Ara is a script / api to transcribe ✍️ and diarize 📓 audio. The typical use case for this is transcribing audio from interviews, podcasts and anything where multiple people are speaking. The output is 'easy' to read (if you like .txt files), formatted so that speakers are clear for each segment.
It uses Whisper to transcribe the audio into text. It then uses Pyannote to diarize different speakers. Finally, it matches the segments from the two models and writes the output to file or returns it through the api.
call the script like so:
python script.py -i input.wav -o output.txt -l English main.py defines a basic FastAPI with an endpoint for transcription
Start the server
uvicorn main:app --reload query
curl 127.0.0.1:8000/transcribe/sample_data.interview.wavThis can be useful for interacting with it through Docker, or deploying the code.
The repo comes with a Dockerfile, which makes it easier to deploy in a containerised way. build the docker, then run like so
sudo docker run -p 80:80 --gpus all <CONTAINER NAME>