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# `AutoTranscript`: Fully Automated Transcription using AI
# `ScrAIbe: Streamlined Conversation Recording with Automated Intelligence Based Environment`
`AutoTranscript` is a [PyTorch](https://pytorch.org/) based interface speech-to-text tool to generate fully automated transcriptions. AutoTranscript uses AI models containing speaker diarization models:
`ScrAIbe` is a [PyTorch](https://pytorch.org/) based interface speech-to-text tool to generate fully automated transcriptions. AutoTranscript uses AI models containing speaker diarization models:
- [whisper](https://github.com/openai/whisper): A general-purpose speech recognition model.
- [payannote-audio](https://github.com/pyannote/pyannote-audio): An open-source toolkit for speaker diarization-.
`AutoTranscript` can be used as a command-line interface, a webserver, or as a Python API.
## Install `AutoTranscript` :
## Install `ScrAIbe` :
The following command will pull and install the latest commit from this repository, along with its Python dependencies.
pip install https://github.com/JSchmie/autotranscript.git
pip install git+https://github.com/JSchmie/autotranscript.git
- **Python version**: Python 3.9
- **PyTorch version**: Python 1.11.0
## Usage examples
## Usage
`AutoTranscript` can be used as a command-line interface, a webserver, or as a Python API.
### Python usage
@@ -32,37 +33,67 @@ print(f"Transcription: \n{text}")
```
Refer to [whisper](https://github.com/openai/whisper) and [payannote-audio](https://github.com/pyannote/pyannote-audio) for further options.
### Command-line usage
If you do not want to control the optimization using Python, you also can use the command-line:
You can also run ScrAIbe in a [Gradio App](https://github.com/gradio-app/gradio) interface using the following command-line:
autotranscript audio.wav
Some example of important functionalities are:
- `--task`: Task to be performed, either transcription, diarization or translation into English. Default is transcription.
- `--hf-token`: To download the models, a Hugging Face token must be generated. Check [Hugging Face](https://huggingface.co/docs/hub/security-tokens) for further information on how to do that.
- `--server-name`: Name of the Web Server. If empty 127.0.0.1 or 0.0.0.0 will be used
- `--whisper-model-name`: Name of the [whisper](https://github.com/openai/whisper) model to be used. Default is `medium`.
Run the following to view all available options:
autotranscript -h
### Documentation usage
## Documentation
To access the documentation run the following command from the docs/_build/html directory:
For further insights check the [documentation page](https://cristinaortizcruz.github.io/Test/).
python -m http.server
## Contributions
We are happy for any interest in contributing: In order to do that, fork the repo and use merge requests to incorporate your contribution.
## Roadmap
- Model quantization
- Model fine-tuning
- Implementation of LLMs
The following milestones are planned for the further development of ScrAIbe:
- Model quantization
Quantization to empower memory and computational efficiency.
- Model fine-tuning
In order to be able to cover a variety of linguistic phenomena.
For example, currently ScrAIbe is able to transcribe word by word, but ignores filler words or speech pauses.
These phenomena can be addressed by fine-tuning with the corresponding data.
- Implementation of LLMs
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.
- Executable for Windows
## Contact
For queries contact Jacob Schmieder at Jacob.Schmieder@dbfz.de
For queries contact [Jacob Schmieder](Jacob.Schmieder@dbfz.de)
## License
<!-- licensing missing? Apache 2.0 -->
ScrAIbe is licensed under (tbd).
## Acknowledgments
Special thanks go to the colleagues of the KIDA project - especially the teams in I5 and I2 - and the BMEL (Bundesministerium für Ernährung und Landwirtschaft).
<!--add KIDA, MRI, DBFZ, BMEL logos-->
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.