wehub-resource-sync
87 lines
3.5 KiB
Python
87 lines
3.5 KiB
Python
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""" This example shows how to get started using WhisperCPP as a fast, local voice-to-text processing engine.
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Whisper is a leading open voice voice-to-text model from OpenAI - https://github.com/openai/whisper
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WhisperCPP is the implementation of Whisper packaged as a GGML deliverable - https://github.com/ggerganov/whisper.cpp
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Starting with llmware 0.2.11, we have integrated WhisperCPPModel as a new model class,
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providing options for direct inference, and coming soon, integration into the Parser for easy text chunking and
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parsing into a Library with other document types.
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llmware provides prebuilt shared libraries for WhisperCPP on the following platforms:
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--Mac M series
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--Linux x86 (no CUDA)
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--Linux x86 (with CUDA) - really fast
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--Windows x86 (only on CPU) currently.
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We have added three Whisper models to the default model catalog:
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1. ggml-base.en.bin - english-only base model
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2. ggml-base.bin - multi-lingual base model
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2. ggml-small.en-tdrz.bin - this is a 'tiny-diarize' implementation that has been finetuned to identify the
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speakers and inserts special [_SOLM_] tags to indicate a conversation turn / change of speaker.
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Main repo: https://github.com/akashmjn/tinydiarize/
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Citation: @software{mahajan2023tinydiarize,
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author = {Mahajan, Akash}, month = {08},
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title = {tinydiarize: Minimal extension of Whisper for speaker segmentation with special tokens},
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url = {https://github.com/akashmjn/tinydiarize},
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year = {2023}
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To use WAV files, there is one additional Python dependency required:
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--pip install librosa
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--Note: this has been added to the default requirements.txt and pypy build starting with 0.2.11
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To use other popular audio/video file formats, such as MP3, MP4, M4A, etc., then the following dependencies are
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required:
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--pip install pydub
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--ffmpeg library - which can be installed as follows:
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-- Linux: `sudo apt install ffmpeg'
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-- Mac: `brew install ffmpeg`
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-- Windows: direct download and install from ffmpeg
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"""
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import os
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from llmware.models import ModelCatalog
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from llmware.gguf_configs import GGUFConfigs
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# optional / to adjust various log/display parameters of the model
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GGUFConfigs().set_config("whisper_cpp_verbose", "OFF")
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GGUFConfigs().set_config("whisper_cpp_realtime_display", True)
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# note: english is default output - change to 'es' | 'fr' | 'de' | 'it' ...
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GGUFConfigs().set_config("whisper_language", "en")
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# whether to add or remove segment markers in llm response output
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GGUFConfigs().set_config("whisper_remove_segment_markers", True)
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def basic_whisper_cpp_use_example():
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""" Hello world example to get started using WhisperCPP in LLMWare. """
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fp = "/local/path/to/.wav"
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fn = "my_wav.wav"
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# prompt = string representing the path to a .wav file
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prompt = os.path.join(fp,fn)
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# choose between english-only and multilingual
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whisper_base_english = "whisper-cpp-base-english"
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whisper_base_multi = "whisper-cpp-base"
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# load and run inference like any other model in llmware
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model = ModelCatalog().load_model(whisper_base_english)
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response = model.inference(prompt)
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print("\nllm response: ", response["llm_response"])
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print("usage: ", response["usage"])
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return response
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if __name__ == "__main__":
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response = basic_whisper_cpp_use_example()
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