lm_negative_outputs, tts_lm_negative_model_kwargs, is_encoder_decoder=False,
|
||||
)
|
||||
|
||||
step = tts_lm_input_ids.shape[1]
|
||||
total_generated_speech_tokens = 0
|
||||
total_prefilled_text_tokens = 0
|
||||
if kwargs.get("show_progress_bar", True):
|
||||
progress_bar = tqdm(
|
||||
total=tts_lm_generation_config.max_length,
|
||||
desc=f"Prefilled {step} tokens, current step ({step} / {tts_lm_generation_config.max_length})",
|
||||
initial=step,
|
||||
leave=False
|
||||
)
|
||||
else:
|
||||
progress_bar = None
|
||||
|
||||
while True:
|
||||
# Check for external stop signal
|
||||
if stop_check_fn is not None and stop_check_fn():
|
||||
if verbose:
|
||||
print(f"Generation stopped externally at step {step + 1}")
|
||||
# End the audio streamer if it exists
|
||||
if audio_streamer is not None:
|
||||
audio_streamer.end()
|
||||
break
|
||||
|
||||
# # Check if audio_streamer has been ended (stopped externally)
|
||||
# if audio_streamer is not None and hasattr(audio_streamer, 'finished_flags'):
|
||||
# if any(audio_streamer.finished_flags):
|
||||
# if verbose:
|
||||
# print(f"Audio generation stopped externally at step {step + 1}")
|
||||
# break
|
||||
|
||||
if finished_tags.all():
|
||||
if hasattr(progress_bar, 'set_description'):
|
||||
progress_bar.set_description("Generation complete")
|
||||
break
|
||||
|
||||
cur_input_tts_text_ids = tts_text_ids[:, tts_text_window_index*TTS_TEXT_WINDOW_SIZE:(tts_text_window_index+1)*TTS_TEXT_WINDOW_SIZE]
|
||||
next_text_window_size = tts_text_ids[:, (tts_text_window_index+1)*TTS_TEXT_WINDOW_SIZE:(tts_text_window_index+2)*TTS_TEXT_WINDOW_SIZE].shape[1]
|
||||
tts_text_window_index += 1
|
||||
|
||||
if cur_input_tts_text_ids.shape[1] > 0:
|
||||
input_ids = torch.cat([input_ids, cur_input_tts_text_ids], dim=-1)
|
||||
tts_lm_input_ids = torch.cat([tts_lm_input_ids, cur_input_tts_text_ids], dim=-1)
|
||||
|
||||
if tts_lm_input_ids.shape[1] > tts_lm_generation_config.max_length:
|
||||
if verbose:
|
||||
print(f"Reached maximum generation length {generation_config.max_length}, stopped it.")
|
||||
reached_samples = torch.arange(batch_size, device=device)[~finished_tags]
|
||||
if reached_samples.numel() > 0:
|
||||
reach_max_step_sample[reached_samples] = True
|
||||
break
|
||||
|
||||
step += cur_input_tts_text_ids.shape[1]
|
||||
total_prefilled_text_tokens += cur_input_tts_text_ids.shape[1]
|
||||
if progress_bar is not None:
|
||||
progress_bar.update(cur_input_tts_text_ids.shape[1])
|
||||
progress_bar.set_description(f"Prefilled {total_prefilled_text_tokens} text tokens, generated {total_generated_speech_tokens} speech tokens, current step ({step} / {tts_lm_generation_config.max_length})")
|
||||
|
||||
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
|
||||
# Forward pass through the model
|
||||
outputs = self.forward_lm(
|
||||
**model_inputs, return_dict=True, output_attentions=False, output_hidden_states=False,
|
||||
)
|
||||
model_kwargs = _update_model_kwargs_for_generation(
|
||||
outputs, model_kwargs, num_new_tokens=next_text_window_size,
|
||||
)
|
||||
|
||||
tts_lm_model_inputs = self.prepare_inputs_for_generation(tts_lm_input_ids, **tts_lm_model_kwargs)
|
||||
tts_lm_additional_inputs = {
|
||||
"tts_text_masks": torch.ones_like(tts_lm_input_ids[:, -1:]),
|
||||
"lm_last_hidden_state": outputs.last_hidden_state,
|
||||
}
|
||||
# Forward pass through the model
|
||||
tts_lm_outputs = self.forward_tts_lm(
|
||||
**tts_lm_model_inputs, **tts_lm_additional_inputs, return_dict=True, output_attentions=False, output_hidden_states=False,
|
||||
)
|
||||
tts_lm_model_kwargs = self._update_model_kwargs_for_generation(
|
||||
tts_lm_outputs, tts_lm_model_kwargs, is_encoder_decoder=False,
|
||||
)
|
||||
|
||||
diffusion_indices = torch.LongTensor([0])
|
||||
for cur_speech_index in range(TTS_SPEECH_WINDOW_SIZE):
|
||||
positive_condition = tts_lm_outputs.last_hidden_state[diffusion_indices, -1, :]
|
||||
negative_condition = tts_lm_negative_outputs.last_hidden_state[diffusion_indices, -1, :]
|
||||
|
||||
speech_latent = self.sample_speech_tokens(
|
||||
positive_condition,
|
||||
negative_condition,
|
||||
cfg_scale=cfg_scale,
|
||||
).unsqueeze(1)
|
||||
|
||||
# Decode acoustic latent to audio using acoustic streaming cache
|
||||
scaled_latent = speech_latent / self.model.speech_scaling_factor.to(speech_latent.device) - self.model.speech_bias_factor.to(speech_latent.device)
|
||||
audio_chunk = self.model.acoustic_tokenizer.decode(
|
||||
scaled_latent.to(self.model.acoustic_tokenizer.device),
|
||||
cache=acoustic_cache, # Use acoustic-specific cache
|
||||
sample_indices=diffusion_indices.to(self.model.acoustic_tokenizer.device),
|
||||
use_cache=True,
|
||||
debug=False
|
||||
)
|
||||
|
||||
# Store audio chunks for each sample
|
||||
for i, sample_idx in enumerate(diffusion_indices):
|
||||
idx = sample_idx.item()
|
||||
# Only append audio chunk if the sample is not finished
|
||||
if not finished_tags[idx]:
|
||||
audio_chunks[idx].append(audio_chunk[i])
|
||||
|
||||
# Add streaming support here
|
||||
if audio_streamer is not None:
|
||||
# Stream the audio chunks immediately
|
||||
audio_streamer.put(audio_chunk, diffusion_indices)
|
||||
|
||||
acoustic_embed = self.model.acoustic_connector(speech_latent)
|
||||
tts_lm_input_ids = torch.cat([tts_lm_input_ids, torch.ones_like(tts_lm_input_ids[:, -1:])], dim=-1)
|
||||
|
||||
if tts_lm_input_ids.shape[1] > tts_lm_generation_config.max_length:
|
||||
break
|
||||
|
||||
step += 1
|
||||
total_generated_speech_tokens += 1
|
||||
if progress_bar is not None:
|
||||
progress_bar.update(1)
|
||||
progress_bar.set_description(f"Prefilled {total_prefilled_text_tokens} text tokens, generated {total_generated_speech_tokens} speech tokens, current step ({step} / {tts_lm_generation_config.max_length})")
|
||||
|
||||
tts_lm_model_inputs = self.prepare_inputs_for_generation(tts_lm_input_ids, **tts_lm_model_kwargs)
|
||||
tts_lm_additional_inputs = {
|
||||
"tts_text_masks": torch.zeros_like(tts_lm_input_ids[:, -1:]),
|
||||
"lm_last_hidden_state": acoustic_embed,
|
||||
}
|
||||
# Forward pass through the model
|
||||
tts_lm_outputs = self.forward_tts_lm(
|
||||
**tts_lm_model_inputs, **tts_lm_additional_inputs, return_dict=True, output_attentions=False, output_hidden_states=False,
|
||||
)
|
||||
if cur_speech_index == TTS_SPEECH_WINDOW_SIZE - 1 and next_text_window_size > 0:
|
||||
tts_lm_model_kwargs = _update_model_kwargs_for_generation(
|
||||
tts_lm_outputs, tts_lm_model_kwargs, num_new_tokens=next_text_window_size,
|
||||
)
|
||||
else:
|
||||
tts_lm_model_kwargs = self._update_model_kwargs_for_generation(
|
||||
tts_lm_outputs, tts_lm_model_kwargs, is_encoder_decoder=False,
|
||||
)
|
||||
|
||||
tts_lm_negative_input_ids = torch.cat([tts_lm_negative_input_ids, torch.ones_like(tts_lm_input_ids[:, -1:])], dim=-1)
|
||||
tts_lm_negative_model_inputs = self.prepare_inputs_for_generation(tts_lm_negative_input_ids, **tts_lm_negative_model_kwargs)
|
||||
# Forward negative pass through the model
|
||||
tts_lm_negative_additional_inputs = {
|
||||
"tts_text_masks": torch.zeros_like(tts_lm_negative_input_ids[:, -1:]),
|
||||
"lm_last_hidden_state": acoustic_embed,
|
||||
}
|
||||
tts_lm_negative_outputs = self.forward_tts_lm(
|
||||
**tts_lm_negative_model_inputs, **tts_lm_negative_additional_inputs, return_dict=True, output_attentions=False, output_hidden_states=False,
|
||||
)
|
||||
tts_lm_negative_model_kwargs = self._update_model_kwargs_for_generation(
|
||||
tts_lm_negative_outputs, tts_lm_negative_model_kwargs, is_encoder_decoder=False,
|
||||
)
|
||||
|
||||
tts_eos_logits = torch.sigmoid(self.tts_eos_classifier(tts_lm_outputs.last_hidden_state[diffusion_indices, -1, :]))
|
||||
if tts_eos_logits[0].item() > 0.5:
|
||||
# If EOS token is predicted, we can stop generation for this sample
|
||||
finished_tags[diffusion_indices] = True
|
||||
if audio_streamer is not None:
|
||||
audio_streamer.end(diffusion_indices)
|
||||
|
||||
if tts_lm_input_ids.shape[1] > tts_lm_generation_config.max_length:
|
||||
if verbose:
|
||||
print(f"Reached maximum generation length {tts_lm_generation_config.max_length}, stopped it.")
|
||||
reached_samples = torch.arange(batch_size, device=device)[~finished_tags]
|
||||
if reached_samples.numel() > 0:
|
||||
reach_max_step_sample[reached_samples] = True
|
||||
break
|
||||
|
||||
if audio_streamer is not None:
|
||||
audio_streamer.end()
|
||||
|
||||
# Concatenate audio chunks for each sample
|
||||
final_audio_outputs = []
|
||||
for sample_chunks in audio_chunks:
|
||||
if sample_chunks:
|
||||
# Concatenate all chunks along the time dimension (assumed to be the last dimension)
|
||||
concatenated_audio = torch.cat(sample_chunks, dim=-1)
|
||||
final_audio_outputs.append(concatenated_audio)
|
||||
else:
|
||||
# If no audio was generated for this sample, append None
|
||||
final_audio_outputs.append(None)
|
||||
|
||||
if reach_max_step_sample is not None and reach_max_step_sample.any():
|
||||
print(f"Reached maximum generation length {tts_lm_generation_config.max_length}, stopped it.")
|
||||
|
||||
return VibeVoiceGenerationOutput(
|
||||
sequences=tts_lm_input_ids,
|
||||
speech_outputs=final_audio_outputs if return_speech else None,
|
||||
reach_max_step_sample=reach_max_step_sample,
|
||||
)
|
||||
|
||||
@torch.no_grad()
|
||||
def sample_speech_tokens(self, condition, neg_condition, cfg_scale=3.0):
|
||||
self.model.noise_scheduler.set_timesteps(self.ddpm_inference_steps)
|
||||
condition = torch.cat([condition, neg_condition], dim=0).to(self.model.prediction_head.device)
|
||||
speech = torch.randn(condition.shape[0], self.config.acoustic_vae_dim).to(condition)
|
||||
for t in self.model.noise_scheduler.timesteps:
|
||||
half = speech[: len(speech) // 2]
|
||||
combined = torch.cat([half, half], dim=0)
|
||||
eps = self.model.prediction_head(combined, t.repeat(combined.shape[0]).to(combined), condition=condition)
|
||||
cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
|
||||
half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
|
||||
eps = torch.cat([half_eps, half_eps], dim=0)
|
||||
speech = self.model.noise_scheduler.step(eps, t, speech).prev_sample
|
||||
return speech[: len(speech) // 2]
|
||||
|
||||
|
||||
AutoModelForCausalLM.register(VibeVoiceStreamingConfig, VibeVoiceStreamingForConditionalGenerationInference)
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceStreamingForConditionalGenerationInference",
|
||||
]
|
||||
@@ -0,0 +1,287 @@
|
||||
import math
|
||||
from typing import Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from transformers.models.auto import AutoModel
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
# from transformers.modeling_layers import GradientCheckpointingLayer
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.utils import logging
|
||||
|
||||
from .configuration_vibevoice import VibeVoiceDiffusionHeadConfig
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class RMSNorm(nn.Module):
|
||||
def __init__(self, dim: int, eps: float = 1e-6, elementwise_affine=True, memory_efficient=False):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.eps = eps
|
||||
self.elementwise_affine = elementwise_affine
|
||||
if self.elementwise_affine:
|
||||
self.weight = nn.Parameter(torch.ones(dim))
|
||||
else:
|
||||
self.register_parameter('weight', None)
|
||||
|
||||
def _norm(self, x):
|
||||
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
|
||||
|
||||
def forward(self, x):
|
||||
output = self._norm(x.float()).type_as(x)
|
||||
if self.weight is not None:
|
||||
output = output * self.weight
|
||||
return output
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return f'dim={self.dim}, eps={self.eps}, elementwise_affine={self.elementwise_affine}'
|
||||
|
||||
def modulate(x, shift, scale):
|
||||
"""Apply modulation to input tensor."""
|
||||
return x * (1 + scale) + shift
|
||||
|
||||
|
||||
class TimestepEmbedder(nn.Module):
|
||||
"""
|
||||
Embeds scalar timesteps into vector representations.
|
||||
|
||||
Args:
|
||||
hidden_size (`int`): Size of the output embedding
|
||||
frequency_embedding_size (`int`, optional): Size of the intermediate frequency embedding
|
||||
"""
|
||||
def __init__(self, hidden_size, frequency_embedding_size=256):
|
||||
super().__init__()
|
||||
self.mlp = nn.Sequential(
|
||||
nn.Linear(frequency_embedding_size, hidden_size, bias=False),
|
||||
# nn.SiLU(),
|
||||
ACT2FN['silu'],
|
||||
nn.Linear(hidden_size, hidden_size, bias=False),
|
||||
)
|
||||
self.frequency_embedding_size = frequency_embedding_size
|
||||
|
||||
@staticmethod
|
||||
def timestep_embedding(t, dim, max_period=10000):
|
||||
"""
|
||||
Create sinusoidal timestep embeddings.
|
||||
|
||||
Args:
|
||||
t (`torch.Tensor`): A 1-D Tensor of N indices, one per batch element.
|
||||
These may be fractional.
|
||||
dim (`int`): The dimension of the output.
|
||||
max_period (`int`, optional): Controls the minimum frequency of the embeddings.
|
||||
|
||||
Returns:
|
||||
`torch.Tensor`: An [N, D] Tensor of positional embeddings.
|
||||
"""
|
||||
half = dim // 2
|
||||
freqs = torch.exp(
|
||||
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
|
||||
).to(t.device)
|
||||
args = t[:, None].float() * freqs[None]
|
||||
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
||||
if dim % 2:
|
||||
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
||||
return embedding.to(t.dtype)
|
||||
|
||||
def forward(self, t):
|
||||
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
|
||||
t_emb = self.mlp(t_freq)
|
||||
return t_emb
|
||||
|
||||
|
||||
class FeedForwardNetwork(nn.Module):
|
||||
"""
|
||||
Standard feed-forward network with SwiGLU activation.
|
||||
|
||||
Args:
|
||||
embed_dim (`int`): Input dimension
|
||||
ffn_dim (`int`): Hidden dimension
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
embed_dim,
|
||||
ffn_dim,
|
||||
):
|
||||
super().__init__()
|
||||
self.embed_dim = embed_dim
|
||||
self.gate_proj = nn.Linear(self.embed_dim, ffn_dim, bias=False)
|
||||
self.up_proj = nn.Linear(self.embed_dim, ffn_dim, bias=False)
|
||||
self.down_proj = nn.Linear(ffn_dim, self.embed_dim, bias=False)
|
||||
self.act_fn = ACT2FN['silu'] # Using SiLU as the activation function
|
||||
|
||||
def forward(self, x):
|
||||
gate = self.gate_proj(x)
|
||||
up = self.up_proj(x)
|
||||
|
||||
# SwiGLU activation
|
||||
# gate = F.silu(gate)
|
||||
gate = self.act_fn(gate)
|
||||
return self.down_proj(gate * up)
|
||||
|
||||
|
||||
class HeadLayer(nn.Module):
|
||||
"""
|
||||
A layer in the diffusion head.
|
||||
|
||||
Args:
|
||||
embed_dim (`int`): Input dimension
|
||||
ffn_dim (`int`): Hidden dimension
|
||||
cond_dim (`int`): Condition embedding dimension
|
||||
norm_eps (`float`, optional): Epsilon for normalization
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
embed_dim,
|
||||
ffn_dim,
|
||||
cond_dim,
|
||||
norm_eps=1e-5,
|
||||
):
|
||||
super().__init__()
|
||||
self.embed_dim = embed_dim
|
||||
self.cond_dim = cond_dim
|
||||
self.ffn_dim = ffn_dim
|
||||
self.ffn = FeedForwardNetwork(
|
||||
self.embed_dim,
|
||||
self.ffn_dim,
|
||||
)
|
||||
self.norm = RMSNorm(self.embed_dim, eps=norm_eps)
|
||||
self.adaLN_modulation = nn.Sequential(
|
||||
# nn.SiLU(),
|
||||
ACT2FN['silu'],
|
||||
nn.Linear(cond_dim, 3 * self.embed_dim, bias=False)
|
||||
)
|
||||
|
||||
def forward(self, x, c):
|
||||
shift_ffn, scale_ffn, gate_ffn = self.adaLN_modulation(c).chunk(3, dim=-1)
|
||||
x = x + gate_ffn * self.ffn(modulate(self.norm(x), shift_ffn, scale_ffn))
|
||||
return x
|
||||
|
||||
|
||||
class FinalLayer(nn.Module):
|
||||
"""
|
||||
Final layer in the diffusion head.
|
||||
|
||||
Args:
|
||||
hidden_size (`int`): Input dimension
|
||||
output_size (`int`): Output dimension
|
||||
cond_size (`int`): Condition embedding dimension
|
||||
norm_eps (`float`, optional): Epsilon for normalization
|
||||
"""
|
||||
def __init__(self, hidden_size, output_size, cond_size, norm_eps=1e-5):
|
||||
super().__init__()
|
||||
self.norm_final = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=False)
|
||||
self.linear = nn.Linear(hidden_size, output_size, bias=False)
|
||||
self.adaLN_modulation = nn.Sequential(
|
||||
# nn.SiLU(),
|
||||
ACT2FN['silu'],
|
||||
nn.Linear(cond_size, 2 * hidden_size, bias=False)
|
||||
)
|
||||
|
||||
def forward(self, x, c):
|
||||
shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
|
||||
x = modulate(self.norm_final(x), shift, scale)
|
||||
x = self.linear(x)
|
||||
return x
|
||||
|
||||
|
||||
class VibeVoiceDiffusionHead(PreTrainedModel):
|
||||
"""
|
||||
Diffusion head model for vibevoice.
|
||||
|
||||
Args:
|
||||
config (`VibeVoiceDiffusionHeadConfig`): Model configuration
|
||||
latent_size (`int`, optional): Size of the latent space. If not provided, uses `config.latent_size`.
|
||||
"""
|
||||
config_class = VibeVoiceDiffusionHeadConfig
|
||||
supports_gradient_checkpointing = True
|
||||
_supports_flash_attn_2 = True
|
||||
_supports_sdpa = True
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config,
|
||||
):
|
||||
super().__init__(config)
|
||||
self.config = config
|
||||
self.cond_dim = config.hidden_size
|
||||
latent_size = config.latent_size
|
||||
|
||||
self.noisy_images_proj = nn.Linear(latent_size, config.hidden_size, bias=False)
|
||||
self.cond_proj = nn.Linear(config.hidden_size, self.cond_dim, bias=False)
|
||||
self.t_embedder = TimestepEmbedder(self.cond_dim)
|
||||
|
||||
ffn_dim = int(config.hidden_size * config.head_ffn_ratio)
|
||||
|
||||
# Create the intermediate layers
|
||||
self.layers = nn.ModuleList([
|
||||
HeadLayer(
|
||||
embed_dim=config.hidden_size,
|
||||
ffn_dim=ffn_dim,
|
||||
cond_dim=self.cond_dim,
|
||||
norm_eps=config.rms_norm_eps
|
||||
)
|
||||
for _ in range(config.head_layers)
|
||||
])
|
||||
|
||||
# Final layer for output
|
||||
self.final_layer = FinalLayer(
|
||||
hidden_size=config.hidden_size,
|
||||
output_size=latent_size,
|
||||
cond_size=self.cond_dim,
|
||||
norm_eps=config.rms_norm_eps
|
||||
)
|
||||
|
||||
self.initialize_weights()
|
||||
|
||||
def initialize_weights(self):
|
||||
"""Initialize the weights of the model."""
|
||||
# Initialize timestep embedder
|
||||
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
|
||||
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
|
||||
|
||||
# Zero-out adaLN modulation layers
|
||||
for layer in self.layers:
|
||||
nn.init.constant_(layer.adaLN_modulation[-1].weight, 0)
|
||||
|
||||
# Zero-out output layers
|
||||
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
|
||||
nn.init.constant_(self.final_layer.linear.weight, 0)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
noisy_images,
|
||||
timesteps,
|
||||
condition,
|
||||
):
|
||||
"""
|
||||
Forward pass of the prediction head.
