ortizcruz
134 lines
5.2 KiB
Markdown
134 lines
5.2 KiB
Markdown
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# `ScrAIbe: Streamlined Conversation Recording with Automated Intelligence Based Environment`
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`ScrAIbe` is a state-of-the-art, [PyTorch](https://pytorch.org/) based multilingual speech-to-text framework to generate fully automated transcriptions.
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Beyond transcription, ScrAIbe supports advanced functions, such as speaker diarization and speaker recognition.
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Designed as a comprehensive AI toolkit, it uses multiple AI models:
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- [whisper](https://github.com/openai/whisper): A general-purpose speech recognition model.
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- [payannote-audio](https://github.com/pyannote/pyannote-audio): An open-source toolkit for speaker diarization.
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The framework utilizes a PyanNet-inspired pipeline with the `Pyannote` library for speaker diarization and `VoxCeleb` for speaker embedding.
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During post-diarization, each audio segment is processed by the OpenAI `Whisper` model, in a transformer encoder-decoder structure. Initially, a CNN mitigates noise and enhances speech. Before transcription, `VoxLingua` dentifies the language segment, facilitating Whisper's role in both transcription and text translation.
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The following graphic illustates the whole pipeline:
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## Install `ScrAIbe` :
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The following command will pull and install the latest commit from this repository, along with its Python dependencies.
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pip install git+https://github.com/JSchmie/autotranscript.git
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- **Python version**: Python 3.9
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- **PyTorch version**: Python 1.11.0
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Important: For the `Pyannote` model you need to be granted access in Hugging Face.
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Check the [Pyannote model page](https://huggingface.co/pyannote/speaker-diarization) to get access to the model.
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Additionally, you need to generate a [Hugging Face token](https://huggingface.co/docs/hub/security-tokens).
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## Usage
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We've developed ScrAIbe with several access points to cater to diverse user needs.
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### Python usage
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It enables full control over the functionalities as well as process customization.
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```python
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from autotranscript import AutoTranscribe
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model = AutoTranscribe()
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text = model.transcribe("audio.wav")
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print(f"Transcription: \n{text}")
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```
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Refer to [whisper](https://github.com/openai/whisper) and [payannote-audio](https://github.com/pyannote/pyannote-audio) for further options.
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### Command-line usage
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You can also run ScrAIbe in a [Gradio App](https://github.com/gradio-app/gradio) interface using the following command-line:
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autotranscript audio.wav
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Some example of important functionalities are:
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- `--task`: Task to be performed, either transcription, diarization or translation into English. Default is transcription.
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- `--hf-token`: Personal `Hugging Face` token.
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- `--server-name`: Name of the Web Server. If empty 127.0.0.1 or 0.0.0.0 will be used.
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- `--port`: To run the Gradio app. The default is 7860.
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- `--whisper-model-name`: Name of the [whisper](https://github.com/openai/whisper) model to be used. Default is `medium`.
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Run the following to view all available options:
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autotranscript -h
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### Running a Docker container
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After you have installed Docker, you can execute the following commands in the terminal.
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```
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sudo docker build . --build-arg="hf_token=[enter your HuggingFace token] " --no-cache -t [image name]
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sudo docker run --rm -it -p 7860:7860 --name [container name][image name] --hf_token [enter your HuggingFace token] --start_server
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```
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Then click the following link to run the app:
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http://0.0.0.0:7860
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## Documentation
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For further insights check the [documentation page](https://cristinaortizcruz.github.io/Test/).
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## Contributions
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We are happy for any interest in contributing: In order to do that, fork the repo and use merge requests to incorporate your contribution.
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## Roadmap
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The following milestones are planned for further releases of ScrAIbe:
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- Model quantization
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Quantization to empower memory and computational efficiency.
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- Model fine-tuning
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In order to be able to cover a variety of linguistic phenomena.
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For example, currently ScrAIbe is able to transcribe word by word, but ignores filler words or speech pauses.
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These phenomena can be addressed by fine-tuning with the corresponding data.
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- Implementation of LLMs
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One example is the implementation of a summarization or extraction model, which enables ScrAIbe to automatically summarize or retrieve the key information out of a generated transcription, which could be the minutes of a meeting.
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- Executable for Windows
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## Contact
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For queries contact [Jacob Schmieder](Jacob.Schmieder@dbfz.de)
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## License
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<!-- licensing missing? Apache 2.0 -->
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ScrAIbe is licensed under (tbd).
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## Acknowledgments
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Special thanks go to the KIDA project and the BMEL (Bundesministerium für Ernährung und Landwirtschaft), especially to the AI Consultancy Team and the Infrastructure Team.
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