scribe/README.md

ScrAIbe: Streamlined Conversation Recording with Automated Intelligence Based Environment

ScrAIbe is a state-of-the-art, PyTorch based multilingual speech-to-text framework to generate fully automated transcriptions.

Beyond transcription, ScrAIbe supports advanced functions, such as speaker diarization and speaker recognition.

Designed as a comprehensive AI toolkit, it uses multiple AI models:

• whisper: A general-purpose speech recognition model.
• payannote-audio: An open-source toolkit for speaker diarization.

The framework utilizes a PyanNet-inspired pipeline with the Pyannote library for speaker diarization and VoxCeleb for speaker embedding.

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.

The following graphic illustates the whole pipeline:

Install ScrAIbe :

The following command will pull and install the latest commit from this repository, along with its Python dependencies.

pip install git+https://github.com/JSchmie/autotranscript.git

• Python version: Python 3.8
• PyTorch version: Python 1.11.0
• CUDA version: Cuda-toolkit 11.3.1

Important: For the Pyannote model you need to be granted access in Hugging Face. Check the Pyannote model page to get access to the model.

Additionally, you need to generate a Hugging Face token.

Usage

We've developed ScrAIbe with several access points to cater to diverse user needs.

Python usage

It enables full control over the functionalities as well as process customization.

Some usage examples:

• Usage of AutoTranscribe, core of the transcription system, for performing trancription and diarization of audio files.

from scraibe import AutoTranscribe

model = AutoTranscribe()

text = model.transcribe("audio.wav")

print(f"Transcription: \n{text}")

• Usage of Diariser, responsible for identifying and segmenting individual speakers from a given audio file.

 from scraibe import Diariser

model = Diariser.load_model()

diarisation_output  = model.diarization("audio.wav")

• Usage of Transcriber, for transcribing audio files and saving the transcription afterwards.

 from scraibe import Transcriber

transcriber = Transcriber.load_model()

transcript  = transcriber.transcribe("audio.wav")

transcriber.save_transcript(transcript, "path/to/save.txt")

Refer to whisper and payannote-audio for further options.

Command-line usage

You can also run ScrAIbe in a Gradio App interface using the following command-line:

scraibe 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: Personal Hugging Face token.

• --server-name: Name of the Web Server. If empty 127.0.0.1 or 0.0.0.0 will be used.

• --port: To run the Gradio app. The default is 7860.

• --whisper-model-name: Name of the whisper model to be used. Default is medium.

Run the following to view all available options:

scraibe -h

Running a Docker container

After you have installed Docker, you can execute the following commands in the terminal.

sudo docker build . --build-arg="hf_token=[enter your HuggingFace token] " -t [image name] 

sudo docker run -it  -p 7860:7860  --name [container name][image name]  --hf_token [enter your HuggingFace token] --start_server

• -p: Flag for connecting the container interal port to the port on your local machine.
• --hf_token: Flag for entering your personal HuggingFace token in the container.
• --start_server: Command to start the Gradio App.

Then click the following link to run the app:

http://0.0.0.0:7860

• Enabling GPU usage

sudo docker run -it  -p 7860:7860 --gpus all  --name [container name][image name]  --hf_token [enter your HuggingFace token] --start_server

For further guidance check: https://blog.roboflow.com/use-the-gpu-in-docker/

Documentation

For further insights check the documentation page.

Contributions

We are happy for any interest in contributing and about feedback: In order to do that, create an issue with your feedback or feel free to contact us.

Roadmap

The following milestones are planned for further releases 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

License

ScrAIbe is licensed under GNU General Public License.

Acknowledgments

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.