|
||||
|
||||
Args:
|
||||
noisy_images (`torch.Tensor`): Noisy images/latents to denoise
|
||||
timesteps (`torch.Tensor`): Timesteps for diffusion
|
||||
condition (`torch.Tensor`): Conditioning information
|
||||
|
||||
Returns:
|
||||
`torch.Tensor`: The predicted noise/velocity
|
||||
"""
|
||||
x = self.noisy_images_proj(noisy_images)
|
||||
t = self.t_embedder(timesteps)
|
||||
condition = self.cond_proj(condition)
|
||||
c = condition + t
|
||||
|
||||
for layer in self.layers:
|
||||
x = layer(x, c)
|
||||
|
||||
x = self.final_layer(x, c)
|
||||
return x
|
||||
|
||||
|
||||
AutoModel.register(VibeVoiceDiffusionHeadConfig, VibeVoiceDiffusionHead)
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceDiffusionHead",
|
||||
]
|
||||
@@ -0,0 +1,313 @@
|
||||
"""Tokenization classes for vibevoice."""
|
||||
|
||||
from typing import List, Optional, Union
|
||||
|
||||
from transformers.utils import logging
|
||||
from transformers.models.qwen2.tokenization_qwen2 import Qwen2Tokenizer
|
||||
from transformers.models.qwen2.tokenization_qwen2_fast import Qwen2TokenizerFast
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class VibeVoiceTextTokenizer(Qwen2Tokenizer):
|
||||
"""
|
||||
Construct a VibeVoice tokenizer. Based on the Qwen2 tokenizer with additional special tokens for speech.
|
||||
|
||||
Args:
|
||||
vocab_file (`str`):
|
||||
Path to the vocabulary file.
|
||||
merges_file (`str`):
|
||||
Path to the merges file.
|
||||
errors (`str`, *optional*, defaults to `"replace"`):
|
||||
Paradigm to follow when decoding bytes to UTF-8.
|
||||
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||||
The unknown token.
|
||||
bos_token (`str`, *optional*):
|
||||
The beginning of sequence token. Not used for vibevoice.
|
||||
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||||
The end of sequence token.
|
||||
pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||||
The token used for padding.
|
||||
add_special_tokens (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to add special tokens when encoding.
|
||||
"""
|
||||
|
||||
model_input_names = ["input_ids", "attention_mask"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_file,
|
||||
merges_file,
|
||||
errors="replace",
|
||||
unk_token="<|endoftext|>",
|
||||
bos_token=None,
|
||||
eos_token="<|endoftext|>",
|
||||
pad_token="<|endoftext|>",
|
||||
add_prefix_space=False,
|
||||
add_special_tokens=True,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
vocab_file=vocab_file,
|
||||
merges_file=merges_file,
|
||||
errors=errors,
|
||||
unk_token=unk_token,
|
||||
bos_token=bos_token,
|
||||
eos_token=eos_token,
|
||||
pad_token=pad_token,
|
||||
add_prefix_space=add_prefix_space,
|
||||
add_special_tokens=add_special_tokens,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Add VibeVoice-specific special tokens
|
||||
self._add_vibevoice_special_tokens()
|
||||
|
||||
def _add_vibevoice_special_tokens(self):
|
||||
"""Add VibeVoice-specific special tokens."""
|
||||
special_tokens = {
|
||||
"additional_special_tokens": [
|
||||
"<|vision_start|>", # Speech start (reusing vision tokens)
|
||||
"<|vision_end|>", # Speech end
|
||||
"<|vision_pad|>", # Speech diffusion pad
|
||||
]
|
||||
}
|
||||
num_added = self.add_special_tokens(special_tokens)
|
||||
|
||||
# Cache special token IDs
|
||||
self._speech_start_id = self.convert_tokens_to_ids("<|vision_start|>")
|
||||
self._speech_end_id = self.convert_tokens_to_ids("<|vision_end|>")
|
||||
self._speech_diffusion_id = self.convert_tokens_to_ids("<|vision_pad|>")
|
||||
|
||||
self._eos_id = self.convert_tokens_to_ids('<|endoftext|>')
|
||||
|
||||
return num_added
|
||||
|
||||
@property
|
||||
def eos_id(self) -> int:
|
||||
"""ID of the end of sequence token."""
|
||||
return self._eos_id
|
||||
|
||||
@property
|
||||
def speech_start_id(self) -> int:
|
||||
"""ID of the speech start token."""
|
||||
return self._speech_start_id
|
||||
|
||||
@property
|
||||
def speech_end_id(self) -> int:
|
||||
"""ID of the speech end token."""
|
||||
return self._speech_end_id
|
||||
|
||||
@property
|
||||
def speech_diffusion_id(self) -> int:
|
||||
"""ID of the speech diffusion token."""
|
||||
return self._speech_diffusion_id
|
||||
|
||||
@property
|
||||
def pad_id(self) -> int:
|
||||
"""ID used for padding (returns -100 for loss masking)."""
|
||||
return -100
|
||||
|
||||
|
||||
class VibeVoiceTextTokenizerFast(Qwen2TokenizerFast):
|
||||
"""
|
||||
Construct a "fast" VibeVoice tokenizer (backed by HuggingFace's *tokenizers* library).
|
||||
Based on the Qwen2 tokenizer with additional special tokens for speech.
|
||||
|
||||
Args:
|
||||
vocab_file (`str`, *optional*):
|
||||
Path to the vocabulary file.
|
||||
merges_file (`str`, *optional*):
|
||||
Path to the merges file.
|
||||
tokenizer_file (`str`, *optional*):
|
||||
Path to [tokenizers](https://github.com/huggingface/tokenizers) file.
|
||||
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||||
The unknown token.
|
||||
bos_token (`str`, *optional*):
|
||||
The beginning of sequence token. Not used for vibevoice.
|
||||
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||||
The end of sequence token.
|
||||
pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||||
The token used for padding.
|
||||
"""
|
||||
|
||||
model_input_names = ["input_ids", "attention_mask"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_file=None,
|
||||
merges_file=None,
|
||||
tokenizer_file=None,
|
||||
unk_token="<|endoftext|>",
|
||||
bos_token=None,
|
||||
eos_token="<|endoftext|>",
|
||||
pad_token="<|endoftext|>",
|
||||
add_prefix_space=False,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
vocab_file=vocab_file,
|
||||
merges_file=merges_file,
|
||||
tokenizer_file=tokenizer_file,
|
||||
unk_token=unk_token,
|
||||
bos_token=bos_token,
|
||||
eos_token=eos_token,
|
||||
pad_token=pad_token,
|
||||
add_prefix_space=add_prefix_space,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Add VibeVoice-specific special tokens
|
||||
self._add_vibevoice_special_tokens()
|
||||
|
||||
def _add_vibevoice_special_tokens(self):
|
||||
"""Add VibeVoice-specific special tokens."""
|
||||
special_tokens = {
|
||||
"additional_special_tokens": [
|
||||
"<|vision_start|>", # Speech start (reusing vision tokens)
|
||||
"<|vision_end|>", # Speech end
|
||||
"<|vision_pad|>", # Speech diffusion pad
|
||||
]
|
||||
}
|
||||
num_added = self.add_special_tokens(special_tokens)
|
||||
|
||||
# Cache special token IDs
|
||||
self._speech_start_id = self.convert_tokens_to_ids("<|vision_start|>")
|
||||
self._speech_end_id = self.convert_tokens_to_ids("<|vision_end|>")
|
||||
self._speech_diffusion_id = self.convert_tokens_to_ids("<|vision_pad|>")
|
||||
|
||||
# self._eos_id = self.convert_tokens_to_ids('<|endoftext|>')
|
||||
self._eos_id = self.eos_token_id # qwen2 / qwen3
|
||||
self._pad_id = self.convert_tokens_to_ids('<|image_pad|>')
|
||||
|
||||
return num_added
|
||||
|
||||
@property
|
||||
def eos_id(self) -> int:
|
||||
"""ID of the end of sequence token."""
|
||||
return self._eos_id
|
||||
|
||||
@property
|
||||
def speech_start_id(self) -> int:
|
||||
"""ID of the speech start token."""
|
||||
return self._speech_start_id
|
||||
|
||||
@property
|
||||
def speech_end_id(self) -> int:
|
||||
"""ID of the speech end token."""
|
||||
return self._speech_end_id
|
||||
|
||||
@property
|
||||
def speech_diffusion_id(self) -> int:
|
||||
"""ID of the speech diffusion token."""
|
||||
return self._speech_diffusion_id
|
||||
|
||||
@property
|
||||
def pad_id(self) -> int:
|
||||
"""ID used for padding (returns -100 for loss masking)."""
|
||||
return self._pad_id
|
||||
|
||||
class VibeVoiceASRTextTokenizerFast(Qwen2TokenizerFast):
|
||||
"""
|
||||
Construct a "fast" VibeVoice tokenizer (backed by HuggingFace's *tokenizers* library).
|
||||
Based on the Qwen2 tokenizer with additional special tokens for speech.
|
||||
|
||||
Args:
|
||||
vocab_file (`str`, *optional*):
|
||||
Path to the vocabulary file.
|
||||
merges_file (`str`, *optional*):
|
||||
Path to the merges file.
|
||||
tokenizer_file (`str`, *optional*):
|
||||
Path to [tokenizers](https://github.com/huggingface/tokenizers) file.
|
||||
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||||
The unknown token.
|
||||
bos_token (`str`, *optional*):
|
||||
The beginning of sequence token. Not used for vibevoice.
|
||||
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||||
The end of sequence token.
|
||||
pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||||
The token used for padding.
|
||||
"""
|
||||
|
||||
model_input_names = ["input_ids", "attention_mask"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_file=None,
|
||||
merges_file=None,
|
||||
tokenizer_file=None,
|
||||
unk_token="<|endoftext|>",
|
||||
bos_token=None,
|
||||
eos_token="<|endoftext|>",
|
||||
pad_token="<|endoftext|>",
|
||||
add_prefix_space=False,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
vocab_file=vocab_file,
|
||||
merges_file=merges_file,
|
||||
tokenizer_file=tokenizer_file,
|
||||
unk_token=unk_token,
|
||||
bos_token=bos_token,
|
||||
eos_token=eos_token,
|
||||
pad_token=pad_token,
|
||||
add_prefix_space=add_prefix_space,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Add VibeVoice-specific special tokens
|
||||
self._add_vibevoice_special_tokens()
|
||||
|
||||
# https://github.com/QwenLM/Qwen2.5-VL/blob/d2240f11656bfe404b9ba56db4e51cd09f522ff1/qwen-vl-finetune/qwenvl/data/data_qwen_packed.py#L57C5-L57C222
|
||||
self.chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
|
||||
|
||||
def _add_vibevoice_special_tokens(self):
|
||||
"""Add VibeVoice-specific special tokens."""
|
||||
special_tokens = {
|
||||
"additional_special_tokens": [
|
||||
"<|object_ref_start|>", # Speech start (reusing vision tokens)
|
||||
"<|object_ref_end|>", # Speech end
|
||||
"<|box_start|>", # Speech diffusion pad
|
||||
]
|
||||
}
|
||||
num_added = self.add_special_tokens(special_tokens)
|
||||
|
||||
# Cache special token IDs
|
||||
self._speech_start_id = self.convert_tokens_to_ids("<|object_ref_start|>")
|
||||
self._speech_end_id = self.convert_tokens_to_ids("<|object_ref_end|>")
|
||||
self._speech_pad_id = self.convert_tokens_to_ids("<|box_start|>")
|
||||
|
||||
self._eos_id = self.eos_token_id # qwen2 / qwen3
|
||||
self._pad_id = self.convert_tokens_to_ids('<|image_pad|>')
|
||||
|
||||
return num_added
|
||||
|
||||
@property
|
||||
def eos_id(self) -> int:
|
||||
"""ID of the end of sequence token."""
|
||||
return self._eos_id
|
||||
|
||||
@property
|
||||
def speech_start_id(self) -> int:
|
||||
"""ID of the speech start token."""
|
||||
return self._speech_start_id
|
||||
|
||||
@property
|
||||
def speech_end_id(self) -> int:
|
||||
"""ID of the speech end token."""
|
||||
return self._speech_end_id
|
||||
|
||||
@property
|
||||
def speech_pad_id(self) -> int:
|
||||
"""ID of the speech diffusion token."""
|
||||
return self._speech_pad_id
|
||||
|
||||
@property
|
||||
def pad_id(self) -> int:
|
||||
return self._pad_id
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceTextTokenizer",
|
||||
"VibeVoiceTextTokenizerFast",
|
||||
"VibeVoiceASRTextTokenizerFast",
|
||||
]
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,264 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import torch
|
||||
|
||||
import asyncio
|
||||
from queue import Empty, Queue
|
||||
from typing import TYPE_CHECKING, Optional
|
||||
|
||||
|
||||
from transformers.generation import BaseStreamer
|
||||
|
||||
|
||||
class AudioStreamer(BaseStreamer):
|
||||
"""
|
||||
Audio streamer that stores audio chunks in queues for each sample in the batch.
|
||||
This allows streaming audio generation for multiple samples simultaneously.
|
||||
|
||||
Parameters:
|
||||
batch_size (`int`):
|
||||
The batch size for generation
|
||||
stop_signal (`any`, *optional*):
|
||||
The signal to put in the queue when generation ends. Defaults to None.
|
||||
timeout (`float`, *optional*):
|
||||
The timeout for the audio queue. If `None`, the queue will block indefinitely.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
batch_size: int,
|
||||
stop_signal: Optional[any] = None,
|
||||
timeout: Optional[float] = None,
|
||||
):
|
||||
self.batch_size = batch_size
|
||||
self.stop_signal = stop_signal
|
||||
self.timeout = timeout
|
||||
|
||||
# Create a queue for each sample in the batch
|
||||
self.audio_queues = [Queue() for _ in range(batch_size)]
|
||||
self.finished_flags = [False for _ in range(batch_size)]
|
||||
self.sample_indices_map = {} # Maps from sample index to queue index
|
||||
|
||||
def put(self, audio_chunks: torch.Tensor, sample_indices: torch.Tensor):
|
||||
"""
|
||||
Receives audio chunks and puts them in the appropriate queues.
|
||||
|
||||
Args:
|
||||
audio_chunks: Tensor of shape (num_samples, ...) containing audio chunks
|
||||
sample_indices: Tensor indicating which samples these chunks belong to
|
||||
"""
|
||||
for i, sample_idx in enumerate(sample_indices):
|
||||
idx = sample_idx.item()
|
||||
if idx < self.batch_size and not self.finished_flags[idx]:
|
||||
# Convert to numpy or keep as tensor based on preference
|
||||
audio_chunk = audio_chunks[i].detach().cpu()
|
||||
self.audio_queues[idx].put(audio_chunk, timeout=self.timeout)
|
||||
|
||||
def end(self, sample_indices: Optional[torch.Tensor] = None):
|
||||
"""
|
||||
Signals the end of generation for specified samples or all samples.
|
||||
|
||||
Args:
|
||||
sample_indices: Optional tensor of sample indices to end. If None, ends all.
|
||||
"""
|
||||
if sample_indices is None:
|
||||
# End all samples
|
||||
for idx in range(self.batch_size):
|
||||
if not self.finished_flags[idx]:
|
||||
self.audio_queues[idx].put(self.stop_signal, timeout=self.timeout)
|
||||
self.finished_flags[idx] = True
|
||||
else:
|
||||
# End specific samples
|
||||
for sample_idx in sample_indices:
|
||||
idx = sample_idx.item() if torch.is_tensor(sample_idx) else sample_idx
|
||||
if idx < self.batch_size and not self.finished_flags[idx]:
|
||||
self.audio_queues[idx].put(self.stop_signal, timeout=self.timeout)
|
||||
self.finished_flags[idx] = True
|
||||
|
||||
def __iter__(self):
|
||||
"""Returns an iterator over the batch of audio streams."""
|
||||
return AudioBatchIterator(self)
|
||||
|
||||
def get_stream(self, sample_idx: int):
|
||||
"""Get the audio stream for a specific sample."""
|
||||
if sample_idx >= self.batch_size:
|
||||
raise ValueError(f"Sample index {sample_idx} exceeds batch size {self.batch_size}")
|
||||
return AudioSampleIterator(self, sample_idx)
|
||||
|
||||
|
||||
class AudioSampleIterator:
|
||||
"""Iterator for a single audio stream from the batch."""
|
||||
|
||||
def __init__(self, streamer: AudioStreamer, sample_idx: int):
|
||||
self.streamer = streamer
|
||||
self.sample_idx = sample_idx
|
||||
|
||||
def __iter__(self):
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
value = self.streamer.audio_queues[self.sample_idx].get(timeout=self.streamer.timeout)
|
||||
if value == self.streamer.stop_signal:
|
||||
raise StopIteration()
|
||||
return value
|
||||
|
||||
|
||||
class AudioBatchIterator:
|
||||
"""Iterator that yields audio chunks for all samples in the batch."""
|
||||
|
||||
def __init__(self, streamer: AudioStreamer):
|
||||
self.streamer = streamer
|
||||
self.active_samples = set(range(streamer.batch_size))
|
||||
|
||||
def __iter__(self):
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
if not self.active_samples:
|
||||
raise StopIteration()
|
||||
|
||||
batch_chunks = {}
|
||||
samples_to_remove = set()
|
||||
|
||||
# Try to get chunks from all active samples
|
||||
for idx in self.active_samples:
|
||||
try:
|
||||
value = self.streamer.audio_queues[idx].get(block=False)
|
||||
if value == self.streamer.stop_signal:
|
||||
samples_to_remove.add(idx)
|
||||
else:
|
||||
batch_chunks[idx] = value
|
||||
except Empty:
|
||||
# Queue is empty for this sample, skip it this iteration
|
||||
pass
|
||||
|
||||
# Remove finished samples
|
||||
self.active_samples -= samples_to_remove
|
||||
|
||||
if batch_chunks:
|
||||
return batch_chunks
|
||||
elif self.active_samples:
|
||||
# If no chunks were ready but we still have active samples,
|
||||
# wait a bit and try again
|
||||
import time
|
||||
time.sleep(0.01)
|
||||
return self.__next__()
|
||||
else:
|
||||
raise StopIteration()
|
||||
|
||||
|
||||
class AsyncAudioStreamer(AudioStreamer):
|
||||
"""
|
||||
Async version of AudioStreamer for use in async contexts.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
batch_size: int,
|
||||
stop_signal: Optional[any] = None,
|
||||
timeout: Optional[float] = None,
|
||||
):
|
||||
super().__init__(batch_size, stop_signal, timeout)
|
||||
# Replace regular queues with async queues
|
||||
self.audio_queues = [asyncio.Queue() for _ in range(batch_size)]
|
||||
self.loop = asyncio.get_running_loop()
|
||||
|
||||
def put(self, audio_chunks: torch.Tensor, sample_indices: torch.Tensor):
|
||||
"""Put audio chunks in the appropriate async queues."""
|
||||
for i, sample_idx in enumerate(sample_indices):
|
||||
idx = sample_idx.item()
|
||||
if idx < self.batch_size and not self.finished_flags[idx]:
|
||||
audio_chunk = audio_chunks[i].detach().cpu()
|
||||
self.loop.call_soon_threadsafe(
|
||||
self.audio_queues[idx].put_nowait, audio_chunk
|
||||
)
|
||||
|
||||
def end(self, sample_indices: Optional[torch.Tensor] = None):
|
||||
"""Signal the end of generation for specified samples."""
|
||||
if sample_indices is None:
|
||||
indices_to_end = range(self.batch_size)
|
||||
else:
|
||||
indices_to_end = [s.item() if torch.is_tensor(s) else s for s in sample_indices]
|
||||
|
||||
for idx in indices_to_end:
|
||||
if idx < self.batch_size and not self.finished_flags[idx]:
|
||||
self.loop.call_soon_threadsafe(
|
||||
self.audio_queues[idx].put_nowait, self.stop_signal
|
||||
)
|
||||
self.finished_flags[idx] = True
|
||||
|
||||
async def get_stream(self, sample_idx: int):
|
||||
"""Get async iterator for a specific sample's audio stream."""
|
||||
if sample_idx >= self.batch_size:
|
||||
raise ValueError(f"Sample index {sample_idx} exceeds batch size {self.batch_size}")
|
||||
|
||||
while True:
|
||||
value = await self.audio_queues[sample_idx].get()
|
||||
if value == self.stop_signal:
|
||||
break
|
||||
yield value
|
||||
|
||||
def __aiter__(self):
|
||||
"""Returns an async iterator over all audio streams."""
|
||||
return AsyncAudioBatchIterator(self)
|
||||
|
||||
|
||||
class AsyncAudioBatchIterator:
|
||||
"""Async iterator for batch audio streaming."""
|
||||
|
||||
def __init__(self, streamer: AsyncAudioStreamer):
|
||||
self.streamer = streamer
|
||||
self.active_samples = set(range(streamer.batch_size))
|
||||
|
||||
def __aiter__(self):
|
||||
return self
|
||||
|
||||
async def __anext__(self):
|
||||
if not self.active_samples:
|
||||
raise StopAsyncIteration()
|
||||
|
||||
batch_chunks = {}
|
||||
samples_to_remove = set()
|
||||
|
||||
# Create tasks for all active samples
|
||||
tasks = {
|
||||
idx: asyncio.create_task(self._get_chunk(idx))
|
||||
for idx in self.active_samples
|
||||
}
|
||||
|
||||
# Wait for at least one chunk to be ready
|
||||
done, pending = await asyncio.wait(
|
||||
tasks.values(),
|
||||
return_when=asyncio.FIRST_COMPLETED,
|
||||
timeout=self.streamer.timeout
|
||||
)
|
||||
|
||||
# Cancel pending tasks
|
||||
for task in pending:
|
||||
task.cancel()
|
||||
|
||||
# Process completed tasks
|
||||
for idx, task in tasks.items():
|
||||
if task in done:
|
||||
try:
|
||||
value = await task
|
||||
if value == self.streamer.stop_signal:
|
||||
samples_to_remove.add(idx)
|
||||
else:
|
||||
batch_chunks[idx] = value
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
self.active_samples -= samples_to_remove
|
||||
|
||||
if batch_chunks:
|
||||
return batch_chunks
|
||||
elif self.active_samples:
|
||||
# Try again if we still have active samples
|
||||
return await self.__anext__()
|
||||
else:
|
||||
raise StopAsyncIteration()
|
||||
|
||||
async def _get_chunk(self, idx):
|
||||
"""Helper to get a chunk from a specific queue."""
