wehub-resource-sync
95 lines
6.3 KiB
Markdown
95 lines
6.3 KiB
Markdown
# FunASR Use-case Showcase
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FunASR is useful far beyond a single offline transcription command. This page collects the fastest paths for developers who want to evaluate, deploy, or integrate speech understanding in real products.
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## Choose the right path
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| Goal | Start here | Why it matters |
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| Try FunASR in a browser | [Colab quickstart](../examples/colab/) | Run a public sample and upload your own audio before setting up a local environment. |
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| Transcribe one file locally | [README quick start](../README.md#quick-start) and [model selection guide](./model_selection.md) | Verify install, model choice, and model download in minutes. |
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| Compare accuracy and speed | [Benchmark report](https://modelscope.github.io/FunASR/benchmark.html) | Reproduce the 184-file long-audio benchmark before choosing a model. |
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| Migrate from Whisper/cloud ASR | [Migration guide](./migration_from_whisper.md) | Map existing pipelines to FunASR, benchmark representative audio, and plan a safe rollout. |
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| Build a private speech API | [OpenAI-compatible API example](../examples/openai_api/), [Gradio browser demo](../examples/openai_api/GRADIO.md), [client recipes](../examples/openai_api/CLIENTS.md), [JavaScript/TypeScript recipes](../examples/openai_api/JAVASCRIPT.md), and [workflow recipes](../examples/openai_api/WORKFLOWS.md) | Reuse LangChain, Dify, n8n, AutoGen, and other OpenAI-style clients without sending audio to a cloud ASR provider. |
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| Add speech input to agents | [MCP server](../examples/mcp_server/) and [voice input](../examples/voice_input/) | Connect local ASR to Claude, Cursor, and desktop agent workflows. |
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| Choose a deployment path | [Deployment matrix](./deployment_matrix.md) | Compare Python API, OpenAI API, Docker Compose, Kubernetes, WebSocket, vLLM, MCP, batch, subtitles, and Triton. |
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| Serve streaming ASR | [Runtime service docs](../runtime/readme.md) | Run WebSocket or service-mode ASR for live captioning and call-center style workloads. |
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| Accelerate LLM-based ASR | [vLLM guide](./vllm_guide.md) | Use tensor parallel decoding and streaming service support for Fun-ASR-Nano. |
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| Generate subtitles | [Subtitle example](../examples/subtitle/) | Turn long audio or video into subtitle files for media workflows. |
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| Process many recordings | [Batch ASR example](../examples/batch_asr_improved.py) | Build repeatable offline jobs for archives, meetings, and datasets. |
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## Production-oriented recipes
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### Private transcription API
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Use this path when an application already speaks OpenAI-style APIs or when audio cannot leave your environment.
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```bash
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pip install funasr fastapi uvicorn python-multipart
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funasr-server --model sensevoice --device cuda
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```
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```bash
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curl http://localhost:8000/v1/audio/transcriptions \
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-F file=@sample.wav \
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-F model=sensevoice \
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-F response_format=verbose_json
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```
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Recommended next steps:
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- Run the [OpenAI-compatible API smoke test](../examples/openai_api/smoke_test.sh) or the cross-platform [Python smoke test](../examples/openai_api/smoke_test.py).
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- For browser upload or microphone demos, start from the [Gradio browser demo](../examples/openai_api/GRADIO.md).
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- For Node.js or Next.js services, start from the [JavaScript/TypeScript recipes](../examples/openai_api/JAVASCRIPT.md).
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- For cluster services, start from the [Kubernetes deployment template](../examples/openai_api/kubernetes/).
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- Add authentication and network controls at your service boundary; start from the [security and gateway guide](../examples/openai_api/SECURITY.md).
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- Record model name, device, driver, and audio duration in bug reports and benchmarks.
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### Agent speech input
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Use this path when you want to talk to coding agents, internal assistants, or workflow tools.
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- Start with the [MCP server example](../examples/mcp_server/) for Claude/Cursor-style tools.
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- Use the [voice input example](../examples/voice_input/) for desktop speech input experiments.
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- Keep latency visible: log audio duration, processing time, and selected model for each request.
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### Streaming and call-center workloads
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Use this path when partial results and low perceived latency matter more than a single final transcript.
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- Start from the [runtime service docs](../runtime/readme.md).
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- Pair ASR with VAD, punctuation, and speaker diarization when the transcript needs to be readable by humans.
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- Validate with realistic audio: background noise, long silence, overlapping speakers, and different microphone quality.
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### Benchmark before migrating from Whisper
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Use this path when deciding whether FunASR is a good replacement for Whisper or a cloud ASR provider.
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- Follow the [migration guide](./migration_from_whisper.md) to map features and benchmark representative audio.
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- Read the [public benchmark report](https://modelscope.github.io/FunASR/benchmark.html).
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- Benchmark your own sample set before migration; include both short clips and long-form recordings.
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- Track cost and throughput together: GPU speed, CPU viability, model download size, and deployment complexity.
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## Model selection hints
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For a deeper comparison of SenseVoice, Paraformer, Fun-ASR-Nano, streaming runtime, and OpenAI API aliases, use the [model selection guide](./model_selection.md).
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| Need | Good first choice | Notes |
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| Fast multilingual transcription | SenseVoice-Small | Strong default for local demos and private APIs. |
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| Mandarin production ASR | Paraformer-Large | Mature choice for Chinese speech recognition. |
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| LLM-based ASR experiments | Fun-ASR-Nano | Pair with the [vLLM guide](./vllm_guide.md) when throughput matters. |
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| Speaker-aware transcripts | SenseVoice or Paraformer with `spk_model="cam++"` | Useful for meetings, interviews, and customer calls. |
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| Live audio | Runtime WebSocket service | Validate chunking, VAD, and endpointing with real traffic. |
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## Share your result
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If FunASR works well in your project, consider opening a [showcase issue](https://github.com/modelscope/FunASR/issues/new?template=showcase.md), [Migration Benchmark Report](https://github.com/modelscope/FunASR/issues/new?template=migration_benchmark.md), or GitHub Discussion with:
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- Use case and deployment mode.
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- Model, device, and processing speed.
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- Audio domain, language, and rough duration.
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- A public demo, screenshot, benchmark summary, or integration link when available.
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Concrete usage reports help new users choose the right path and help maintainers prioritize the next round of docs and examples.
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