Get Started#

This page is the entry point for running the Audio Analyzer microservice. Pick one of the two deployment paths and follow the linked guide.

Application Overview#

The Audio Analyzer microservice provides automatic speech recognition (ASR) and optional speaker diarization. It captures and transcribes audio, optionally identifying different speakers in the same audio chunk. Optional sentiment analysis per chunk complements the transcription output.

If you enable speaker diarization in config.yaml (models.asr.diarization: true), you must provide a Hugging Face access token (HF_TOKEN) and accept the Pyannote speaker-diarization model license on Hugging Face if you want diarization to initialize successfully. If diarization setup is incomplete, the service continues running and logs a warning while disabling diarization for that session.

Before You Begin#

Choose Deployment Path#

The container image exposes the API on host port 8010 and mounts shared folders for models, chunks, storage, and the Hugging Face cache. Fresh clones include placeholder directories for these mount roots. If you delete them and then start Compose, Docker may recreate the missing host paths as root before the container starts.

See Run with Docker Compose for the full step-by-step guide.

Quick start:

docker compose up -d --build
curl --noproxy '*' http://127.0.0.1:8010/health

If you hit permission errors on models/, chunks/, storage/, or .cache/huggingface/, see Troubleshooting.

Run the service directly with Python. This path is useful for development or when you do not want to use Docker.

See Run on the Host for the full step-by-step guide.

Quick start:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python main.py

Verify#

Once the service is running:

curl --noproxy '*' http://127.0.0.1:8010/health

Expected response:

{"status": "ok"}

Next Steps#