|
||||
return await self.streamer.audio_queues[idx].get()
|
||||
@@ -0,0 +1,11 @@
|
||||
# vibevoice/processor/__init__.py
|
||||
from .vibevoice_processor import VibeVoiceProcessor
|
||||
from .vibevoice_streaming_processor import VibeVoiceStreamingProcessor
|
||||
from .vibevoice_tokenizer_processor import VibeVoiceTokenizerProcessor, AudioNormalizer
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceProcessor",
|
||||
"VibeVoiceStreamingProcessor",
|
||||
"VibeVoiceTokenizerProcessor",
|
||||
"AudioNormalizer",
|
||||
]
|
||||
@@ -0,0 +1,217 @@
|
||||
import os
|
||||
import threading
|
||||
|
||||
import numpy as np
|
||||
from subprocess import run
|
||||
from typing import List, Optional, Union, Dict, Any
|
||||
|
||||
COMMON_AUDIO_EXTS = [
|
||||
'.mp3', '.MP3', '.Mp3', # All case variations of mp3
|
||||
'.m4a',
|
||||
'.mp4', '.MP4',
|
||||
'.wav', '.WAV',
|
||||
'.m4v',
|
||||
'.aac',
|
||||
'.ogg',
|
||||
'.mov', '.MOV',
|
||||
'.opus',
|
||||
'.m4b',
|
||||
'.flac',
|
||||
'.wma', '.WMA',
|
||||
'.rm', '.3gp', '.mpeg', '.flv', '.webm', '.mp2', '.aif', '.aiff', '.oga', '.ogv', '.mpga', '.m3u8', '.amr'
|
||||
]
|
||||
|
||||
def load_audio_use_ffmpeg(file: str, resample: bool = False, target_sr: int = 24000):
|
||||
"""
|
||||
Open an audio file and read as mono waveform, optionally resampling.
|
||||
Returns both the audio data and the original sample rate.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
file: str
|
||||
The audio file to open
|
||||
resample: bool
|
||||
Whether to resample the audio
|
||||
target_sr: int
|
||||
The target sample rate if resampling is requested
|
||||
|
||||
Returns
|
||||
-------
|
||||
A tuple containing:
|
||||
- A NumPy array with the audio waveform in float32 dtype
|
||||
- The original sample rate of the audio file
|
||||
"""
|
||||
if not resample:
|
||||
# First, get the original sample rate
|
||||
cmd_probe = [
|
||||
"ffprobe",
|
||||
"-v", "quiet",
|
||||
"-show_entries", "stream=sample_rate",
|
||||
"-of", "default=noprint_wrappers=1:nokey=1",
|
||||
file
|
||||
]
|
||||
|
||||
original_sr = int(run(cmd_probe, capture_output=True, check=True).stdout.decode().strip())
|
||||
else:
|
||||
original_sr = None
|
||||
|
||||
# Now load the audio
|
||||
sr_to_use = target_sr if resample else original_sr
|
||||
|
||||
cmd = [
|
||||
"ffmpeg",
|
||||
"-loglevel", "error",
|
||||
"-nostdin",
|
||||
"-threads", "0",
|
||||
"-i", file,
|
||||
"-f", "s16le",
|
||||
"-ac", "1",
|
||||
"-acodec", "pcm_s16le",
|
||||
"-ar", str(sr_to_use),
|
||||
"-",
|
||||
]
|
||||
|
||||
out = _run_ffmpeg(cmd).stdout
|
||||
audio_data = np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
|
||||
|
||||
return audio_data, sr_to_use
|
||||
|
||||
|
||||
def _get_ffmpeg_max_concurrency() -> int:
|
||||
"""Get the maximum FFmpeg concurrency from environment variable."""
|
||||
v = os.getenv("VIBEVOICE_FFMPEG_MAX_CONCURRENCY", "")
|
||||
try:
|
||||
n = int(v) if v.strip() else 0
|
||||
except Exception:
|
||||
n = 0
|
||||
# 0/negative means no explicit limit.
|
||||
return n
|
||||
|
||||
|
||||
_FFMPEG_MAX_CONCURRENCY = _get_ffmpeg_max_concurrency()
|
||||
_FFMPEG_SEM = threading.Semaphore(_FFMPEG_MAX_CONCURRENCY) if _FFMPEG_MAX_CONCURRENCY > 0 else None
|
||||
|
||||
|
||||
def _run_ffmpeg(cmd: list, *, stdin_bytes: bytes = None):
|
||||
"""Run ffmpeg with optional global concurrency limiting.
|
||||
|
||||
This is important for vLLM multi-request concurrency: spawning too many
|
||||
ffmpeg processes can saturate CPU/IO and cause request failures/timeouts.
|
||||
"""
|
||||
if _FFMPEG_SEM is None:
|
||||
return run(cmd, capture_output=True, check=True, input=stdin_bytes)
|
||||
with _FFMPEG_SEM:
|
||||
return run(cmd, capture_output=True, check=True, input=stdin_bytes)
|
||||
|
||||
|
||||
def load_audio_bytes_use_ffmpeg(data: bytes, *, resample: bool = False, target_sr: int = 24000):
|
||||
"""Decode audio bytes via ffmpeg stdin pipe.
|
||||
|
||||
Compared to writing bytes to a temp file, this avoids filesystem IO and
|
||||
reduces contention under high request concurrency.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data: bytes
|
||||
The audio data bytes
|
||||
resample: bool
|
||||
Whether to resample the audio (must be True)
|
||||
target_sr: int
|
||||
The target sample rate if resampling is requested
|
||||
|
||||
Returns
|
||||
-------
|
||||
A tuple containing:
|
||||
- A NumPy array with the audio waveform in float32 dtype
|
||||
- The sample rate
|
||||
"""
|
||||
if not resample:
|
||||
# For stdin bytes, we don't have a cheap/robust way to probe original sr.
|
||||
# Keep behavior explicit.
|
||||
raise ValueError("load_audio_bytes_use_ffmpeg requires resample=True")
|
||||
|
||||
cmd = [
|
||||
"ffmpeg",
|
||||
"-loglevel", "error",
|
||||
"-threads", "0",
|
||||
"-i", "pipe:0",
|
||||
"-f", "s16le",
|
||||
"-ac", "1",
|
||||
"-acodec", "pcm_s16le",
|
||||
"-ar", str(target_sr),
|
||||
"-",
|
||||
]
|
||||
out = _run_ffmpeg(cmd, stdin_bytes=data).stdout
|
||||
audio_data = np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
|
||||
return audio_data, target_sr
|
||||
|
||||
|
||||
class AudioNormalizer:
|
||||
"""
|
||||
Audio normalization class for VibeVoice tokenizer.
|
||||
|
||||
This class provides audio normalization to ensure consistent input levels
|
||||
for the VibeVoice tokenizer while maintaining audio quality.
|
||||
"""
|
||||
|
||||
def __init__(self, target_dB_FS: float = -25, eps: float = 1e-6):
|
||||
"""
|
||||
Initialize the audio normalizer.
|
||||
|
||||
Args:
|
||||
target_dB_FS (float): Target dB FS level for the audio. Default: -25
|
||||
eps (float): Small value to avoid division by zero. Default: 1e-6
|
||||
"""
|
||||
self.target_dB_FS = target_dB_FS
|
||||
self.eps = eps
|
||||
|
||||
def tailor_dB_FS(self, audio: np.ndarray) -> tuple:
|
||||
"""
|
||||
Adjust the audio to the target dB FS level.
|
||||
|
||||
Args:
|
||||
audio (np.ndarray): Input audio signal
|
||||
|
||||
Returns:
|
||||
tuple: (normalized_audio, rms, scalar)
|
||||
"""
|
||||
rms = np.sqrt(np.mean(audio**2))
|
||||
scalar = 10 ** (self.target_dB_FS / 20) / (rms + self.eps)
|
||||
normalized_audio = audio * scalar
|
||||
return normalized_audio, rms, scalar
|
||||
|
||||
def avoid_clipping(self, audio: np.ndarray, scalar: Optional[float] = None) -> tuple:
|
||||
"""
|
||||
Avoid clipping by scaling down if necessary.
|
||||
|
||||
Args:
|
||||
audio (np.ndarray): Input audio signal
|
||||
scalar (float, optional): Explicit scaling factor
|
||||
|
||||
Returns:
|
||||
tuple: (normalized_audio, scalar)
|
||||
"""
|
||||
if scalar is None:
|
||||
max_val = np.max(np.abs(audio))
|
||||
if max_val > 1.0:
|
||||
scalar = max_val + self.eps
|
||||
else:
|
||||
scalar = 1.0
|
||||
|
||||
return audio / scalar, scalar
|
||||
|
||||
def __call__(self, audio: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Normalize the audio by adjusting to target dB FS and avoiding clipping.
|
||||
|
||||
Args:
|
||||
audio (np.ndarray): Input audio signal
|
||||
|
||||
Returns:
|
||||
np.ndarray: Normalized audio signal
|
||||
"""
|
||||
# First adjust to target dB FS
|
||||
audio, _, _ = self.tailor_dB_FS(audio)
|
||||
# Then avoid clipping
|
||||
audio, _ = self.avoid_clipping(audio)
|
||||
return audio
|
||||
@@ -0,0 +1,572 @@
|
||||
"""
|
||||
Processor class for VibeVoice ASR models.
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
import math
|
||||
import warnings
|
||||
from typing import List, Optional, Union, Dict, Any, Tuple
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from transformers.tokenization_utils_base import BatchEncoding
|
||||
from transformers.utils import TensorType, logging
|
||||
from .vibevoice_tokenizer_processor import VibeVoiceTokenizerProcessor, AudioNormalizer
|
||||
|
||||
try:
|
||||
from .audio_utils import load_audio_use_ffmpeg
|
||||
HAS_FFMPEG_UTILS = True
|
||||
except ImportError:
|
||||
HAS_FFMPEG_UTILS = False
|
||||
warnings.warn("audio_utils not available, will fall back to soundfile for audio loading")
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
SYSTEM_PROMPT = "You are a helpful assistant that transcribes audio input into text output in JSON format."
|
||||
|
||||
|
||||
class VibeVoiceASRProcessor:
|
||||
"""
|
||||
Processor for VibeVoice ASR (Automatic Speech Recognition) models.
|
||||
|
||||
This processor handles audio preprocessing and tokenization for ASR tasks,
|
||||
following the exact format used in training with proper chat templates.
|
||||
|
||||
Args:
|
||||
tokenizer: The text tokenizer for processing text
|
||||
audio_processor: The audio processor for processing speech
|
||||
speech_tok_compress_ratio (int): Compression ratio for speech tokenization. Default: 3200 (product of encoder ratios [8,5,5,4,2,2])
|
||||
target_sample_rate (int): Target sample rate for audio
|
||||
normalize_audio (bool): Whether to normalize audio input
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
tokenizer=None,
|
||||
audio_processor=None,
|
||||
speech_tok_compress_ratio=3200,
|
||||
target_sample_rate=24000,
|
||||
normalize_audio=True,
|
||||
**kwargs
|
||||
):
|
||||
self.tokenizer = tokenizer
|
||||
self.audio_processor = audio_processor or VibeVoiceTokenizerProcessor(
|
||||
sampling_rate=target_sample_rate,
|
||||
normalize_audio=normalize_audio
|
||||
)
|
||||
self.speech_tok_compress_ratio = speech_tok_compress_ratio
|
||||
self.target_sample_rate = target_sample_rate
|
||||
self.normalize_audio = normalize_audio
|
||||
|
||||
if normalize_audio:
|
||||
self.audio_normalizer = AudioNormalizer()
|
||||
else:
|
||||
self.audio_normalizer = None
|
||||
|
||||
# Cache special token IDs
|
||||
self._cache_special_tokens()
|
||||
|
||||
def _cache_special_tokens(self):
|
||||
"""Cache special token IDs for efficiency."""
|
||||
# Add safety checks for special tokens
|
||||
if hasattr(self.tokenizer, 'speech_start_id'):
|
||||
self.speech_start_id = self.tokenizer.speech_start_id
|
||||
else:
|
||||
self.speech_start_id = self.tokenizer.convert_tokens_to_ids("<|speech_start|>")
|
||||
|
||||
if hasattr(self.tokenizer, 'speech_end_id'):
|
||||
self.speech_end_id = self.tokenizer.speech_end_id
|
||||
else:
|
||||
self.speech_end_id = self.tokenizer.convert_tokens_to_ids("<|speech_end|>")
|
||||
|
||||
if hasattr(self.tokenizer, 'speech_pad_id'):
|
||||
self.speech_pad_id = self.tokenizer.speech_pad_id
|
||||
else:
|
||||
self.speech_pad_id = self.tokenizer.convert_tokens_to_ids("<|speech_pad|>")
|
||||
|
||||
if hasattr(self.tokenizer, 'pad_id'):
|
||||
self.pad_id = self.tokenizer.pad_id
|
||||
elif hasattr(self.tokenizer, 'pad_token_id'):
|
||||
self.pad_id = self.tokenizer.pad_token_id
|
||||
else:
|
||||
self.pad_id = self.tokenizer.convert_tokens_to_ids("<|endoftext|>")
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
|
||||
"""
|
||||
Load processor from a pretrained model path.
|
||||
|
||||
Args:
|
||||
pretrained_model_name_or_path: Path to the pretrained model
|
||||
**kwargs: Additional keyword arguments
|
||||
|
||||
Returns:
|
||||
VibeVoiceASRProcessor: The loaded processor
|
||||
"""
|
||||
import json
|
||||
from transformers.utils import cached_file
|
||||
from vibevoice.modular.modular_vibevoice_text_tokenizer import VibeVoiceASRTextTokenizerFast
|
||||
|
||||
# Try to load configuration
|
||||
config_path = os.path.join(pretrained_model_name_or_path, "preprocessor_config.json")
|
||||
config = {}
|
||||
|
||||
if os.path.exists(config_path):
|
||||
with open(config_path, 'r') as f:
|
||||
config = json.load(f)
|
||||
else:
|
||||
try:
|
||||
config_file = cached_file(
|
||||
pretrained_model_name_or_path,
|
||||
"preprocessor_config.json",
|
||||
**kwargs
|
||||
)
|
||||
with open(config_file, 'r') as f:
|
||||
config = json.load(f)
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not load preprocessor_config.json: {e}")
|
||||
logger.warning("Using default configuration")
|
||||
|
||||
# Extract parameters
|
||||
speech_tok_compress_ratio = config.get("speech_tok_compress_ratio", 3200)
|
||||
target_sample_rate = config.get("target_sample_rate", 24000)
|
||||
normalize_audio = config.get("normalize_audio", True)
|
||||
|
||||
# Load tokenizer
|
||||
language_model_pretrained_name = config.get("language_model_pretrained_name", None) or kwargs.pop("language_model_pretrained_name", "Qwen/Qwen2.5-1.5B")
|
||||
logger.info(f"Loading tokenizer from {language_model_pretrained_name}")
|
||||
|
||||
if 'qwen' in language_model_pretrained_name.lower():
|
||||
tokenizer = VibeVoiceASRTextTokenizerFast.from_pretrained(
|
||||
language_model_pretrained_name,
|
||||
**kwargs
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unsupported tokenizer type for {language_model_pretrained_name}")
|
||||
|
||||
# Load audio processor
|
||||
audio_processor = VibeVoiceTokenizerProcessor(
|
||||
sampling_rate=target_sample_rate,
|
||||
normalize_audio=normalize_audio,
|
||||
target_dB_FS=config.get("target_dB_FS", -25),
|
||||
eps=config.get("eps", 1e-6),
|
||||
)
|
||||
|
||||
return cls(
|
||||
tokenizer=tokenizer,
|
||||
audio_processor=audio_processor,
|
||||
speech_tok_compress_ratio=speech_tok_compress_ratio,
|
||||
target_sample_rate=target_sample_rate,
|
||||
normalize_audio=normalize_audio,
|
||||
)
|
||||
|
||||
def save_pretrained(self, save_directory: Union[str, os.PathLike], **kwargs):
|
||||
"""
|
||||
Save processor configuration to a directory.
|
||||
|
||||
Args:
|
||||
save_directory: Directory to save the configuration
|
||||
**kwargs: Additional keyword arguments
|
||||
"""
|
||||
import json
|
||||
|
||||
os.makedirs(save_directory, exist_ok=True)
|
||||
|
||||
# Save processor configuration
|
||||
processor_config = {
|
||||
"processor_class": "VibeVoiceASRProcessor",
|
||||
"speech_tok_compress_ratio": self.speech_tok_compress_ratio,
|
||||
"target_sample_rate": self.target_sample_rate,
|
||||
"normalize_audio": self.normalize_audio,
|
||||
"target_dB_FS": -25,
|
||||
"eps": 1e-6,
|
||||
}
|
||||
|
||||
config_path = os.path.join(save_directory, "preprocessor_config.json")
|
||||
with open(config_path, 'w') as f:
|
||||
json.dump(processor_config, f, indent=2)
|
||||
|
||||
logger.info(f"Processor configuration saved in {config_path}")
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
audio: Optional[Union[str, np.ndarray, torch.Tensor, List[Union[str, np.ndarray, torch.Tensor]]]] = None,
|
||||
sampling_rate: Optional[int] = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
padding: bool = True,
|
||||
max_length: Optional[int] = None,
|
||||
truncation: bool = False,
|
||||
add_generation_prompt: bool = True,
|
||||
use_streaming: bool = True,
|
||||
context_info: Optional[str] = None,
|
||||
**kwargs
|
||||
) -> BatchEncoding:
|
||||
"""
|
||||
Process audio input for ASR model.
|
||||
|
||||
Args:
|
||||
audio: Audio input(s). Can be:
|
||||
- str: Path to audio file
|
||||
- np.ndarray: Audio array
|
||||
- torch.Tensor: Audio tensor
|
||||
- List of the above for batch processing
|
||||
sampling_rate: Sampling rate of input audio
|
||||
return_tensors: Output format ('pt' for PyTorch, 'np' for NumPy)
|
||||
padding: Whether to pad batch inputs
|
||||
max_length: Maximum sequence length
|
||||
truncation: Whether to truncate long sequences
|
||||
add_generation_prompt: Whether to add generation prompt for inference
|
||||
use_streaming: Whether to use streaming mode (True by default, auto False if <60s)
|
||||
context_info: Optional context information (e.g., hotwords, metadata) to help transcription
|
||||
|
||||
Returns:
|
||||
BatchEncoding with:
|
||||
- input_ids: Token IDs for the model
|
||||
- attention_mask: Attention mask
|
||||
- acoustic_input_mask: Mask indicating speech token positions
|
||||
- speech_tensors: Processed speech features
|
||||
- speech_masks: Valid speech masks
|
||||
- vae_tok_seqlens: Length of each speech segment in tokens
|
||||
"""
|
||||
if audio is None:
|
||||
raise ValueError("Audio input is required for ASR processing")
|
||||
|
||||
# Handle single vs batch input
|
||||
if isinstance(audio, list):
|
||||
is_batched = True
|
||||
audio_list = audio
|
||||
else:
|
||||
is_batched = False
|
||||
audio_list = [audio]
|
||||
|
||||
# Process each audio input
|
||||
all_encodings = []
|
||||
for audio_input in audio_list:
|
||||
encoding = self._process_single_audio(
|
||||
audio_input,
|
||||
sampling_rate=sampling_rate,
|
||||
add_generation_prompt=add_generation_prompt,
|
||||
use_streaming=use_streaming,
|
||||
context_info=context_info,
|
||||
)
|
||||
all_encodings.append(encoding)
|
||||
|
||||
# Combine into batch
|
||||
batch_encoding = self._batch_encode(
|
||||
all_encodings,
|
||||
padding=padding,
|
||||
max_length=max_length,
|
||||
truncation=truncation,
|
||||
return_tensors=return_tensors,
|
||||
)
|
||||
|
||||
return batch_encoding
|
||||
|
||||
def _process_single_audio(
|
||||
self,
|
||||
audio: Union[str, np.ndarray, torch.Tensor],
|
||||
sampling_rate: Optional[int] = None,
|
||||
add_generation_prompt: bool = True,
|
||||
use_streaming: bool = True,
|
||||
context_info: Optional[str] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Process a single audio input.
|
||||
|
||||
Args:
|
||||
audio: Single audio input
|
||||
sampling_rate: Audio sampling rate
|
||||
add_generation_prompt: Whether to add generation prompt
|
||||
context_info: Optional context information (e.g., hotwords, metadata) to help transcription
|
||||
|
||||
Returns:
|
||||
Dictionary with processed tokens and audio features
|
||||
"""
|
||||
# Process audio through audio processor
|
||||
if isinstance(audio, str):
|
||||
# Load from file using ffmpeg for better format support
|
||||
if HAS_FFMPEG_UTILS:
|
||||
try:
|
||||
audio_array, file_sr = load_audio_use_ffmpeg(audio, resample=False)
|
||||
except Exception as e:
|
||||
# Fall back to soundfile if ffmpeg fails
|
||||
warnings.warn(f"ffmpeg loading failed, falling back to soundfile: {e}")
|
||||
import soundfile as sf
|
||||
audio_array, file_sr = sf.read(audio)
|
||||
if audio_array.ndim > 1:
|
||||
audio_array = audio_array.mean(axis=1) # Convert to mono
|
||||
else:
|
||||
import soundfile as sf
|
||||
audio_array, file_sr = sf.read(audio)
|
||||
if audio_array.ndim > 1:
|
||||
audio_array = audio_array.mean(axis=1) # Convert to mono
|
||||
|
||||
# Resample if needed
|
||||
if file_sr != self.target_sample_rate:
|
||||
import librosa
|
||||
audio_array = librosa.resample(
|
||||
audio_array,
|
||||
orig_sr=file_sr,
|
||||
target_sr=self.target_sample_rate
|
||||
)
|
||||
elif isinstance(audio, torch.Tensor):
|
||||
audio_array = audio.cpu().numpy()
|
||||
if audio_array.ndim > 1:
|
||||
audio_array = audio_array.squeeze()
|
||||
else:
|
||||
audio_array = np.array(audio, dtype=np.float32)
|
||||
if audio_array.ndim > 1:
|
||||
audio_array = audio_array.squeeze()
|
||||
|
||||
# Ensure float32
|
||||
audio_array = audio_array.astype(np.float32)
|
||||
|
||||
# Normalize if needed
|
||||
if self.normalize_audio and self.audio_normalizer:
|
||||
audio_array = self.audio_normalizer(audio_array)
|
||||
|
||||
# Calculate audio duration
|
||||
audio_duration = len(audio_array) / self.target_sample_rate
|
||||
|
||||
# Auto-disable streaming for short audio (<60s)
|
||||
if use_streaming and audio_duration < 60.0:
|
||||
use_streaming = False
|
||||
|
||||
# Calculate token length based on streaming mode
|
||||
# Non-streaming: uses ceil (encoder adds extra_padding for stride alignment)
|
||||
# Streaming: uses floor (segments processed independently, no global alignment)
|
||||
# if use_streaming:
|
||||
# vae_tok_len = len(audio_array) // self.speech_tok_compress_ratio
|
||||
# else:
|
||||
vae_tok_len = math.ceil(len(audio_array) / self.speech_tok_compress_ratio)
|
||||
|
||||
# Build token sequence following training format
|
||||
# 1. System prompt - use apply_chat_template then encode like in training
|
||||
system_prompt_text = self.tokenizer.apply_chat_template(
|
||||
[{"role": "system", "content": SYSTEM_PROMPT}],
|
||||
tokenize=False
|
||||
)
|
||||
system_tokens = self.tokenizer.encode(system_prompt_text)
|
||||
|
||||
# 2. User input with speech tokens
|
||||
# Build speech placeholder string
|
||||
sp_start_token = self.tokenizer.convert_ids_to_tokens(self.speech_start_id)
|
||||
sp_pad_token = self.tokenizer.convert_ids_to_tokens(self.speech_pad_id)
|
||||
sp_end_token = self.tokenizer.convert_ids_to_tokens(self.speech_end_id)
|
||||
|
||||
# User suffix with audio duration info
|
||||
show_keys = ['Start time', 'End time', 'Speaker ID', 'Content']
|
||||
if context_info and context_info.strip():
|
||||
user_suffix = f"This is a {audio_duration:.2f} seconds audio, with extra info: {context_info.strip()}\n\nPlease transcribe it with these keys: " + ", ".join(show_keys)
|
||||
else:
|
||||
user_suffix = f"This is a {audio_duration:.2f} seconds audio, please transcribe it with these keys: " + ", ".join(show_keys)
|
||||
|
||||
user_input_string = ''.join(
|
||||
[sp_start_token] + [sp_pad_token] * vae_tok_len + [sp_end_token]
|
||||
) + '\n' + user_suffix
|
||||
|
||||
user_tokens = self.tokenizer.apply_chat_template(
|
||||
[{"role": "user", "content": user_input_string}],
|
||||
tokenize=True
|
||||
)
|
||||
|
||||
# Combine tokens
|
||||
full_tokens = system_tokens + user_tokens
|
||||
|
||||
# Create acoustic input mask
|
||||
acoustic_input_mask = [1 if token == self.speech_pad_id else 0 for token in full_tokens]
|
||||
|
||||
return {
|
||||
"input_ids": full_tokens,
|
||||
"acoustic_input_mask": acoustic_input_mask,
|
||||
"speech": audio_array,
|
||||
"vae_tok_len": vae_tok_len,
|
||||
}
|
||||
|
||||
def _batch_encode(
|
||||
self,
|
||||
encodings: List[Dict[str, Any]],
|
||||
padding: bool = True,
|
||||
max_length: Optional[int] = None,
|
||||
truncation: bool = False,
|
||||
return_tensors: Optional[str] = None,
|
||||
) -> BatchEncoding:
|
||||
"""
|
||||
Combine multiple encodings into a batch.
|
||||
|
||||
Args:
|
||||
encodings: List of encoded samples
|
||||
padding: Whether to pad sequences
|
||||
max_length: Maximum sequence length
|
||||
truncation: Whether to truncate
|
||||
return_tensors: Output format
|
||||
|
||||
Returns:
|
||||
BatchEncoding with batched data
|
||||
"""
|
||||
# Extract components
|
||||
input_ids_list = [enc["input_ids"] for enc in encodings]
|
||||
acoustic_masks_list = [enc["acoustic_input_mask"] for enc in encodings]
|
||||
speech_list = [enc["speech"] for enc in encodings]
|
||||
vae_tok_lens = [enc["vae_tok_len"] for enc in encodings]
|
||||
|
||||
# Determine max length for padding
|
||||
if padding:
|
||||
if max_length is not None:
|
||||
target_length = max_length
|
||||
else:
|
||||
target_length = max(len(ids) for ids in input_ids_list)
|
||||
|
||||
# Pad sequences
|
||||
padded_input_ids = []
|
||||
padded_acoustic_masks = []
|
||||
attention_masks = []
|
||||
|
||||
for input_ids, acoustic_mask in zip(input_ids_list, acoustic_masks_list):
|
||||
# Truncate if needed
|
||||
if truncation and len(input_ids) > target_length:
|
||||
input_ids = input_ids[:target_length]
|
||||
acoustic_mask = acoustic_mask[:target_length]
|
||||
|
||||
# Pad sequences to left (for autoregressive generation)
|
||||
padding_length = target_length - len(input_ids)
|
||||
padded_ids = [self.pad_id] * padding_length + input_ids
|
||||
padded_acoustic = [0] * padding_length + acoustic_mask
|
||||
attention_mask = [0] * padding_length + [1] * len(input_ids)
|
||||
|
||||
padded_input_ids.append(padded_ids)
|
||||
padded_acoustic_masks.append(padded_acoustic)
|
||||
attention_masks.append(attention_mask)
|
||||
|
||||
input_ids_list = padded_input_ids
|
||||
acoustic_masks_list = padded_acoustic_masks
|
||||
else:
|
||||
attention_masks = [[1] * len(ids) for ids in input_ids_list]
|
||||
|
||||
# Process speech tensors - raw audio is 1D, so we keep it as is
|
||||
max_speech_length = max(len(s) for s in speech_list)
|
||||
padded_speeches = np.zeros((len(speech_list), max_speech_length), dtype=np.float32)
|
||||
speech_masks = np.zeros((len(speech_list), max(vae_tok_lens)), dtype=bool)
|
||||
|
||||
for i, (speech, vae_len) in enumerate(zip(speech_list, vae_tok_lens)):
|
||||
padded_speeches[i, :len(speech)] = speech
|
||||
speech_masks[i, :vae_len] = True
|
||||
|
||||
# Create batch encoding
|
||||
batch_encoding = BatchEncoding()
|
||||
|
||||
if return_tensors == "pt":
|
||||
batch_encoding["input_ids"] = torch.tensor(input_ids_list, dtype=torch.long)
|
||||
batch_encoding["attention_mask"] = torch.tensor(attention_masks, dtype=torch.long)
|
||||
batch_encoding["acoustic_input_mask"] = torch.tensor(acoustic_masks_list, dtype=torch.bool)
|
||||
batch_encoding["speech_tensors"] = torch.tensor(padded_speeches, dtype=torch.float32)
|
||||
batch_encoding["speech_masks"] = torch.tensor(speech_masks, dtype=torch.bool)
|
||||
# Note: vae_tok_seqlens and speech_type are not included as they are not model inputs
|
||||
else:
|
||||
batch_encoding["input_ids"] = input_ids_list if len(input_ids_list) > 1 else input_ids_list[0]
|
||||
batch_encoding["attention_mask"] = attention_masks if len(attention_masks) > 1 else attention_masks[0]
|
||||
batch_encoding["acoustic_input_mask"] = acoustic_masks_list if len(acoustic_masks_list) > 1 else acoustic_masks_list[0]
|
||||
batch_encoding["speech_tensors"] = padded_speeches if len(padded_speeches) > 1 else padded_speeches[0]
|
||||
batch_encoding["speech_masks"] = speech_masks if len(speech_masks) > 1 else speech_masks[0]
|
||||
|
||||
return batch_encoding
|
||||
|
||||
def batch_decode(self, *args, **kwargs):
|
||||
"""
|
||||
Decode batch of token IDs to text.
|
||||
Forwards to tokenizer's batch_decode method.
|
||||
"""
|
||||
return self.tokenizer.batch_decode(*args, **kwargs)
|
||||
|
||||
def decode(self, *args, **kwargs):
|
||||
"""
|
||||
Decode token IDs to text.
|
||||
Forwards to tokenizer's decode method.
|
||||
"""
|
||||
return self.tokenizer.decode(*args, **kwargs)
|
||||
|
||||
def post_process_transcription(self, text: str) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Post-process the generated transcription text to extract structured data.
|
||||
|
||||
Args:
|
||||
text: Generated text from the model
|
||||
|
||||
Returns:
|
||||
List of dictionaries with transcription segments
|
||||
"""
|
||||
try:
|
||||
# Try to parse as JSON
|
||||
if "```json" in text:
|
||||
# Extract JSON from markdown code block
|
||||
json_start = text.find("```json") + 7
|
||||
json_end = text.find("```", json_start)
|
||||
json_str = text[json_start:json_end].strip()
|
||||
else:
|
||||
# Try to find JSON array or object
|
||||
json_start = text.find("[")
|
||||
if json_start == -1:
|
||||
json_start = text.find("{")
|
||||
if json_start != -1:
|
||||
# Find matching closing bracket
|
||||
bracket_count = 0
|
||||
json_end = json_start
|
||||
for i in range(json_start, len(text)):
|
||||
if text[i] in "[{":
|
||||
bracket_count += 1
|
||||
elif text[i] in "]}":
|
||||
bracket_count -= 1
|
||||
if bracket_count == 0:
|
||||
json_end = i + 1
|
||||
break
|
||||
json_str = text[json_start:json_end]
|
||||
else:
|
||||
json_str = text
|
||||
|
||||
# Parse JSON
|
||||
result = json.loads(json_str)
|
||||
|
||||
# Ensure it's a list
|
||||
if isinstance(result, dict):
|
||||
result = [result]
|
||||
|
||||
# Validate and clean up the result
|
||||
cleaned_result = []
|
||||
for item in result:
|
||||
if isinstance(item, dict):
|
||||
cleaned_item = {}
|
||||
# Map keys to expected format
|
||||
key_mapping = {
|
||||
"Start time": "start_time",
|
||||
"Start": "start_time",
|
||||
"End time": "end_time",
|
||||
"End": "end_time",
|
||||
"Speaker ID": "speaker_id",
|
||||
"Speaker": "speaker_id",
|
||||
"Content": "text",
|
||||
}
|
||||
for key, mapped_key in key_mapping.items():
|
||||
if key in item:
|
||||
cleaned_item[mapped_key] = item[key]
|
||||
|
||||
if cleaned_item:
|
||||
cleaned_result.append(cleaned_item)
|
||||
|
||||
return cleaned_result
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning(f"Failed to parse JSON from transcription: {e}")
|
||||
logger.debug(f"Raw text: {text}")
|
||||
return []
|
||||
except Exception as e:
|
||||
logger.warning(f"Error post-processing transcription: {e}")
|
||||
return []
|
||||
|
||||
@property
|
||||
def model_input_names(self):
|
||||
"""Return the list of inputs accepted by the model."""
|
||||
return ["input_ids", "attention_mask", "acoustic_input_mask", "speech_tensors", "speech_masks"]
|
||||
|
||||
__all__ = ["VibeVoiceASRProcessor"]
|
||||
@@ -0,0 +1,692 @@
|
||||
import math
|
||||
import warnings
|
||||
from typing import List, Optional, Union, Dict, Any, Tuple
|
||||
import os
|
||||
import re
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from transformers.tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
|
||||
from transformers.utils import TensorType, logging
|
||||
from .vibevoice_tokenizer_processor import AudioNormalizer
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class VibeVoiceProcessor:
|
||||
r"""
|
||||
Constructs a VibeVoice processor which wraps a VibeVoice tokenizer and audio processor into a single processor.
|
||||
|
||||
[`VibeVoiceProcessor`] offers all the functionalities of [`VibeVoiceTokenizer`] and [`VibeVoiceTokenizerProcessor`].
|
||||
See the [`~VibeVoiceProcessor.__call__`] and [`~VibeVoiceProcessor.decode`] for more information.
|
||||
|
||||
Args:
|
||||
tokenizer (`VibeVoiceTextTokenizer` or `VibeVoiceTextTokenizerFast`):
|
||||
The tokenizer for text processing.
|
||||
audio_processor (`VibeVoiceTokenizerProcessor`):
|
||||
The audio processor for speech processing.
|
||||
speech_tok_compress_ratio (`int`, *optional*, defaults to 3200):
|
||||
The compression ratio for speech tokenization.
|
||||
db_normalize (`bool`, *optional*, defaults to True):
|
||||
Whether to apply decibel normalization to audio inputs.
|
||||
"""
|
||||
|
||||
def __init__(self, tokenizer=None, audio_processor=None, speech_tok_compress_ratio=3200, db_normalize=True, **kwargs):
|
||||
self.tokenizer = tokenizer
|
||||
self.audio_processor = audio_processor
|
||||
self.speech_tok_compress_ratio = speech_tok_compress_ratio
|
||||
self.db_normalize = db_normalize
|
||||
self.audio_normalizer = AudioNormalizer() if db_normalize else None
|
||||
self.system_prompt = " Transform the text provided by various speakers into speech output, utilizing the distinct voice of each respective speaker.\n"
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
|
||||
"""
|
||||
Instantiate a VibeVoiceProcessor from a pretrained VibeVoice processor.
|
||||
|
||||
Args:
|
||||
pretrained_model_name_or_path (`str` or `os.PathLike`):
|
||||
This can be either:
|
||||
- a string, the *model id* of a pretrained model
|
||||
- a path to a *directory* containing processor config
|
||||
|
||||
Returns:
|
||||
[`VibeVoiceProcessor`]: The processor object instantiated from pretrained model.
|
||||
"""
|
||||
import os
|
||||
import json
|
||||
from transformers.utils import cached_file
|
||||
from .vibevoice_tokenizer_processor import VibeVoiceTokenizerProcessor
|
||||
from vibevoice.modular.modular_vibevoice_text_tokenizer import (
|
||||
VibeVoiceTextTokenizer,
|
||||
VibeVoiceTextTokenizerFast
|
||||
)
|
||||
|
||||
# Try to load from local path first, then from HF hub
|
||||
config_path = os.path.join(pretrained_model_name_or_path, "preprocessor_config.json")
|
||||
config = None
|
||||
|
||||
if os.path.exists(config_path):
|
||||
# Local path exists
|
||||
with open(config_path, 'r') as f:
|
||||
config = json.load(f)
|
||||
else:
|
||||
# Try to load from HF hub
|
||||
try:
|
||||
config_file = cached_file(
|
||||
pretrained_model_name_or_path,
|
||||
"preprocessor_config.json",
|
||||
**kwargs
|
||||
)
|
||||
with open(config_file, 'r') as f:
|
||||
config = json.load(f)
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not load preprocessor_config.json from {pretrained_model_name_or_path}: {e}")
|
||||
logger.warning("Using default configuration")
|
||||
config = {
|
||||
"speech_tok_compress_ratio": 3200,
|
||||
"db_normalize": True,
|
||||
}
|
||||
|
||||
# Extract main processor parameters
|
||||
speech_tok_compress_ratio = config.get("speech_tok_compress_ratio", 3200)
|
||||
db_normalize = config.get("db_normalize", True)
|
||||
|
||||
# Load tokenizer - try from model path first, then fall back to Qwen
|
||||
language_model_pretrained_name = config.get("language_model_pretrained_name", None) or kwargs.pop("language_model_pretrained_name", "Qwen/Qwen2.5-1.5B")
|
||||
logger.info(f"Loading tokenizer from {language_model_pretrained_name}")
|
||||
if 'qwen' in language_model_pretrained_name.lower():
|
||||
tokenizer = VibeVoiceTextTokenizerFast.from_pretrained(
|
||||
language_model_pretrained_name,
|
||||
**kwargs
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unsupported tokenizer type for {language_model_pretrained_name}. Supported types: Qwen, Llama, Gemma.")
|
||||
|
||||
# Load audio processor
|
||||
if "audio_processor" in config:
|
||||
# Create audio processor from config
|
||||
audio_config = config["audio_processor"]
|
||||
audio_processor = VibeVoiceTokenizerProcessor(
|
||||
sampling_rate=audio_config.get("sampling_rate", 24000),
|
||||
normalize_audio=audio_config.get("normalize_audio", True),
|
||||
target_dB_FS=audio_config.get("target_dB_FS", -25),
|
||||
eps=audio_config.get("eps", 1e-6),
|
||||
)
|
||||
else:
|
||||
# Create default audio processor
|
||||
audio_processor = VibeVoiceTokenizerProcessor()
|
||||
|
||||
# Create and return the processor
|
||||
return cls(
|
||||
tokenizer=tokenizer,
|
||||
audio_processor=audio_processor,
|
||||
speech_tok_compress_ratio=speech_tok_compress_ratio,
|
||||
db_normalize=db_normalize,
|
||||
)
|
||||
|
||||
def save_pretrained(self, save_directory: Union[str, os.PathLike], **kwargs):
|
||||
"""
|
||||
Save a processor to a directory, so that it can be re-loaded using the
|
||||
[`~VibeVoiceProcessor.from_pretrained`] class method.
|
||||
|
||||
Args:
|
||||
save_directory (`str` or `os.PathLike`):
|
||||
Directory where the processor will be saved.
|
||||
"""
|
||||
import os
|
||||
import json
|
||||
|
||||
os.makedirs(save_directory, exist_ok=True)
|
||||
|
||||
# Save processor configuration
|
||||
processor_config = {
|
||||
"processor_class": "VibeVoiceProcessor",
|
||||
"speech_tok_compress_ratio": self.speech_tok_compress_ratio,
|
||||
"db_normalize": self.db_normalize,
|
||||
"audio_processor": {
|
||||
"feature_extractor_type": "VibeVoiceTokenizerProcessor",
|
||||
"sampling_rate": getattr(self.audio_processor, 'sampling_rate', 24000),
|
||||
"normalize_audio": getattr(self.audio_processor, 'normalize_audio', True),
|
||||
"target_dB_FS": getattr(self.audio_processor, 'target_dB_FS', -25),
|
||||
"eps": getattr(self.audio_processor, 'eps', 1e-6),
|
||||
}
|
||||
}
|
||||
|
||||
config_path = os.path.join(save_directory, "preprocessor_config.json")
|
||||
with open(config_path, 'w') as f:
|
||||
json.dump(processor_config, f, indent=2)
|
||||
|
||||
logger.info(f"Processor configuration saved in {config_path}")
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
text: Optional[Union[str, List[str], TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
|
||||
voice_samples: Optional[Union[List[Union[str, np.ndarray]], List[List[Union[str, np.ndarray]]]]] = None,
|
||||
padding: Union[bool, str, PaddingStrategy] = True,
|
||||
truncation: Union[bool, str, TruncationStrategy] = False,
|
||||
max_length: Optional[int] = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
return_attention_mask: bool = True,
|
||||
**kwargs,
|
||||
) -> BatchEncoding:
|
||||
"""
|
||||
Main method to process one or more podcast scripts with optional voice samples.
|
||||
|
||||
Args:
|
||||
text (`str`, `List[str]`):
|
||||
The input text(s) to process. Can be:
|
||||
- A single script string
|
||||
- A list of script strings for batch processing
|
||||
- A path to a .json or .txt file
|
||||
- A list of paths
|
||||
voice_samples (`List[Union[str, np.ndarray]]`, `List[List[Union[str, np.ndarray]]]`, *optional*):
|
||||
Voice samples for each script. Can be:
|
||||
- A list of samples for a single script
|
||||
- A list of lists for batch processing
|
||||
padding (`bool`, `str` or `PaddingStrategy`, defaults to `True`):
|
||||
Whether to pad sequences to the same length
|
||||
truncation (`bool`, `str` or `TruncationStrategy`, defaults to `False`):
|
||||
Whether to truncate sequences
|
||||
max_length (`int`, *optional*):
|
||||
Maximum length of the returned sequences
|
||||
return_tensors (`str` or `TensorType`, *optional*):
|
||||
If set, will return tensors of a particular framework
|
||||
return_attention_mask (`bool`, defaults to `True`):
|
||||
Whether to return the attention mask
|
||||
|
||||
Returns:
|
||||
`BatchEncoding`: A BatchEncoding with the following fields:
|
||||
- **input_ids** -- List of token id sequences or tensor
|
||||
- **attention_mask** -- List of attention masks or tensor
|
||||
- **speech_tensors** -- Padded speech inputs (if voice_samples provided)
|
||||
- **speech_masks** -- Speech masks (if voice_samples provided)
|
||||
- **speech_input_mask** -- Boolean masks indicating speech token positions
|
||||
"""
|
||||
# Handle single vs batch input
|
||||
if isinstance(text, str) or (isinstance(text, list) and len(text) > 0 and not isinstance(text[0], str)):
|
||||
# Single input
|
||||
texts = [text]
|
||||
is_batched = False
|
||||
else:
|
||||
# Batch input
|
||||
texts = text
|
||||
is_batched = True
|
||||
|
||||
# Handle voice samples
|
||||
if voice_samples is not None:
|
||||
if not is_batched or (isinstance(voice_samples[0], (str, np.ndarray))):
|
||||
# Single set of voice samples
|
||||
voice_samples_list = [voice_samples]
|
||||
else:
|
||||
# Batch of voice samples
|
||||
voice_samples_list = voice_samples
|
||||
else:
|
||||
voice_samples_list = [None] * len(texts)
|
||||
|
||||
# Process each input
|
||||
all_encodings = []
|
||||
for text_input, voice_input in zip(texts, voice_samples_list):
|
||||
encoding = self._process_single(text_input, voice_input)
|
||||
all_encodings.append(encoding)
|
||||
|
||||
# Combine batch
|
||||
batch_encoding = self._batch_encode(
|
||||
all_encodings,
|
||||
padding=padding,
|
||||
truncation=truncation,
|
||||
max_length=max_length,
|
||||
return_tensors=return_tensors,
|
||||
return_attention_mask=return_attention_mask,
|
||||
)
|
||||
|
||||
return batch_encoding
|
||||
|
||||
def _process_single(
|
||||
self,
|
||||
text: Union[str, TextInput],
|
||||
voice_samples: Optional[List[Union[str, np.ndarray]]] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""Process a single podcast script."""
|
||||
# Determine if text is a file path or direct script
|
||||
script = None
|
||||
if isinstance(text, str):
|
||||
# Check if it's a file path
|
||||
if text.endswith('.json') and os.path.exists(text):
|
||||
script = self._convert_json_to_script(text)
|
||||
elif text.endswith('.txt') and os.path.exists(text):
|
||||
script = self._convert_text_to_script(text)
|
||||
else:
|
||||
# Assume it's the script content directly
|
||||
script = text
|
||||
|
||||
if script is None:
|
||||
raise ValueError(f"Could not process input text: {text}")
|
||||
|
||||
# Parse the script
|
||||
parsed_lines = self._parse_script(script)
|
||||
all_speakers = list(set(speaker_id for speaker_id, _ in parsed_lines))
|
||||
|
||||
# Create system prompt
|
||||
# system_tokens = self.tokenizer.encode(self.system_prompt, add_special_tokens=False)
|
||||
system_tokens = self.tokenizer.encode(self.system_prompt)
|
||||
|
||||
# Process voice samples if provided
|
||||
if voice_samples:
|
||||
voice_tokens, voice_speech_inputs, voice_speech_masks = self._create_voice_prompt(voice_samples[:len(all_speakers)])
|
||||
else:
|
||||
voice_tokens, voice_speech_inputs, voice_speech_masks = [], [], []
|
||||
|
||||
# Build full token sequence
|
||||
full_tokens = system_tokens + voice_tokens
|
||||
speech_input_mask = [False] * len(system_tokens) + voice_speech_masks
|
||||
|
||||
# Add text input section
|
||||
full_tokens += self.tokenizer.encode(' Text input:\n', add_special_tokens=False)
|
||||
speech_input_mask += [False] * len(self.tokenizer.encode(' Text input:\n', add_special_tokens=False))
|
||||
|
||||
for speaker_id, speaker_text in parsed_lines:
|
||||
speaker_text_tokens = self.tokenizer.encode(f" Speaker {speaker_id}:{speaker_text}\n", add_special_tokens=False)
|
||||
full_tokens += speaker_text_tokens
|
||||
speech_input_mask += [False] * len(speaker_text_tokens)
|
||||
|
||||
# Add speech output section
|
||||
full_tokens += self.tokenizer.encode(' Speech output:\n', add_special_tokens=False) + [self.tokenizer.speech_start_id]
|
||||
speech_input_mask += [False] * (len(self.tokenizer.encode(' Speech output:\n', add_special_tokens=False)) + 1)
|
||||
|
||||
return {
|
||||
"input_ids": full_tokens,
|
||||
"speech_inputs": voice_speech_inputs if voice_speech_inputs else None,
|
||||
"speech_input_mask": speech_input_mask,
|
||||
"parsed_script": parsed_lines,
|
||||
"all_speakers": all_speakers,
|
||||
}
|
||||
|
||||
def _batch_encode(
|
||||
self,
|
||||
encodings: List[Dict[str, Any]],
|
||||
padding: Union[bool, str, PaddingStrategy] = True,
|
||||
truncation: Union[bool, str, TruncationStrategy] = False,
|
||||
max_length: Optional[int] = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
return_attention_mask: bool = True,
|
||||
) -> BatchEncoding:
|
||||
"""Combine multiple encodings into a batch with padding."""
|
||||
# Extract input_ids and create attention_mask
|
||||
input_ids_list = [enc["input_ids"] for enc in encodings]
|
||||
speech_input_masks_list = [enc["speech_input_mask"] for enc in encodings]
|
||||
|
||||
# Determine padding strategy
|
||||
if isinstance(padding, bool):
|
||||
padding_strategy = PaddingStrategy.LONGEST if padding else PaddingStrategy.DO_NOT_PAD
|
||||
elif isinstance(padding, str):
|
||||
padding_strategy = PaddingStrategy(padding)
|
||||
else:
|
||||
padding_strategy = padding
|
||||
|
||||
# Apply padding to input_ids
|
||||
if padding_strategy != PaddingStrategy.DO_NOT_PAD:
|
||||
if padding_strategy == PaddingStrategy.LONGEST:
|
||||
max_len = max(len(ids) for ids in input_ids_list)
|
||||
elif padding_strategy == PaddingStrategy.MAX_LENGTH and max_length is not None:
|
||||
max_len = max_length
|
||||
else:
|
||||
max_len = max(len(ids) for ids in input_ids_list)
|
||||
|
||||
# Pad sequences
|
||||
padded_input_ids = []
|
||||
attention_masks = []
|
||||
padded_speech_input_masks = []
|
||||
|
||||
for input_ids, speech_mask in zip(input_ids_list, speech_input_masks_list):
|
||||
# Truncate if needed
|
||||
if truncation and len(input_ids) > max_len:
|
||||
input_ids = input_ids[:max_len]
|
||||
speech_mask = speech_mask[:max_len]
|
||||
|
||||
# Pad
|
||||
padding_length = max_len - len(input_ids)
|
||||
# padded_ids = [self.tokenizer.pad_token_id] * padding_length + input_ids
|
||||
padded_ids = [self.tokenizer.pad_id] * padding_length + input_ids
|
||||
attention_mask = [0] * padding_length + [1] * len(input_ids)
|
||||
padded_speech_mask = [False] * padding_length + speech_mask
|
||||
|
||||
padded_input_ids.append(padded_ids)
|
||||
attention_masks.append(attention_mask)
|
||||
padded_speech_input_masks.append(padded_speech_mask)
|
||||
|
||||
input_ids_list = padded_input_ids
|
||||
speech_input_masks_list = padded_speech_input_masks
|
||||
else:
|
||||
# No padding, just create attention masks
|
||||
attention_masks = [[1] * len(ids) for ids in input_ids_list] if return_attention_mask else None
|
||||
|
||||
# Process speech inputs
|
||||
all_speech_inputs = []
|
||||
has_speech = False
|
||||
for enc in encodings:
|
||||
if enc["speech_inputs"] is not None:
|
||||
all_speech_inputs.extend(enc["speech_inputs"])
|
||||
has_speech = True
|
||||
|
||||
# Prepare batch encoding
|
||||
batch_encoding = BatchEncoding()
|
||||
|
||||
# Handle tensor conversion
|
||||
if return_tensors is not None:
|
||||
batch_encoding["input_ids"] = torch.tensor(input_ids_list, dtype=torch.long)
|
||||
if return_attention_mask and attention_masks is not None:
|
||||
batch_encoding["attention_mask"] = torch.tensor(attention_masks, dtype=torch.long)
|
||||
batch_encoding["speech_input_mask"] = torch.tensor(speech_input_masks_list, dtype=torch.bool)
|
||||
else:
|
||||
batch_encoding["input_ids"] = input_ids_list
|
||||
if return_attention_mask and attention_masks is not None:
|
||||
batch_encoding["attention_mask"] = attention_masks
|
||||
batch_encoding["speech_input_mask"] = speech_input_masks_list
|
||||
|
||||
# Process speech tensors if present
|
||||
if has_speech:
|
||||
speech_dict = self.prepare_speech_inputs(
|
||||
all_speech_inputs,
|
||||
return_tensors=return_tensors,
|
||||
)
|
||||
batch_encoding["speech_tensors"] = speech_dict["padded_speeches"]
|
||||
batch_encoding["speech_masks"] = speech_dict["speech_masks"]
|
||||
else:
|
||||
batch_encoding["speech_tensors"] = None
|
||||
batch_encoding["speech_masks"] = None
|
||||
|
||||
# Add metadata
|
||||
batch_encoding["parsed_scripts"] = [enc["parsed_script"] for enc in encodings]
|
||||
batch_encoding["all_speakers_list"] = [enc["all_speakers"] for enc in encodings]
|
||||
|
||||
return batch_encoding
|
||||
|
||||
def _create_voice_prompt(
|
||||
self,
|
||||
speaker_samples: List[Union[str, np.ndarray]]
|
||||
) -> Tuple[List[int], List[np.ndarray], List[bool]]:
|
||||
"""
|
||||
Create voice prompt tokens and process audio samples.
|
||||
|
||||
Returns:
|
||||
tuple: (voice_tokens, voice_speech_inputs, voice_speech_masks)
|
||||
"""
|
||||
vae_token_id = self.tokenizer.speech_diffusion_id
|
||||
|
||||
voice_full_tokens = self.tokenizer.encode(' Voice input:\n', add_special_tokens=False)
|
||||
voice_speech_inputs = []
|
||||
voice_speech_masks = [False] * len(voice_full_tokens)
|
||||
|
||||
for speaker_id, speaker_audio in enumerate(speaker_samples):
|
||||
prefix_tokens = self.tokenizer.encode(f" Speaker {speaker_id}:", add_special_tokens=False)
|
||||
|
||||
# Process audio
|
||||
if isinstance(speaker_audio, str):
|
||||
# Load audio from file
|
||||
wav = self.audio_processor._load_audio_from_path(speaker_audio)
|
||||
else:
|
||||
wav = np.array(speaker_audio, dtype=np.float32)
|
||||
|
||||
# Apply normalization if needed
|
||||
if self.db_normalize and self.audio_normalizer:
|
||||
wav = self.audio_normalizer(wav)
|
||||
|
||||
# Calculate token length based on compression ratio
|
||||
# if speaker_audio.endswith('.pt') or speaker_audio.endswith('.npy'):
|
||||
# vae_tok_len = wav.shape[0]
|
||||
# else:
|
||||
vae_tok_len = math.ceil(wav.shape[0] / self.speech_tok_compress_ratio)
|
||||
|
||||
# Build tokens and masks
|
||||
speaker_tokens = (prefix_tokens +
|
||||
[self.tokenizer.speech_start_id] +
|
||||
[vae_token_id] * vae_tok_len +
|
||||
[self.tokenizer.speech_end_id] +
|
||||
self.tokenizer.encode('\n', add_special_tokens=False))
|
||||
|
||||
vae_input_mask = ([False] * len(prefix_tokens) +
|
||||
[False] +
|
||||
[True] * vae_tok_len +
|
||||
[False] +
|
||||
[False])
|
||||
|
||||
voice_full_tokens.extend(speaker_tokens)
|
||||
voice_speech_masks.extend(vae_input_mask)
|
||||
voice_speech_inputs.append(wav)
|
||||
|
||||
return voice_full_tokens, voice_speech_inputs, voice_speech_masks
|
||||
|
||||
def prepare_speech_inputs(
|
||||
self,
|
||||
speech_inputs: List[np.ndarray],
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
device: Optional[Union[str, torch.device]] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Prepare speech inputs for model consumption.
|
||||
|
||||
Args:
|
||||
speech_inputs: List of speech arrays
|
||||
return_tensors: Output tensor type
|
||||
device: Device to place tensors on
|
||||
dtype: Data type for tensors
|
||||
|
||||
Returns:
|
||||
Dictionary with padded_speeches and speech_masks
|
||||
"""
|
||||
if not speech_inputs:
|
||||
return {"padded_speeches": None, "speech_masks": None}
|
||||
|
||||
# Calculate sequence lengths
|
||||
vae_tok_seqlens = [math.ceil(s.shape[0] / self.speech_tok_compress_ratio) for s in speech_inputs]
|
||||
# vae_tok_seqlens = [math.ceil(s.shape[0] / self.speech_tok_compress_ratio) if s.ndim == 1 else s.shape[0] for s in speech_inputs]
|
||||
max_speech_length = max(s.shape[0] for s in speech_inputs)
|
||||
|
||||
# Pad speeches
|
||||
if speech_inputs[0].ndim == 1:
|
||||
padded_speeches = np.full((len(speech_inputs), max_speech_length), fill_value=0, dtype=np.float32)
|
||||
else:
|
||||
padded_speeches = np.full((len(speech_inputs), max_speech_length, speech_inputs[0].shape[-1]), fill_value=0, dtype=np.float32)
|
||||
speech_masks = np.zeros((len(speech_inputs), max(vae_tok_seqlens)), dtype=np.bool_)
|
||||
|
||||
for i, (speech, vae_tok_length) in enumerate(zip(speech_inputs, vae_tok_seqlens)):
|
||||
padded_speeches[i, :len(speech)] = speech
|
||||
speech_masks[i, :vae_tok_length] = True
|
||||
|
||||
result = {
|
||||
"padded_speeches": padded_speeches,
|
||||
"speech_masks": speech_masks,
|
||||
}
|
||||
|
||||
# Convert to tensors if requested
|
||||
if return_tensors == "pt":
|
||||
result["padded_speeches"] = torch.tensor(padded_speeches, device=device, dtype=dtype or torch.float32)
|
||||
result["speech_masks"] = torch.tensor(speech_masks, device=device, dtype=torch.bool)
|
||||
|
||||
return result
|
||||
|
||||
def _convert_json_to_script(self, json_file: str) -> str:
|
||||
"""
|
||||
Convert JSON format to script format.
|
||||
Expected JSON format:
|
||||
[
|
||||
{"speaker": "1", "text": "Hello everyone..."},
|
||||
{"speaker": "2", "text": "Great to be here..."}
|
||||
]
|
||||
"""
|
||||
import json
|
||||
|
||||
with open(json_file, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
|
||||
if not isinstance(data, list):
|
||||
raise ValueError("JSON file must contain a list of speaker entries")
|
||||
|
||||
script_lines = []
|
||||
for item in data:
|
||||
if not isinstance(item, dict):
|
||||
logger.warning(f"Skipping non-dict entry: {item}")
|
||||
continue
|
||||
|
||||
speaker = item.get('speaker')
|
||||
text = item.get('text')
|
||||
|
||||
if speaker is None or text is None:
|
||||
logger.warning(f"Skipping entry missing speaker or text: {item}")
|
||||
continue
|
||||
|
||||
# Ensure speaker ID is valid
|
||||
try:
|
||||
speaker_id = int(speaker)
|
||||
except (ValueError, TypeError):
|
||||
logger.warning(f"Invalid speaker ID: {speaker}, skipping entry")
|
||||
continue
|
||||
|
||||
# Clean up text
|
||||
text = text.strip()
|
||||
if text:
|
||||
script_lines.append(f"Speaker {speaker_id}: {text}")
|
||||
|
||||
if not script_lines:
|
||||
raise ValueError("No valid entries found in JSON file")
|
||||
|
||||
return "\n".join(script_lines)
|
||||
|
||||
def _convert_text_to_script(self, text_file: str) -> str:
|
||||
"""
|
||||
Convert text file to script format.
|
||||
Handles multiple formats:
|
||||
1. Already formatted as "Speaker X: text"
|
||||
2. Plain text (assigns to Speaker 1)
|
||||
|
||||
Handles edge cases like multiple colons in a line.
|
||||
"""
|
||||
with open(text_file, 'r', encoding='utf-8') as f:
|
||||
lines = f.readlines()
|
||||
|
||||
script_lines = []
|
||||
current_speaker = 1
|
||||
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
|
||||
# Try to parse as "Speaker X: text" format
|
||||
# Use regex to be more robust
|
||||
speaker_match = re.match(r'^Speaker\s+(\d+)\s*:\s*(.*)$', line, re.IGNORECASE)
|
||||
|
||||
if speaker_match:
|
||||
speaker_id = int(speaker_match.group(1))
|
||||
text = speaker_match.group(2).strip()
|
||||
if text:
|
||||
script_lines.append(f"Speaker {speaker_id}: {text}")
|
||||
else:
|
||||
# Treat as plain text - assign to current speaker
|
||||
script_lines.append(f"Speaker {current_speaker}: {line}")
|
||||
|
||||
if not script_lines:
|
||||
raise ValueError("No valid content found in text file")
|
||||
|
||||
return "\n".join(script_lines)
|
||||
|
||||
def _parse_script(self, script: str) -> List[Tuple[int, str]]:
|
||||
"""Parse script into list of (speaker_id, text) tuples."""
|
||||
lines = script.strip().split("\n")
|
||||
parsed_lines = []
|
||||
speaker_ids = []
|
||||
|
||||
# First pass: parse all lines and collect speaker IDs
|
||||
for line in lines:
|
||||
if not line.strip():
|
||||
continue
|
||||
|
||||
# Use regex to handle edge cases like multiple colons
|
||||
match = re.match(r'^Speaker\s+(\d+)\s*:\s*(.*)$', line.strip(), re.IGNORECASE)
|
||||
|
||||
if match:
|
||||
speaker_id = int(match.group(1))
|
||||
text = ' ' + match.group(2).strip()
|
||||
parsed_lines.append((speaker_id, text))
|
||||
speaker_ids.append(speaker_id)
|
||||
else:
|
||||
logger.warning(f"Could not parse line: '{line}'")
|
||||
|
||||
if not parsed_lines:
|
||||
raise ValueError("No valid speaker lines found in script")
|
||||
|
||||
# Check if we need to normalize speaker IDs (only if all are > 0)
|
||||
min_speaker_id = min(speaker_ids)
|
||||
if min_speaker_id > 0:
|
||||
# Normalize to start from 0
|
||||
normalized_lines = []
|
||||
for speaker_id, text in parsed_lines:
|
||||
normalized_lines.append((speaker_id - 1, text))
|
||||
return normalized_lines
|
||||
else:
|
||||
# Keep original IDs
|
||||
return parsed_lines
|
||||
|
||||
def _merge_inputs(self, text_inputs: BatchEncoding, audio_inputs: Dict) -> BatchEncoding:
|
||||
"""Merge text and audio inputs into a single BatchEncoding."""
|
||||
# Start with text inputs
|
||||
merged = BatchEncoding(text_inputs)
|
||||
|
||||
# Add audio-specific fields
|
||||
if "audio" in audio_inputs:
|
||||
merged["speech_inputs"] = audio_inputs["audio"]
|
||||
if "streaming" in audio_inputs:
|
||||
merged["streaming"] = audio_inputs["streaming"]
|
||||
|
||||
return merged
|
||||
|
||||
def batch_decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to VibeVoiceTextTokenizer's [`~PreTrainedTokenizer.batch_decode`].
|
||||
Please refer to the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.batch_decode(*args, **kwargs)
|
||||
|
||||
def decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to VibeVoiceTextTokenizer's [`~PreTrainedTokenizer.decode`].
|
||||
Please refer to the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.decode(*args, **kwargs)
|
||||
|
||||
@property
|
||||
def model_input_names(self):
|
||||
"""
|
||||
Return the list of inputs accepted by the model.
|
||||
"""
|
||||
tokenizer_input_names = self.tokenizer.model_input_names
|
||||
audio_processor_input_names = self.audio_processor.model_input_names
|
||||
return list(dict.fromkeys(tokenizer_input_names + audio_processor_input_names + ["speech_inputs", "speech_input_mask"]))
|
||||
|
||||
def save_audio(self,
|
||||
audio: Union[torch.Tensor, np.ndarray, List[Union[torch.Tensor, np.ndarray]]],
|
||||
output_path: str = "output.wav",
|
||||
sampling_rate: Optional[int] = None,
|
||||
normalize: bool = False,
|
||||
batch_prefix: str = "audio_",
|
||||
) -> str:
|
||||
"""
|
||||
Save audio data to a file.
|
||||
Args:
|
||||
audio (Union[torch.Tensor, np.ndarray, List[Union[torch.Tensor, np.ndarray]]]):
|
||||
The audio data to save. Can be a single tensor/array or a list of them.
|
||||
output_path (str, optional): Path to save the audio file. Defaults to "output.wav".
|
||||
sampling_rate (int, optional): Sampling rate for the audio. If None, uses the processor's default.
|
||||
normalize (bool, optional): Whether to normalize the audio before saving. Defaults to False.
|
||||
batch_prefix (str, optional): Prefix for batch audio files. Defaults to "audio_".
|
||||
Returns:
|
||||
str: The path to the saved audio file.
|
||||
"""
|
||||
return self.audio_processor.save_audio(audio, output_path=output_path, sampling_rate=sampling_rate, normalize=normalize, batch_prefix=batch_prefix)
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceProcessor",
|
||||
]
|
||||
@@ -0,0 +1,409 @@
|
||||
import math
|
||||
import warnings
|
||||
from typing import List, Optional, Union, Dict, Any, Tuple
|
||||
import os
|
||||
import re
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from transformers.tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
|
||||
from transformers.utils import TensorType, logging
|
||||
from .vibevoice_tokenizer_processor import AudioNormalizer
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class VibeVoiceStreamingProcessor:
|
||||
r"""
|
||||
Constructs a VibeVoice Streaming processor which wraps a VibeVoice tokenizer and audio processor into a single processor.
|
||||
|
||||
Args:
|
||||
tokenizer (`VibeVoiceTextTokenizer` or `VibeVoiceTextTokenizerFast`):
|
||||
The tokenizer for text processing.
|
||||
audio_processor (`VibeVoiceTokenizerProcessor`):
|
||||
The audio processor for speech processing.
|
||||
speech_tok_compress_ratio (`int`, *optional*, defaults to 3200):
|
||||
The compression ratio for speech tokenization.
|
||||
db_normalize (`bool`, *optional*, defaults to True):
|
||||
Whether to apply decibel normalization to audio inputs.
|
||||
"""
|
||||
|
||||
def __init__(self, tokenizer=None, audio_processor=None, speech_tok_compress_ratio=3200, db_normalize=True, **kwargs):
|
||||
self.tokenizer = tokenizer
|
||||
self.audio_processor = audio_processor
|
||||
self.speech_tok_compress_ratio = speech_tok_compress_ratio
|
||||
self.db_normalize = db_normalize
|
||||
self.audio_normalizer = AudioNormalizer() if db_normalize else None
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
|
||||
"""
|
||||
Instantiate a VibeVoiceStreamingProcessor from a pretrained VibeVoice Streaming processor.
|
||||
|
||||
Args:
|
||||
pretrained_model_name_or_path (`str` or `os.PathLike`):
|
||||
This can be either:
|
||||
- a string, the *model id* of a pretrained model
|
||||
- a path to a *directory* containing processor config
|
||||
|
||||
Returns:
|
||||
[`VibeVoiceStreamingProcessor`]: The processor object instantiated from pretrained model.
|
||||
"""
|
||||
import os
|
||||
import json
|
||||
from transformers.utils import cached_file
|
||||
from .vibevoice_tokenizer_processor import VibeVoiceTokenizerProcessor
|
||||
from vibevoice.modular.modular_vibevoice_text_tokenizer import (
|
||||
VibeVoiceTextTokenizer,
|
||||
VibeVoiceTextTokenizerFast
|
||||
)
|
||||
|
||||
# Try to load from local path first, then from HF hub
|
||||
config_path = os.path.join(pretrained_model_name_or_path, "preprocessor_config.json")
|
||||
config = None
|
||||
|
||||
if os.path.exists(config_path):
|
||||
# Local path exists
|
||||
with open(config_path, 'r') as f:
|
||||
config = json.load(f)
|
||||
else:
|
||||
# Try to load from HF hub
|
||||
try:
|
||||
config_file = cached_file(
|
||||
pretrained_model_name_or_path,
|
||||
"preprocessor_config.json",
|
||||
**kwargs
|
||||
)
|
||||
with open(config_file, 'r') as f:
|
||||
config = json.load(f)
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not load preprocessor_config.json from {pretrained_model_name_or_path}: {e}")
|
||||
logger.warning("Using default configuration")
|
||||
config = {
|
||||
"speech_tok_compress_ratio": 3200,
|
||||
"db_normalize": True,
|
||||
}
|
||||
|
||||
# Extract main processor parameters
|
||||
speech_tok_compress_ratio = config.get("speech_tok_compress_ratio", 3200)
|
||||
db_normalize = config.get("db_normalize", True)
|
||||
|
||||
# Load tokenizer - try from model path first, then fall back to Qwen
|
||||
language_model_pretrained_name = config.get("language_model_pretrained_name", None) or kwargs.pop("language_model_pretrained_name", "Qwen/Qwen2.5-1.5B")
|
||||
logger.info(f"Loading tokenizer from {language_model_pretrained_name}")
|
||||
if 'qwen' in language_model_pretrained_name.lower():
|
||||
tokenizer = VibeVoiceTextTokenizerFast.from_pretrained(
|
||||
language_model_pretrained_name,
|
||||
**kwargs
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unsupported tokenizer type for {language_model_pretrained_name}. Supported types: Qwen, Llama, Gemma.")
|
||||
|
||||
# Load audio processor
|
||||
if "audio_processor" in config:
|
||||
# Create audio processor from config
|
||||
audio_config = config["audio_processor"]
|
||||
audio_processor = VibeVoiceTokenizerProcessor(
|
||||
sampling_rate=audio_config.get("sampling_rate", 24000),
|
||||
normalize_audio=audio_config.get("normalize_audio", True),
|
||||
target_dB_FS=audio_config.get("target_dB_FS", -25),
|
||||
eps=audio_config.get("eps", 1e-6),
|
||||
)
|
||||
else:
|
||||
# Create default audio processor
|
||||
audio_processor = VibeVoiceTokenizerProcessor()
|
||||
|
||||
# Create and return the processor
|
||||
return cls(
|
||||
tokenizer=tokenizer,
|
||||
audio_processor=audio_processor,
|
||||
speech_tok_compress_ratio=speech_tok_compress_ratio,
|
||||
db_normalize=db_normalize,
|
||||
)
|
||||
|
||||
def save_pretrained(self, save_directory: Union[str, os.PathLike], **kwargs):
|
||||
"""
|
||||
Save a processor to a directory, so that it can be re-loaded using the
|
||||
[`~VibeVoiceStreamingProcessor.from_pretrained`] class method.
|
||||
|
||||
Args:
|
||||
save_directory (`str` or `os.PathLike`):
|
||||
Directory where the processor will be saved.
|
||||
"""
|
||||
import os
|
||||
import json
|
||||
|
||||
os.makedirs(save_directory, exist_ok=True)
|
||||
|
||||
# Save processor configuration
|
||||
processor_config = {
|
||||
"processor_class": "VibeVoiceStreamingProcessor",
|
||||
"speech_tok_compress_ratio": self.speech_tok_compress_ratio,
|
||||
"db_normalize": self.db_normalize,
|
||||
"audio_processor": {
|
||||
"feature_extractor_type": "VibeVoiceTokenizerProcessor",
|
||||
"sampling_rate": getattr(self.audio_processor, 'sampling_rate', 24000),
|
||||
"normalize_audio": getattr(self.audio_processor, 'normalize_audio', True),
|
||||
"target_dB_FS": getattr(self.audio_processor, 'target_dB_FS', -25),
|
||||
"eps": getattr(self.audio_processor, 'eps', 1e-6),
|
||||
}
|
||||
}
|
||||
|
||||
config_path = os.path.join(save_directory, "preprocessor_config.json")
|
||||
with open(config_path, 'w') as f:
|
||||
json.dump(processor_config, f, indent=2)
|
||||
|
||||
logger.info(f"Processor configuration saved in {config_path}")
|
||||
|
||||
def __call__(self) -> BatchEncoding:
|
||||
"""
|
||||
Note:
|
||||
This method is intentionally not implemented in the streaming processor.
|
||||
Use `process_input_with_cached_prompt` for streaming use cases.
|
||||
"""
|
||||
raise NotImplementedError(
|
||||
"VibeVoiceStreamingProcessor.__call__ is not implemented. "
|
||||
"Use process_input_with_cached_prompt for streaming inputs."
|
||||
)
|
||||
|
||||
def process_input_with_cached_prompt(
|
||||
self,
|
||||
text: Optional[str] = None,
|
||||
cached_prompt: Optional[Dict[str, Any]] = None,
|
||||
padding: Union[bool, str, PaddingStrategy] = True,
|
||||
truncation: Union[bool, str, TruncationStrategy] = False,
|
||||
max_length: Optional[int] = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
return_attention_mask: bool = True,
|
||||
**kwargs,
|
||||
) -> BatchEncoding:
|
||||
"""
|
||||
Main method to process one text script based on cached prompt. The function currently only supports single examples.
|
||||
|
||||
Args:
|
||||
text (`str`):
|
||||
The input text to process.
|
||||
cached_prompt (`Dict[str, Any]`, *optional*):
|
||||
The cached prompt to use for processing. It contains the kv cache of the voice prompt.
|
||||
padding (`bool`, `str` or `PaddingStrategy`, defaults to `True`):
|
||||
Whether to pad sequences to the same length
|
||||
truncation (`bool`, `str` or `TruncationStrategy`, defaults to `False`):
|
||||
Whether to truncate sequences
|
||||
max_length (`int`, *optional*):
|
||||
Maximum length of the returned sequences
|
||||
return_tensors (`str` or `TensorType`, *optional*):
|
||||
If set, will return tensors of a particular framework
|
||||
return_attention_mask (`bool`, defaults to `True`):
|
||||
Whether to return the attention mask
|
||||
|
||||
Returns:
|
||||
`BatchEncoding`: A BatchEncoding with the following fields:
|
||||
- **input_ids** -- List of token id sequences or tensor
|
||||
- **attention_mask** -- List of attention masks or tensor
|
||||
- **tts_lm_input_ids** -- List of token id sequences or tensor used for TTS LM
|
||||
- **tts_lm_attention_mask** -- List of attention masks or tensor used for TTS LM
|
||||
- **tts_text_ids** -- List of token id sequences or tensor for TTS text input
|
||||
- **speech_tensors** -- Padded speech inputs (if voice_samples provided)
|
||||
- **speech_masks** -- Speech masks (if voice_samples provided)
|
||||
- **speech_input_mask** -- Boolean masks indicating speech token positions
|
||||
"""
|
||||
# Only support single example
|
||||
texts = [text]
|
||||
cached_prompts = [cached_prompt]
|
||||
is_batched = False
|
||||
|
||||
# Process each input
|
||||
all_encodings = []
|
||||
for text_input, cached_prompt_input in zip(texts, cached_prompts):
|
||||
script_tokens = self.tokenizer.encode(text_input.strip() + "\n", add_special_tokens=False)
|
||||
input_id_length = cached_prompt_input['lm']['last_hidden_state'].size(1)
|
||||
tts_lm_input_id_length = cached_prompt_input['tts_lm']['last_hidden_state'].size(1)
|
||||
|
||||
# pseudo input ids and masks
|
||||
input_ids = [self.tokenizer.pad_id] * input_id_length
|
||||
tts_lm_input_ids = [self.tokenizer.pad_id] * tts_lm_input_id_length
|
||||
speech_input_mask = [False] * tts_lm_input_id_length
|
||||
|
||||
encoding = {
|
||||
"input_ids": input_ids,
|
||||
"tts_lm_input_ids": tts_lm_input_ids,
|
||||
"tts_text_ids": script_tokens,
|
||||
"speech_inputs": None,
|
||||
"speech_input_mask": speech_input_mask,
|
||||
}
|
||||
all_encodings.append(encoding)
|
||||
|
||||
# Combine batch
|
||||
batch_encoding = self._batch_encode(
|
||||
all_encodings,
|
||||
padding=padding,
|
||||
truncation=truncation,
|
||||
max_length=max_length,
|
||||
return_tensors=return_tensors,
|
||||
return_attention_mask=return_attention_mask,
|
||||
)
|
||||
|
||||
return batch_encoding
|
||||
|
||||
def _batch_encode(
|
||||
self,
|
||||
encodings: List[Dict[str, Any]],
|
||||
padding: Union[bool, str, PaddingStrategy] = True,
|
||||
truncation: Union[bool, str, TruncationStrategy] = False,
|
||||
max_length: Optional[int] = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
return_attention_mask: bool = True,
|
||||
) -> BatchEncoding:
|
||||
"""Combine multiple encodings into a batch with padding."""
|
||||
# Extract input_ids and create attention_mask
|
||||
input_ids_list = [enc["input_ids"] for enc in encodings]
|
||||
tts_lm_input_ids_list = [enc["tts_lm_input_ids"] for enc in encodings]
|
||||
tts_text_ids_list = [enc["tts_text_ids"] for enc in encodings]
|
||||
speech_input_masks_list = [enc["speech_input_mask"] for enc in encodings]
|
||||
|
||||
attention_masks = [[1] * len(ids) for ids in input_ids_list] if return_attention_mask else None
|
||||
tts_lm_attention_masks = [[1] * len(ids) for ids in tts_lm_input_ids_list] if return_attention_mask else None
|
||||
|
||||
# Process speech inputs
|
||||
all_speech_inputs = []
|
||||
has_speech = False
|
||||
for enc in encodings:
|
||||
if enc["speech_inputs"] is not None:
|
||||
all_speech_inputs.extend(enc["speech_inputs"])
|
||||
has_speech = True
|
||||
|
||||
# Prepare batch encoding
|
||||
batch_encoding = BatchEncoding()
|
||||
|
||||
# Handle tensor conversion
|
||||
if return_tensors is not None:
|
||||
batch_encoding["input_ids"] = torch.tensor(input_ids_list, dtype=torch.long)
|
||||
batch_encoding["tts_lm_input_ids"] = torch.tensor(tts_lm_input_ids_list, dtype=torch.long)
|
||||
batch_encoding["tts_text_ids"] = torch.tensor(tts_text_ids_list, dtype=torch.long)
|
||||
|
||||
if return_attention_mask and attention_masks is not None:
|
||||
batch_encoding["attention_mask"] = torch.tensor(attention_masks, dtype=torch.long)
|
||||
batch_encoding["tts_lm_attention_mask"] = torch.tensor(tts_lm_attention_masks, dtype=torch.long)
|
||||
|
||||
batch_encoding["speech_input_mask"] = torch.tensor(speech_input_masks_list, dtype=torch.bool)
|
||||
else:
|
||||
batch_encoding["input_ids"] = input_ids_list
|
||||
batch_encoding["tts_lm_input_ids"] = tts_lm_input_ids_list
|
||||
batch_encoding["tts_text_ids"] = tts_text_ids_list
|
||||
if return_attention_mask and attention_masks is not None:
|
||||
batch_encoding["attention_mask"] = attention_masks
|
||||
batch_encoding["tts_lm_attention_mask"] = tts_lm_attention_masks
|
||||
batch_encoding["speech_input_mask"] = speech_input_masks_list
|
||||
|
||||
# Process speech tensors if present
|
||||
if has_speech:
|
||||
speech_dict = self.prepare_speech_inputs(
|
||||
all_speech_inputs,
|
||||
return_tensors=return_tensors,
|
||||
)
|
||||
batch_encoding["speech_tensors"] = speech_dict["padded_speeches"]
|
||||
batch_encoding["speech_masks"] = speech_dict["speech_masks"]
|
||||
else:
|
||||
batch_encoding["speech_tensors"] = None
|
||||
batch_encoding["speech_masks"] = None
|
||||
|
||||
return batch_encoding
|
||||
|
||||
def prepare_speech_inputs(
|
||||
self,
|
||||
speech_inputs: List[np.ndarray],
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
device: Optional[Union[str, torch.device]] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Prepare speech inputs for model consumption.
|
||||
|
||||
Args:
|
||||
speech_inputs: List of speech arrays
|
||||
return_tensors: Output tensor type
|
||||
device: Device to place tensors on
|
||||
dtype: Data type for tensors
|
||||
|
||||
Returns:
|
||||
Dictionary with padded_speeches and speech_masks
|
||||
"""
|
||||
if not speech_inputs:
|
||||
return {"padded_speeches": None, "speech_masks": None}
|
||||
|
||||
# Calculate sequence lengths
|
||||
vae_tok_seqlens = [math.ceil(s.shape[0] / self.speech_tok_compress_ratio) for s in speech_inputs]
|
||||
# vae_tok_seqlens = [math.ceil(s.shape[0] / self.speech_tok_compress_ratio) if s.ndim == 1 else s.shape[0] for s in speech_inputs]
|
||||
max_speech_length = max(s.shape[0] for s in speech_inputs)
|
||||
|
||||
# Pad speeches
|
||||
if speech_inputs[0].ndim == 1:
|
||||
padded_speeches = np.full((len(speech_inputs), max_speech_length), fill_value=0, dtype=np.float32)
|
||||
else:
|
||||
padded_speeches = np.full((len(speech_inputs), max_speech_length, speech_inputs[0].shape[-1]), fill_value=0, dtype=np.float32)
|
||||
speech_masks = np.zeros((len(speech_inputs), max(vae_tok_seqlens)), dtype=np.bool_)
|
||||
|
||||
for i, (speech, vae_tok_length) in enumerate(zip(speech_inputs, vae_tok_seqlens)):
|
||||
padded_speeches[i, :len(speech)] = speech
|
||||
speech_masks[i, :vae_tok_length] = True
|
||||
|
||||
result = {
|
||||
"padded_speeches": padded_speeches,
|
||||
"speech_masks": speech_masks,
|
||||
}
|
||||
|
||||
# Convert to tensors if requested
|
||||
if return_tensors == "pt":
|
||||
result["padded_speeches"] = torch.tensor(padded_speeches, device=device, dtype=dtype or torch.float32)
|
||||
result["speech_masks"] = torch.tensor(speech_masks, device=device, dtype=torch.bool)
|
||||
|
||||
return result
|
||||
|
||||
def batch_decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to VibeVoiceTextTokenizer's [`~PreTrainedTokenizer.batch_decode`].
|
||||
Please refer to the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.batch_decode(*args, **kwargs)
|
||||
|
||||
def decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to VibeVoiceTextTokenizer's [`~PreTrainedTokenizer.decode`].
|
||||
Please refer to the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.decode(*args, **kwargs)
|
||||
|
||||
@property
|
||||
def model_input_names(self):
|
||||
"""
|
||||
Return the list of inputs accepted by the model.
|
||||
"""
|
||||
tokenizer_input_names = self.tokenizer.model_input_names
|
||||
audio_processor_input_names = self.audio_processor.model_input_names
|
||||
return list(dict.fromkeys(tokenizer_input_names + audio_processor_input_names + ["speech_inputs", "speech_input_mask"]))
|
||||
|
||||
def save_audio(self,
|
||||
audio: Union[torch.Tensor, np.ndarray, List[Union[torch.Tensor, np.ndarray]]],
|
||||
output_path: str = "output.wav",
|
||||
sampling_rate: Optional[int] = None,
|
||||
normalize: bool = False,
|
||||
batch_prefix: str = "audio_",
|
||||
) -> str:
|
||||
"""
|
||||
Save audio data to a file.
|
||||
Args:
|
||||
audio (Union[torch.Tensor, np.ndarray, List[Union[torch.Tensor, np.ndarray]]]):
|
||||
The audio data to save. Can be a single tensor/array or a list of them.
|
||||
output_path (str, optional): Path to save the audio file. Defaults to "output.wav".
|
||||
sampling_rate (int, optional): Sampling rate for the audio. If None, uses the processor's default.
|
||||
normalize (bool, optional): Whether to normalize the audio before saving. Defaults to False.
|
||||
batch_prefix (str, optional): Prefix for batch audio files. Defaults to "audio_".
|
||||
Returns:
|
||||
str: The path to the saved audio file.
|
||||
"""
|
||||
return self.audio_processor.save_audio(audio, output_path=output_path, sampling_rate=sampling_rate, normalize=normalize, batch_prefix=batch_prefix)
|
||||
|
||||
__all__ = [
|
||||
"VibeVoiceStreamingProcessor",
|
||||
]
|
||||
@@ -0,0 +1,413 @@
|
||||
"""
|
||||
Processor class for VibeVoice models.
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
import warnings
|
||||
from typing import List, Optional, Union, Dict, Any
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from transformers.feature_extraction_utils import FeatureExtractionMixin
|
||||
from transformers.utils import logging
|
||||
|
||||
from .audio_utils import AudioNormalizer
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
# Change from ProcessorMixin to FeatureExtractionMixin which is designed for single components
|
||||
class VibeVoiceTokenizerProcessor(FeatureExtractionMixin):
|
||||
"""
|
||||
Processor for VibeVoice acoustic tokenizer models.
|
||||
|
||||
This processor handles audio preprocessing for VibeVoice models, including:
|
||||
- Audio format conversion (stereo to mono)
|
||||
- Optional audio normalization
|
||||
- Streaming support for infinite-length audio
|
||||
|
||||
Args:
|
||||
sampling_rate (int, optional): Expected sampling rate. Defaults to 24000.
|
||||
normalize_audio (bool, optional): Whether to normalize audio. Defaults to True.
|
||||
target_dB_FS (float, optional): Target dB FS for normalization. Defaults to -25.
|
||||
eps (float, optional): Small value for numerical stability. Defaults to 1e-6.
|
||||
"""
|
||||
model_input_names = ["input_features"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sampling_rate: int = 24000,
|
||||
normalize_audio: bool = True,
|
||||
target_dB_FS: float = -25,
|
||||
eps: float = 1e-6,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.sampling_rate = sampling_rate
|
||||
self.normalize_audio = normalize_audio
|
||||
|
||||
# Initialize audio normalizer if needed
|
||||
if self.normalize_audio:
|
||||
self.normalizer = AudioNormalizer(target_dB_FS=target_dB_FS, eps=eps)
|
||||
else:
|
||||
self.normalizer = None
|
||||
|
||||
# Save config
|
||||
self.feature_extractor_dict = {
|
||||
"sampling_rate": sampling_rate,
|
||||
"normalize_audio": normalize_audio,
|
||||
"target_dB_FS": target_dB_FS,
|
||||
"eps": eps,
|
||||
}
|
||||
|
||||
def _ensure_mono(self, audio: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Convert stereo audio to mono if needed.
|
||||
|
||||
Args:
|
||||
audio (np.ndarray): Input audio array
|
||||
|
||||
Returns:
|
||||
np.ndarray: Mono audio array
|
||||
"""
|
||||
if len(audio.shape) == 1:
|
||||
return audio
|
||||
elif len(audio.shape) == 2:
|
||||
if audio.shape[0] == 2: # (2, time)
|
||||
return np.mean(audio, axis=0)
|
||||
elif audio.shape[1] == 2: # (time, 2)
|
||||
return np.mean(audio, axis=1)
|
||||
else:
|
||||
# If one dimension is 1, squeeze it
|
||||
if audio.shape[0] == 1:
|
||||
return audio.squeeze(0)
|
||||
elif audio.shape[1] == 1:
|
||||
return audio.squeeze(1)
|
||||
else:
|
||||
raise ValueError(f"Unexpected audio shape: {audio.shape}")
|
||||
else:
|
||||
raise ValueError(f"Audio should be 1D or 2D, got shape: {audio.shape}")
|
||||
|
||||
def _process_single_audio(self, audio: Union[np.ndarray, List[float]]) -> np.ndarray:
|
||||
"""
|
||||
Process a single audio array.
|
||||
|
||||
Args:
|
||||
audio: Single audio input
|
||||
|
||||
Returns:
|
||||
np.ndarray: Processed audio
|
||||
"""
|
||||
# Convert to numpy array
|
||||
if not isinstance(audio, np.ndarray):
|
||||
audio = np.array(audio, dtype=np.float32)
|
||||
else:
|
||||
audio = audio.astype(np.float32)
|
||||
|
||||
# Ensure mono
|
||||
audio = self._ensure_mono(audio)
|
||||
|
||||
# Normalize if requested
|
||||
if self.normalize_audio and self.normalizer is not None:
|
||||
audio = self.normalizer(audio)
|
||||
|
||||
return audio
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
audio: Union[str, np.ndarray, List[float], List[np.ndarray], List[List[float]], List[str]] = None,
|
||||
sampling_rate: Optional[int] = None,
|
||||
return_tensors: Optional[str] = None,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Process audio for VibeVoice models.
|
||||
|
||||
Args:
|
||||
audio: Audio input(s) to process. Can be:
|
||||
- str: Path to audio file
|
||||
- np.ndarray: Audio array
|
||||
- List[float]: Audio as list of floats
|
||||
- List[np.ndarray]: Batch of audio arrays
|
||||
- List[str]: Batch of audio file paths
|
||||
sampling_rate (int, optional): Sampling rate of the input audio
|
||||
return_tensors (str, optional): Return format ('pt' for PyTorch, 'np' for NumPy)
|
||||
|
||||
Returns:
|
||||
dict: Processed audio inputs with keys:
|
||||
- input_features: Audio tensor(s) ready for the model
|
||||
"""
|
||||
if audio is None:
|
||||
raise ValueError("Audio input is required")
|
||||
|
||||
# Validate sampling rate
|
||||
if sampling_rate is not None and sampling_rate != self.sampling_rate:
|
||||
logger.warning(
|
||||
f"Input sampling rate ({sampling_rate}) differs from expected "
|
||||
f"sampling rate ({self.sampling_rate}). Please resample your audio."
|
||||
)
|
||||
|
||||
# Handle different input types
|
||||
if isinstance(audio, str):
|
||||
# Single audio file path
|
||||
audio = self._load_audio_from_path(audio)
|
||||
is_batched = False
|
||||
elif isinstance(audio, list):
|
||||
if len(audio) == 0:
|
||||
raise ValueError("Empty audio list provided")
|
||||
|
||||
# Check if it's a list of file paths
|
||||
if all(isinstance(item, str) for item in audio):
|
||||
# Batch of audio file paths
|
||||
audio = [self._load_audio_from_path(path) for path in audio]
|
||||
is_batched = True
|
||||
else:
|
||||
# Check if it's batched audio arrays
|
||||
is_batched = isinstance(audio[0], (np.ndarray, list))
|
||||
else:
|
||||
# Single audio array or list
|
||||
is_batched = False
|
||||
|
||||
# Process audio
|
||||
if is_batched:
|
||||
processed_audio = [self._process_single_audio(a) for a in audio]
|
||||
else:
|
||||
processed_audio = [self._process_single_audio(audio)]
|
||||
|
||||
# Convert to tensors if requested
|
||||
if return_tensors == "pt":
|
||||
if len(processed_audio) == 1:
|
||||
# Create a proper batch dimension (B, T)
|
||||
input_features = torch.from_numpy(processed_audio[0]).unsqueeze(0).unsqueeze(1)
|
||||
else:
|
||||
# For batched input with different lengths, create a batch properly
|
||||
input_features = torch.stack([torch.from_numpy(a) for a in processed_audio]).unsqueeze(1)
|
||||
elif return_tensors == "np":
|
||||
if len(processed_audio) == 1:
|
||||
input_features = processed_audio[0][np.newaxis, np.newaxis, :]
|
||||
else:
|
||||
input_features = np.stack(processed_audio)[:, np.newaxis, :]
|
||||
else:
|
||||
input_features = processed_audio[0] if len(processed_audio) == 1 else processed_audio
|
||||
|
||||
outputs = {
|
||||
"audio": input_features, # Use "audio" instead of "input_features"
|
||||
}
|
||||
|
||||
return outputs
|
||||
|
||||
def _load_audio_from_path(self, audio_path: str) -> np.ndarray:
|
||||
"""
|
||||
Load audio from file path.
|
||||
|
||||
Args:
|
||||
audio_path (str): Path to audio file
|
||||
|
||||
Returns:
|
||||
np.ndarray: Loaded audio array
|
||||
"""
|
||||
# Get file extension to determine loading method
|
||||
file_ext = os.path.splitext(audio_path)[1].lower()
|
||||
|
||||
if file_ext in ['.wav', '.mp3', '.flac', '.m4a', '.ogg']:
|
||||
# Audio file - use librosa
|
||||
import librosa
|
||||
audio_array, sr = librosa.load(
|
||||
audio_path,
|
||||
sr=self.sampling_rate,
|
||||
mono=True
|
||||
)
|
||||
return audio_array
|
||||
elif file_ext == '.pt':
|
||||
# PyTorch tensor file
|
||||
audio_tensor = torch.load(audio_path, map_location='cpu', weights_only=True).squeeze()
|
||||
if isinstance(audio_tensor, torch.Tensor):
|
||||
audio_array = audio_tensor.numpy()
|
||||
else:
|
||||
audio_array = np.array(audio_tensor)
|
||||
return audio_array.astype(np.float32)
|
||||
elif file_ext == '.npy':
|
||||
# NumPy file
|
||||
audio_array = np.load(audio_path)
|
||||
return audio_array.astype(np.float32)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported file format: {file_ext}. "
|
||||
f"Supported formats: .wav, .mp3, .flac, .m4a, .ogg, .pt, .npy, .npz"
|
||||
)
|
||||
|
||||
def preprocess_audio(
|
||||
self,
|
||||
audio_path_or_array: Union[str, np.ndarray],
|
||||
normalize: Optional[bool] = None,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Convenience method to preprocess audio from file path or array.
|
||||
This method is kept for backward compatibility but __call__ is recommended.
|
||||
|
||||
Args:
|
||||
audio_path_or_array: Path to audio file or numpy array
|
||||
normalize: Whether to normalize (overrides default setting)
|
||||
|
||||
Returns:
|
||||
np.ndarray: Preprocessed audio array
|
||||
"""
|
||||
if isinstance(audio_path_or_array, str):
|
||||
audio_array = self._load_audio_from_path(audio_path_or_array)
|
||||
else:
|
||||
audio_array = np.array(audio_path_or_array, dtype=np.float32)
|
||||
|
||||
# Override normalization setting if specified
|
||||
original_normalize = self.normalize_audio
|
||||
if normalize is not None:
|
||||
self.normalize_audio = normalize
|
||||
|
||||
try:
|
||||
processed = self._process_single_audio(audio_array)
|
||||
finally:
|
||||
# Restore original setting
|
||||
self.normalize_audio = original_normalize
|
||||
|
||||
return processed
|
||||
|
||||
# Override to_dict method for configuration saving
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Convert the object to a dict containing all attributes needed for serialization.
|
||||
"""
|
||||
return self.feature_extractor_dict
|
||||
|
||||
def save_audio(
|
||||
self,
|
||||
audio: Union[torch.Tensor, np.ndarray, List[Union[torch.Tensor, np.ndarray]]],
|
||||
output_path: str = "output.wav",
|
||||
sampling_rate: Optional[int] = None,
|
||||
normalize: bool = False,
|
||||
batch_prefix: str = "audio_",
|
||||
):
|
||||
"""
|
||||
Save audio data to WAV file(s).
|
||||
|
||||
Args:
|
||||
audio: Audio data to save. Can be:
|
||||
- torch.Tensor: PyTorch tensor with shape (B, C, T) or (B, T) or (T)
|
||||
- np.ndarray: NumPy array with shape (B, C, T) or (B, T) or (T)
|
||||
- List of tensors or arrays
|
||||
output_path: Path where to save the audio. If saving multiple files,
|
||||
this is treated as a directory and individual files will be saved inside.
|
||||
sampling_rate: Sampling rate for the saved audio. Defaults to the processor's rate.
|
||||
normalize: Whether to normalize audio before saving.
|
||||
batch_prefix: Prefix for batch files when saving multiple audios.
|
||||
|
||||
Returns:
|
||||
List[str]: Paths to the saved audio files.
|
||||
"""
|
||||
if sampling_rate is None:
|
||||
sampling_rate = self.sampling_rate
|
||||
|
||||
try:
|
||||
import soundfile as sf
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"soundfile is required to save audio files. "
|
||||
"Install it with: pip install soundfile"
|
||||
)
|
||||
|
||||
# Ensure audio is in the right format
|
||||
if isinstance(audio, torch.Tensor):
|
||||
# Convert PyTorch tensor to numpy
|
||||
audio_np = audio.float().detach().cpu().numpy()
|
||||
elif isinstance(audio, np.ndarray):
|
||||
audio_np = audio
|
||||
elif isinstance(audio, list):
|
||||
# Handle list of tensors or arrays
|
||||
if all(isinstance(a, torch.Tensor) for a in audio):
|
||||
audio_np = [a.float().detach().cpu().numpy() for a in audio]
|
||||
else:
|
||||
audio_np = audio
|
||||
else:
|
||||
raise ValueError(f"Unsupported audio type: {type(audio)}")
|
||||
|
||||
saved_paths = []
|
||||
|
||||
# Handle based on shape or type
|
||||
if isinstance(audio_np, list):
|
||||
# Multiple separate audios to save
|
||||
output_dir = output_path
|
||||
|
||||
# Ensure output directory exists
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
# Save each audio
|
||||
for i, audio_item in enumerate(audio_np):
|
||||
audio_item = self._prepare_audio_for_save(audio_item, normalize)
|
||||
file_path = os.path.join(output_dir, f"{batch_prefix}{i}.wav")
|
||||
sf.write(file_path, audio_item, sampling_rate)
|
||||
saved_paths.append(file_path)
|
||||
|
||||
else:
|
||||
# Handle different dimensions
|
||||
if len(audio_np.shape) >= 3: # (B, C, T) or similar
|
||||
# Get batch size
|
||||
batch_size = audio_np.shape[0]
|
||||
|
||||
if batch_size > 1:
|
||||
# Multiple audios in a batch
|
||||
output_dir = output_path
|
||||
|
||||
# Ensure output directory exists
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
# Save each audio in the batch
|
||||
for i in range(batch_size):
|
||||
# Extract single audio and remove channel dim if present
|
||||
single_audio = audio_np[i]
|
||||
if len(single_audio.shape) > 1:
|
||||
if single_audio.shape[0] == 1: # (1, T)
|
||||
single_audio = single_audio.squeeze(0)
|
||||
|
||||
single_audio = self._prepare_audio_for_save(single_audio, normalize)
|
||||
file_path = os.path.join(output_dir, f"{batch_prefix}{i}.wav")
|
||||
sf.write(file_path, single_audio, sampling_rate)
|
||||
saved_paths.append(file_path)
|
||||
else:
|
||||
# Single audio with batch and channel dims
|
||||
audio_item = audio_np.squeeze() # Remove batch and channel dimensions
|
||||
audio_item = self._prepare_audio_for_save(audio_item, normalize)
|
||||
sf.write(output_path, audio_item, sampling_rate)
|
||||
saved_paths.append(output_path)
|
||||
else:
|
||||
# Single audio without batch dimension
|
||||
audio_item = self._prepare_audio_for_save(audio_np, normalize)
|
||||
sf.write(output_path, audio_item, sampling_rate)
|
||||
saved_paths.append(output_path)
|
||||
|
||||
return saved_paths
|
||||
|
||||
def _prepare_audio_for_save(self, audio: np.ndarray, normalize: bool) -> np.ndarray:
|
||||
"""
|
||||
Prepare audio for saving by ensuring it's the right shape and optionally normalizing.
|
||||
|
||||
Args:
|
||||
audio: Audio data as numpy array
|
||||
normalize: Whether to normalize audio
|
||||
|
||||
Returns:
|
||||
np.ndarray: Processed audio ready for saving
|
||||
"""
|
||||
# Ensure right dimensionality
|
||||
if len(audio.shape) > 1 and audio.shape[0] == 1: # (1, T)
|
||||
audio = audio.squeeze(0)
|
||||
|
||||
# Normalize if requested
|
||||
if normalize:
|
||||
max_val = np.abs(audio).max()
|
||||
if max_val > 0:
|
||||
audio = audio / max_val
|
||||
|
||||
return audio
|
||||
|
||||
|
||||
__all__ = ["VibeVoiceTokenizerProcessor", "AudioNormalizer"]
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,19 @@
|
||||
import math
|
||||
import torch
|
||||
|
||||
|
||||
class UniformSampler:
|
||||
def __init__(self, timesteps = 1000):
|
||||
self.timesteps = timesteps
|
||||
def sample(self, batch_size, device):
|
||||
return torch.randint(0, self.timesteps, (batch_size,), device=device)
|
||||
|
||||
class LogitNormalSampler:
|
||||
def __init__(self, timesteps = 1000, m = 0, s = 1):
|
||||
self.timesteps = timesteps
|
||||
timesteps = torch.linspace(0, 1, timesteps)
|
||||
logit = torch.log(timesteps / (1 - timesteps))
|
||||
self.prob = torch.exp(-0.5 * (logit - m) ** 2 / s ** 2) / (s * math.sqrt(2 * math.pi))
|
||||
def sample(self, batch_size, device):
|
||||
return torch.multinomial(self.prob, batch_size, replacement=True).to(device)
|
||||
|
||||
@@ -0,0 +1,62 @@
|
||||
"""VibeVoice vLLM Plugin - Registers VibeVoice model for vLLM inference.
|
||||
|
||||
This plugin enables VibeVoice ASR models to be loaded and served through vLLM.
|
||||
It registers the model architecture, configuration, tokenizer, and processor
|
||||
with their respective registries.
|
||||
|
||||
The plugin is automatically loaded by vLLM via the 'vllm.general_plugins'
|
||||
entry point defined in pyproject.toml.
|
||||
"""
|
||||
|
||||
from vllm.model_executor.models import ModelRegistry
|
||||
from transformers import AutoConfig, AutoTokenizer, Qwen2Tokenizer, AutoProcessor, Qwen2AudioProcessor
|
||||
|
||||
from vibevoice.modular.configuration_vibevoice import VibeVoiceConfig
|
||||
from vibevoice.modular.modular_vibevoice_text_tokenizer import VibeVoiceASRTextTokenizerFast
|
||||
|
||||
from .model import VibeVoiceForCausalLM
|
||||
|
||||
|
||||
def register_vibevoice():
|
||||
"""Register VibeVoice model with vLLM and transformers.
|
||||
|
||||
This function is called automatically by vLLM through the entry point
|
||||
mechanism. It registers:
|
||||
- VibeVoiceConfig with AutoConfig
|
||||
- VibeVoiceASRTextTokenizerFast with AutoTokenizer (for ASR)
|
||||
- Qwen2AudioProcessor with AutoProcessor
|
||||
- VibeVoiceForCausalLM with vLLM ModelRegistry
|
||||
"""
|
||||
# Register the configuration class with transformers
|
||||
AutoConfig.register("vibevoice", VibeVoiceConfig)
|
||||
|
||||
# Register the tokenizer with transformers.
|
||||
# IMPORTANT (ASR): Align with the PyTorch ASR path.
|
||||
# VibeVoiceASRTextTokenizerFast maps:
|
||||
# speech_start_id -> <|object_ref_start|>
|
||||
# speech_pad_id -> <|box_start|>
|
||||
# speech_end_id -> <|object_ref_end|>
|
||||
# This significantly affects ASR quality even when requests succeed.
|
||||
try:
|
||||
AutoTokenizer.register(
|
||||
VibeVoiceConfig,
|
||||
slow_tokenizer_class=Qwen2Tokenizer,
|
||||
fast_tokenizer_class=VibeVoiceASRTextTokenizerFast,
|
||||
)
|
||||
except Exception:
|
||||
pass # May already be registered
|
||||
|
||||
# Register the processor with transformers
|
||||
try:
|
||||
AutoProcessor.register(VibeVoiceConfig, processor_class=Qwen2AudioProcessor)
|
||||
except Exception:
|
||||
pass # May already be registered
|
||||
|
||||
# Register the model class with the architecture name "VibeVoice"
|
||||
# This name must match the "architectures" list in config.json
|
||||
ModelRegistry.register_model("VibeVoice", VibeVoiceForCausalLM)
|
||||
ModelRegistry.register_model("VibeVoiceForASRTraining", VibeVoiceForCausalLM)
|
||||
|
||||
|
||||
# Note: This function is called via vllm.general_plugins entry point
|
||||
# defined in pyproject.toml, ensuring it runs in all vLLM processes
|
||||
@@ -0,0 +1,101 @@
|
||||
"""Audio input mapper for vLLM multimodal pipeline.
|
||||
|
||||
This module handles audio data loading and preprocessing for VibeVoice ASR inference.
|
||||
It converts various audio input formats (path, bytes, numpy array) into tensors
|
||||
that can be processed by the VibeVoice model.
|
||||
"""
|
||||
import os
|
||||
import logging
|
||||
import torch
|
||||
import numpy as np
|
||||
from typing import Union, List
|
||||
from vllm.multimodal.inputs import MultiModalInputs
|
||||
from vibevoice.processor.audio_utils import load_audio_use_ffmpeg, load_audio_bytes_use_ffmpeg, AudioNormalizer
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Maximum audio duration in seconds. Default 3660s (61 minutes) matches the
|
||||
# model's designed capacity. Override via environment variable to guard against
|
||||
# OOM on GPUs with less VRAM. See https://github.com/microsoft/VibeVoice/issues/210
|
||||
_MAX_AUDIO_DURATION = float(os.environ.get("VIBEVOICE_MAX_AUDIO_DURATION", "3660"))
|
||||
|
||||
|
||||
def load_audio(audio_path: str, target_sr: int = 24000) -> np.ndarray:
|
||||
"""Load and normalize audio from file path.
|
||||
|
||||
Args:
|
||||
audio_path: Path to audio file
|
||||
target_sr: Target sample rate (default 24kHz for VibeVoice)
|
||||
|
||||
Returns:
|
||||
Normalized audio waveform as numpy array
|
||||
"""
|
||||
# Load with FFmpeg (handles various formats)
|
||||
audio, sr = load_audio_use_ffmpeg(audio_path, resample=True, target_sr=target_sr)
|
||||
|
||||
# Normalize audio
|
||||
normalizer = AudioNormalizer()
|
||||
audio = normalizer(audio)
|
||||
|
||||
return audio
|
||||
|
||||
|
||||
def vibevoice_audio_input_mapper(ctx, data: Union[str, bytes, np.ndarray, List[str]]) -> MultiModalInputs:
|
||||
"""Map audio input data to vLLM MultiModalInputs format.
|
||||
|
||||
This function is registered as the input mapper for VibeVoice audio processing.
|
||||
It handles multiple input formats and converts them to normalized tensors.
|
||||
|
||||
Args:
|
||||
ctx: vLLM context (unused)
|
||||
data: Audio data in one of these formats:
|
||||
- str: Path to audio file
|
||||
- bytes: Raw audio bytes (any format FFmpeg supports)
|
||||
- np.ndarray: Pre-loaded audio waveform
|
||||
- List[str]: List of audio paths (only first is used)
|
||||
|
||||
Returns:
|
||||
MultiModalInputs containing:
|
||||
- audio: Audio tensor (float32)
|
||||
- audio_length: Length of audio in samples
|
||||
"""
|
||||
# Handle list input (take first item)
|
||||
if isinstance(data, list):
|
||||
data = data[0]
|
||||
|
||||
audio_waveform = None
|
||||
|
||||
if isinstance(data, str):
|
||||
# Load from file path
|
||||
audio_waveform = load_audio(data)
|
||||
|
||||
elif isinstance(data, bytes):
|
||||
# Decode bytes directly via ffmpeg stdin pipe to avoid temp-file IO
|
||||
audio_waveform, _sr = load_audio_bytes_use_ffmpeg(data, resample=True, target_sr=24000)
|
||||
normalizer = AudioNormalizer()
|
||||
audio_waveform = normalizer(audio_waveform)
|
||||
|
||||
elif isinstance(data, np.ndarray):
|
||||
# Already loaded numpy array
|
||||
audio_waveform = data
|
||||
else:
|
||||
raise ValueError(f"Unsupported audio data type: {type(data)}")
|
||||
|
||||
# Validate audio duration before tensor conversion to catch OOM early
|
||||
duration_sec = len(audio_waveform) / 24000
|
||||
if duration_sec > _MAX_AUDIO_DURATION:
|
||||
raise ValueError(
|
||||
f"Audio duration ({duration_sec:.1f}s) exceeds the configured "
|
||||
f"limit ({_MAX_AUDIO_DURATION:.0f}s). Set the "
|
||||
f"VIBEVOICE_MAX_AUDIO_DURATION environment variable to adjust "
|
||||
f"this limit, or use shorter audio."
|
||||
)
|
||||
|
||||
# Convert to tensor
|
||||
audio_tensor = torch.from_numpy(audio_waveform).float()
|
||||
audio_length = audio_tensor.shape[0]
|
||||
|
||||
return MultiModalInputs({
|
||||
"audio": audio_tensor,
|
||||
"audio_length": audio_length
|
||||
})
|
||||
File diff suppressed because it is too large
Load Diff
+2209
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,451 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
VibeVoice vLLM ASR Server Launcher
|
||||
|
||||
One-click deployment script that handles:
|
||||
1. Installing system dependencies (FFmpeg, etc.)
|
||||
2. Installing VibeVoice Python package
|
||||
3. Downloading model from HuggingFace
|
||||
4. Generating tokenizer files
|
||||
5. Starting vLLM server
|
||||
|
||||
For DP > 1, launches N independent vLLM processes behind an nginx
|
||||
reverse proxy for optimal throughput (avoids single-process HTTP
|
||||
bottleneck of vLLM's built-in DP coordinator).
|
||||
|
||||
Usage:
|
||||
python3 start_server.py [--model MODEL_ID] [--port PORT]
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import signal
|
||||
import subprocess
|
||||
import sys
|
||||
import textwrap
|
||||
import time
|
||||
|
||||
|
||||
def run_command(cmd: list[str], description: str, shell: bool = False) -> None:
|
||||
"""Run a command with logging."""
|
||||
print(f"\n{'='*60}")
|
||||
print(f" {description}")
|
||||
print(f"{'='*60}\n")
|
||||
if shell:
|
||||
subprocess.run(cmd, shell=True, check=True)
|
||||
else:
|
||||
subprocess.run(cmd, check=True)
|
||||
|
||||
|
||||
def install_system_deps() -> None:
|
||||
"""Install system dependencies (FFmpeg, etc.)."""
|
||||
run_command(["apt-get", "update"], "Updating package list")
|
||||
run_command(
|
||||
["apt-get", "install", "-y", "ffmpeg", "libsndfile1"],
|
||||
"Installing FFmpeg and audio libraries"
|
||||
)
|
||||
|
||||
|
||||
def install_vibevoice() -> None:
|
||||
"""Install VibeVoice Python package."""
|
||||
run_command(
|
||||
[sys.executable, "-m", "pip", "install", "-e", "/app[vllm]"],
|
||||
"Installing VibeVoice with vLLM support"
|
||||
)
|
||||
|
||||
|
||||
def download_model(model_id: str) -> str:
|
||||
"""Download model from HuggingFace using default cache."""
|
||||
print(f"\n{'='*60}")
|
||||
print(f" Downloading model: {model_id}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
import warnings
|
||||
from huggingface_hub import snapshot_download
|
||||
|
||||
# Suppress deprecation warnings from huggingface_hub
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
model_path = snapshot_download(model_id)
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f" ✅ Model downloaded successfully!")
|
||||
print(f" 📁 Path: {model_path}")
|
||||
print(f"{'='*60}\n")
|
||||
return model_path
|
||||
|
||||
|
||||
def generate_tokenizer(model_path: str) -> None:
|
||||
"""Generate tokenizer files for the model."""
|
||||
run_command(
|
||||
[sys.executable, "-m", "vllm_plugin.tools.generate_tokenizer_files",
|
||||
"--output", model_path],
|
||||
"Generating tokenizer files"
|
||||
)
|
||||
|
||||
|
||||
def _build_vllm_cmd(model_path: str, port: int,
|
||||
tensor_parallel_size: int = 1,
|
||||
data_parallel_size: int = 1,
|
||||
max_num_seqs: int = 64,
|
||||
max_model_len: int = 65536,
|
||||
gpu_memory_utilization: float = 0.8) -> list[str]:
|
||||
"""Build the vllm serve command."""
|
||||
return [
|
||||
"vllm", "serve", model_path,
|
||||
"--served-model-name", "vibevoice",
|
||||
"--trust-remote-code",
|
||||
"--dtype", "bfloat16",
|
||||
"--max-num-seqs", str(max_num_seqs),
|
||||
"--max-model-len", str(max_model_len),
|
||||
"--gpu-memory-utilization", str(gpu_memory_utilization),
|
||||
"--no-enable-prefix-caching",
|
||||
"--enable-chunked-prefill",
|
||||
"--chat-template-content-format", "openai",
|
||||
"--tensor-parallel-size", str(tensor_parallel_size),
|
||||
"--data-parallel-size", str(data_parallel_size),
|
||||
"--allowed-local-media-path", "/app",
|
||||
"--port", str(port),
|
||||
]
|
||||
|
||||
|
||||
def start_vllm_server(model_path: str, port: int,
|
||||
tensor_parallel_size: int = 1,
|
||||
data_parallel_size: int = 1,
|
||||
max_num_seqs: int = 64,
|
||||
max_model_len: int = 65536,
|
||||
gpu_memory_utilization: float = 0.8) -> None:
|
||||
"""Start a single vLLM server (replaces current process)."""
|
||||
print(f"\n{'='*60}")
|
||||
print(f" Starting vLLM server on port {port}")
|
||||
print(f" Tensor Parallel (TP): {tensor_parallel_size}")
|
||||
print(f" Data Parallel (DP): {data_parallel_size}")
|
||||
print(f" Max Num Seqs: {max_num_seqs}")
|
||||
print(f" Max Model Len: {max_model_len}")
|
||||
print(f" GPU Mem Utilization: {gpu_memory_utilization}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
vllm_cmd = _build_vllm_cmd(
|
||||
model_path, port,
|
||||
tensor_parallel_size=tensor_parallel_size,
|
||||
data_parallel_size=data_parallel_size,
|
||||
max_num_seqs=max_num_seqs,
|
||||
max_model_len=max_model_len,
|
||||
gpu_memory_utilization=gpu_memory_utilization,
|
||||
)
|
||||
os.execvp("vllm", vllm_cmd)
|
||||
|
||||
|
||||
def _install_nginx() -> None:
|
||||
"""Install nginx if not already available."""
|
||||
if subprocess.run(["which", "nginx"], capture_output=True).returncode != 0:
|
||||
run_command(["apt-get", "update"], "Updating package list for nginx")
|
||||
run_command(
|
||||
["apt-get", "install", "-y", "nginx"],
|
||||
"Installing nginx for load balancing"
|
||||
)
|
||||
|
||||
|
||||
def _write_nginx_config(frontend_port: int, backend_ports: list[int],
|
||||
num_workers: int = 0) -> str:
|
||||
"""Write nginx config for round-robin load balancing.
|
||||
|
||||
Args:
|
||||
num_workers: Number of nginx worker processes. 0 = auto (2 × num backends).
|
||||
"""
|
||||
if num_workers <= 0:
|
||||
num_workers = len(backend_ports) * 2
|
||||
backends = "\n".join(f" server 127.0.0.1:{p};" for p in backend_ports)
|
||||
config = textwrap.dedent(f"""\
|
||||
worker_processes {num_workers};
|
||||
worker_rlimit_nofile 65536;
|
||||
error_log /dev/stderr warn;
|
||||
pid /tmp/nginx.pid;
|
||||
|
||||
events {{
|
||||
worker_connections 8192;
|
||||
}}
|
||||
|
||||
http {{
|
||||
access_log off;
|
||||
|
||||
upstream vllm_backends {{
|
||||
least_conn;
|
||||
{backends}
|
||||
}}
|
||||
|
||||
server {{
|
||||
listen {frontend_port};
|
||||
client_max_body_size 200m;
|
||||
client_body_buffer_size 10m;
|
||||
proxy_buffering on;
|
||||
proxy_buffer_size 64k;
|
||||
proxy_buffers 16 64k;
|
||||
|
||||
location / {{
|
||||
proxy_pass http://vllm_backends;
|
||||
proxy_read_timeout 600s;
|
||||
proxy_connect_timeout 10s;
|
||||
proxy_send_timeout 600s;
|
||||
proxy_http_version 1.1;
|
||||
proxy_set_header Connection "";
|
||||
}}
|
||||
}}
|
||||
}}
|
||||
""")
|
||||
config_path = "/tmp/nginx_vllm.conf"
|
||||
with open(config_path, "w") as f:
|
||||
f.write(config)
|
||||
return config_path
|
||||
|
||||
|
||||
def start_dp_server(model_path: str, frontend_port: int,
|
||||
data_parallel_size: int,
|
||||
tensor_parallel_size: int = 1,
|
||||
max_num_seqs: int = 64,
|
||||
max_model_len: int = 65536,
|
||||
gpu_memory_utilization: float = 0.8) -> None:
|
||||
"""Start multiple vLLM workers behind nginx for data parallelism.
|
||||
|
||||
Launches N independent vLLM processes (one per GPU group) on internal
|
||||
ports, with an nginx reverse proxy on the frontend port for load
|
||||
balancing. This avoids the single-process HTTP bottleneck of vLLM's
|
||||
built-in DP coordinator when handling large audio payloads.
|
||||
"""
|
||||
import torch
|
||||
num_gpus = torch.cuda.device_count()
|
||||
gpus_per_replica = tensor_parallel_size
|
||||
total_gpus_needed = data_parallel_size * gpus_per_replica
|
||||
assert num_gpus >= total_gpus_needed, (
|
||||
f"Need {total_gpus_needed} GPUs (dp={data_parallel_size} × tp={tensor_parallel_size}) "
|
||||
f"but only {num_gpus} available"
|
||||
)
|
||||
|
||||
# Auto-tune per-worker env vars based on dp size
|
||||
ffmpeg_concurrency = max(
|
||||
64, int(os.environ.get("VIBEVOICE_FFMPEG_MAX_CONCURRENCY", "64"))
|
||||
)
|
||||
media_threads = max(
|
||||
8, int(os.environ.get("VLLM_MEDIA_LOADING_THREAD_COUNT", "8"))
|
||||
)
|
||||
|
||||
_install_nginx()
|
||||
|
||||
# Assign internal ports: frontend_port + 100, +101, ...
|
||||
backend_ports = [frontend_port + 100 + i for i in range(data_parallel_size)]
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f" Starting DP server with nginx load balancing")
|
||||
print(f" Frontend port: {frontend_port} (nginx)")
|
||||
print(f" Backend ports: {backend_ports}")
|
||||
print(f" Data Parallel: {data_parallel_size}")
|
||||
print(f" Tensor Parallel: {tensor_parallel_size}")
|
||||
print(f" GPUs per replica: {gpus_per_replica}")
|
||||
print(f" Max Num Seqs: {max_num_seqs}")
|
||||
print(f" Max Model Len: {max_model_len}")
|
||||
print(f" FFmpeg concurrency (per worker): {ffmpeg_concurrency}")
|
||||
print(f" Media loading threads (per worker): {media_threads}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
# Write nginx config
|
||||
nginx_conf = _write_nginx_config(frontend_port, backend_ports)
|
||||
|
||||
# Launch vLLM workers
|
||||
workers: list[subprocess.Popen] = []
|
||||
for rank in range(data_parallel_size):
|
||||
gpu_start = rank * gpus_per_replica
|
||||
gpu_ids = ",".join(str(gpu_start + j) for j in range(gpus_per_replica))
|
||||
port = backend_ports[rank]
|
||||
|
||||
env = os.environ.copy()
|
||||
env["CUDA_VISIBLE_DEVICES"] = gpu_ids
|
||||
env["VIBEVOICE_FFMPEG_MAX_CONCURRENCY"] = str(ffmpeg_concurrency)
|
||||
env["VLLM_MEDIA_LOADING_THREAD_COUNT"] = str(media_threads)
|
||||
|
||||
vllm_cmd = _build_vllm_cmd(
|
||||
model_path, port,
|
||||
tensor_parallel_size=tensor_parallel_size,
|
||||
data_parallel_size=1, # Each worker is dp=1
|
||||
max_num_seqs=max_num_seqs,
|
||||
max_model_len=max_model_len,
|
||||
gpu_memory_utilization=gpu_memory_utilization,
|
||||
)
|
||||
|
||||
print(f" Launching worker rank={rank} on GPU(s) {gpu_ids}, port {port}")
|
||||
proc = subprocess.Popen(vllm_cmd, env=env)
|
||||
workers.append(proc)
|
||||
|
||||
# Start nginx
|
||||
print(f"\n Starting nginx on port {frontend_port} ...")
|
||||
nginx_proc = subprocess.Popen(
|
||||
["nginx", "-c", nginx_conf, "-g", "daemon off;"]
|
||||
)
|
||||
|
||||
# Wait for all backends to be ready
|
||||
print(" Waiting for all backends to be ready ...")
|
||||
import urllib.request
|
||||
for port in backend_ports:
|
||||
url = f"http://127.0.0.1:{port}/v1/models"
|
||||
for attempt in range(600): # up to 10 minutes
|
||||
try:
|
||||
urllib.request.urlopen(url, timeout=2)
|
||||
print(f" ✅ Backend on port {port} is ready")
|
||||
break
|
||||
except Exception:
|
||||
time.sleep(1)
|
||||
else:
|
||||
print(f" ❌ Backend on port {port} failed to start")
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f" ✅ VibeVoice DP server ready on port {frontend_port}")
|
||||
print(f" {data_parallel_size} replicas behind nginx load balancer")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
# Handle shutdown: forward signals to all children
|
||||
def _shutdown(signum, frame):
|
||||
print("\nShutting down ...")
|
||||
nginx_proc.terminate()
|
||||
for w in workers:
|
||||
w.terminate()
|
||||
for w in workers:
|
||||
w.wait(timeout=10)
|
||||
nginx_proc.wait(timeout=5)
|
||||
sys.exit(0)
|
||||
|
||||
signal.signal(signal.SIGTERM, _shutdown)
|
||||
signal.signal(signal.SIGINT, _shutdown)
|
||||
|
||||
# Wait for any child to exit (indicates a failure)
|
||||
while True:
|
||||
for i, w in enumerate(workers):
|
||||
ret = w.poll()
|
||||
if ret is not None:
|
||||
print(f" ❌ Worker {i} exited with code {ret}")
|
||||
_shutdown(None, None)
|
||||
if nginx_proc.poll() is not None:
|
||||
print(f" ❌ nginx exited with code {nginx_proc.returncode}")
|
||||
_shutdown(None, None)
|
||||
time.sleep(1)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="VibeVoice vLLM ASR Server - One-Click Deployment",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Examples:
|
||||
# Start with default settings (single GPU)
|
||||
python3 start_server.py
|
||||
|
||||
# Use custom port
|
||||
python3 start_server.py --port 8080
|
||||
|
||||
# Data parallel: 4 replicas on 4 GPUs (nginx load balancing)
|
||||
python3 start_server.py --dp 4
|
||||
|
||||
# Tensor parallel: split model across 2 GPUs
|
||||
python3 start_server.py --tp 2
|
||||
|
||||
# Skip dependency installation (if already installed)
|
||||
python3 start_server.py --skip-deps
|
||||
"""
|
||||
)
|
||||
parser.add_argument(
|
||||
"--model", "-m",
|
||||
default="microsoft/VibeVoice-ASR",
|
||||
help="HuggingFace model ID (default: microsoft/VibeVoice-ASR)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--port", "-p",
|
||||
type=int,
|
||||
default=8000,
|
||||
help="Server port (default: 8000)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--skip-deps",
|
||||
action="store_true",
|
||||
help="Skip installing system dependencies"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--skip-tokenizer",
|
||||
action="store_true",
|
||||
help="Skip generating tokenizer files"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--tp", "--tensor-parallel-size",
|
||||
type=int,
|
||||
default=1,
|
||||
dest="tensor_parallel_size",
|
||||
help="Tensor parallel size: split one model across N GPUs (default: 1)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dp", "--data-parallel-size",
|
||||
type=int,
|
||||
default=1,
|
||||
dest="data_parallel_size",
|
||||
help="Data parallel size: run N independent model replicas for load balancing (default: 1)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max-num-seqs",
|
||||
type=int,
|
||||
default=64,
|
||||
dest="max_num_seqs",
|
||||
help="Maximum number of sequences per batch (default: 64)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max-model-len",
|
||||
type=int,
|
||||
default=65536,
|
||||
dest="max_model_len",
|
||||
help="Maximum model context length (default: 65536)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--gpu-memory-utilization",
|
||||
type=float,
|
||||
default=0.8,
|
||||
dest="gpu_memory_utilization",
|
||||
help="GPU memory utilization fraction (default: 0.8)"
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
print("\n" + "="*60)
|
||||
print(" VibeVoice vLLM ASR Server - One-Click Deployment")
|
||||
print("="*60)
|
||||
|
||||
# Step 1: Install system dependencies
|
||||
if not args.skip_deps:
|
||||
install_system_deps()
|
||||
|
||||
# Step 2: Install VibeVoice
|
||||
install_vibevoice()
|
||||
|
||||
# Step 3: Download model
|
||||
model_path = download_model(args.model)
|
||||
|
||||
# Step 4: Generate tokenizer files
|
||||
if not args.skip_tokenizer:
|
||||
generate_tokenizer(model_path)
|
||||
|
||||
# Step 5: Start server
|
||||
if args.data_parallel_size > 1:
|
||||
start_dp_server(
|
||||
model_path, args.port,
|
||||
data_parallel_size=args.data_parallel_size,
|
||||
tensor_parallel_size=args.tensor_parallel_size,
|
||||
max_num_seqs=args.max_num_seqs,
|
||||
max_model_len=args.max_model_len,
|
||||
gpu_memory_utilization=args.gpu_memory_utilization,
|
||||
)
|
||||
else:
|
||||
start_vllm_server(
|
||||
model_path, args.port,
|
||||
tensor_parallel_size=args.tensor_parallel_size,
|
||||
data_parallel_size=1,
|
||||
max_num_seqs=args.max_num_seqs,
|
||||
max_model_len=args.max_model_len,
|
||||
gpu_memory_utilization=args.gpu_memory_utilization,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,270 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test VibeVoice vLLM API with Streaming and Optional Hotwords Support.
|
||||
|
||||
This script tests ASR transcription via the vLLM OpenAI-compatible API.
|
||||
By default, it runs standard transcription without hotwords.
|
||||
|
||||
Optionally, you can provide hotwords (context_info) to improve recognition
|
||||
of domain-specific content like proper nouns, technical terms, and speaker names.
|
||||
Hotwords are embedded in the prompt as "with extra info: {hotwords}".
|
||||
|
||||
Usage:
|
||||
python test_api_with_hotwords.py [audio_path] [--url URL] [--hotwords "word1,word2"]
|
||||
|
||||
Examples:
|
||||
# Standard transcription (no hotwords)
|
||||
python3 test_api.py audio.wav
|
||||
|
||||
# With hotwords for better recognition of specific terms
|
||||
python3 test_api.py audio.wav --hotwords "Microsoft,Azure,VibeVoice"
|
||||
"""
|
||||
import requests
|
||||
import json
|
||||
import base64
|
||||
import time
|
||||
import sys
|
||||
import os
|
||||
import subprocess
|
||||
import argparse
|
||||
|
||||
|
||||
def _guess_mime_type(path: str) -> str:
|
||||
"""Guess MIME type from file extension."""
|
||||
ext = os.path.splitext(path)[1].lower()
|
||||
mime_map = {
|
||||
".wav": "audio/wav",
|
||||
".mp3": "audio/mpeg",
|
||||
".m4a": "audio/mp4",
|
||||
".mp4": "video/mp4",
|
||||
".flac": "audio/flac",
|
||||
".ogg": "audio/ogg",
|
||||
".opus": "audio/ogg",
|
||||
}
|
||||
return mime_map.get(ext, "application/octet-stream")
|
||||
|
||||
|
||||
def _get_duration_seconds_ffprobe(path: str) -> float:
|
||||
"""Get audio duration using ffprobe."""
|
||||
cmd = [
|
||||
"ffprobe", "-v", "error",
|
||||
"-show_entries", "format=duration",
|
||||
"-of", "default=noprint_wrappers=1:nokey=1",
|
||||
path,
|
||||
]
|
||||
out = subprocess.check_output(cmd, stderr=subprocess.STDOUT).decode("utf-8").strip()
|
||||
return float(out)
|
||||
|
||||
|
||||
def _is_video_file(path: str) -> bool:
|
||||
"""Check if the file is a video file that needs audio extraction."""
|
||||
ext = os.path.splitext(path)[1].lower()
|
||||
return ext in (".mp4", ".m4v", ".mov", ".webm", ".avi", ".mkv")
|
||||
|
||||
|
||||
def _extract_audio_from_video(video_path: str) -> str:
|
||||
"""
|
||||
Extract audio from video file (mp4/mov/webm) to a temporary mp3 file.
|
||||
Returns the path to the extracted audio file.
|
||||
"""
|
||||
import tempfile
|
||||
# Create temp file with .mp3 extension
|
||||
fd, audio_path = tempfile.mkstemp(suffix=".mp3")
|
||||
os.close(fd)
|
||||
|
||||
cmd = [
|
||||
"ffmpeg", "-y", "-i", video_path,
|
||||
"-vn", # No video
|
||||
"-acodec", "libmp3lame",
|
||||
"-q:a", "2", # High quality
|
||||
audio_path
|
||||
]
|
||||
subprocess.run(cmd, check=True, capture_output=True)
|
||||
return audio_path
|
||||
|
||||
|
||||
def test_transcription_with_hotwords(
|
||||
audio_path: str,
|
||||
context_info: str = None,
|
||||
base_url: str = "http://localhost:8000",
|
||||
):
|
||||
"""
|
||||
Test ASR transcription with customized hotwords.
|
||||
|
||||
Hotwords are embedded in the prompt text as "with extra info: {hotwords}".
|
||||
This helps the model recognize domain-specific terms more accurately.
|
||||
|
||||
Args:
|
||||
audio_path: Path to the audio file
|
||||
context_info: Hotwords string (e.g., "Microsoft,Azure,VibeVoice")
|
||||
base_url: vLLM server URL
|
||||
"""
|
||||
|
||||
print(f"=" * 70)
|
||||
print(f"Testing Customized Hotwords Support")
|
||||
print(f"=" * 70)
|
||||
print(f"Input file: {audio_path}")
|
||||
print(f"Hotwords: {context_info or '(none)'}")
|
||||
print()
|
||||
|
||||
# Handle video files: extract audio first
|
||||
temp_audio_path = None
|
||||
actual_audio_path = audio_path
|
||||
if _is_video_file(audio_path):
|
||||
print(f"🎬 Detected video file, extracting audio...")
|
||||
temp_audio_path = _extract_audio_from_video(audio_path)
|
||||
actual_audio_path = temp_audio_path
|
||||
print(f"✅ Audio extracted to: {temp_audio_path}")
|
||||
|
||||
# Load audio
|
||||
try:
|
||||
duration = _get_duration_seconds_ffprobe(actual_audio_path)
|
||||
print(f"Audio duration: {duration:.2f} seconds")
|
||||
|
||||
with open(actual_audio_path, "rb") as f:
|
||||
audio_bytes = f.read()
|
||||
|
||||
audio_b64 = base64.b64encode(audio_bytes).decode("utf-8")
|
||||
print(f"Audio size: {len(audio_bytes)} bytes")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error preparing audio: {e}")
|
||||
# Cleanup temp file if created
|
||||
if temp_audio_path and os.path.exists(temp_audio_path):
|
||||
os.remove(temp_audio_path)
|
||||
return
|
||||
|
||||
# Build the request
|
||||
url = f"{base_url}/v1/chat/completions"
|
||||
|
||||
show_keys = ["Start time", "End time", "Speaker ID", "Content"]
|
||||
|
||||
# Build prompt with optional hotwords
|
||||
# Hotwords are embedded as "with extra info: {hotwords}" in the prompt
|
||||
if context_info and context_info.strip():
|
||||
prompt_text = (
|
||||
f"This is a {duration:.2f} seconds audio, with extra info: {context_info.strip()}\n\n"
|
||||
f"Please transcribe it with these keys: " + ", ".join(show_keys)
|
||||
)
|
||||
print(f"\n📝 Hotwords embedded in prompt: '{context_info}'")
|
||||
else:
|
||||
prompt_text = (
|
||||
f"This is a {duration:.2f} seconds audio, please transcribe it with these keys: "
|
||||
+ ", ".join(show_keys)
|
||||
)
|
||||
print(f"\n📝 No hotwords provided")
|
||||
|
||||
mime = _guess_mime_type(actual_audio_path)
|
||||
data_url = f"data:{mime};base64,{audio_b64}"
|
||||
|
||||
payload = {
|
||||
"model": "vibevoice",
|
||||
"messages": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful assistant that transcribes audio input into text output in JSON format."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "audio_url", "audio_url": {"url": data_url}},
|
||||
{"type": "text", "text": prompt_text}
|
||||
]
|
||||
}
|
||||
],
|
||||
"max_tokens": 32768,
|
||||
"temperature": 0.0,
|
||||
"stream": True,
|
||||
"top_p": 1.0,
|
||||
}
|
||||
|
||||
print(f"\n{'=' * 70}")
|
||||
print(f"Sending request to {url}")
|
||||
print(f"{'=' * 70}")
|
||||
|
||||
t0 = time.time()
|
||||
try:
|
||||
response = requests.post(url, json=payload, stream=True, timeout=12000)
|
||||
|
||||
if response.status_code == 200:
|
||||
print("\n✅ Response received. Streaming content:\n")
|
||||
print("-" * 50)
|
||||
|
||||
printed = ""
|
||||
for line in response.iter_lines():
|
||||
if line:
|
||||
decoded_line = line.decode('utf-8')
|
||||
|
||||
if decoded_line.startswith("data: "):
|
||||
json_str = decoded_line[6:]
|
||||
if json_str.strip() == "[DONE]":
|
||||
print("\n" + "-" * 50)
|
||||
print("✅ [Finished]")
|
||||
break
|
||||
try:
|
||||
data = json.loads(json_str)
|
||||
delta = data['choices'][0]['delta']
|
||||
content = delta.get('content', '')
|
||||
if content:
|
||||
if content.startswith(printed):
|
||||
to_print = content[len(printed):]
|
||||
else:
|
||||
to_print = content
|
||||
if to_print:
|
||||
print(to_print, end='', flush=True)
|
||||
printed += to_print
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
else:
|
||||
print(f"❌ Error: {response.status_code}")
|
||||
print(response.text)
|
||||
|
||||
except requests.exceptions.Timeout:
|
||||
print("❌ Request timed out!")
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
|
||||
elapsed = time.time() - t0
|
||||
print(f"\n{'=' * 70}")
|
||||
print(f"⏱️ Total time elapsed: {elapsed:.2f}s")
|
||||
print(f"📊 RTF (Real-Time Factor): {elapsed / duration:.2f}x")
|
||||
print(f"{'=' * 70}")
|
||||
|
||||
# Cleanup temp audio file if created
|
||||
if temp_audio_path and os.path.exists(temp_audio_path):
|
||||
os.remove(temp_audio_path)
|
||||
print(f"🗑️ Cleaned up temp file: {temp_audio_path}")
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Test VibeVoice vLLM API with Customized Hotwords"
|
||||
)
|
||||
parser.add_argument(
|
||||
"audio_path",
|
||||
help="Path to audio file (wav, mp3, flac, etc.) or video file"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--url",
|
||||
default="http://localhost:8000",
|
||||
help="vLLM server URL (default: http://localhost:8000)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--hotwords",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Hotwords to improve recognition (e.g., 'Microsoft,Azure,VibeVoice')"
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Run test
|
||||
test_transcription_with_hotwords(
|
||||
audio_path=args.audio_path,
|
||||
context_info=args.hotwords,
|
||||
base_url=args.url,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user