This project sets up a robust web application using FastAPI. It integrates the open-source Whisper AI model for Speech-to-Text (STT) transcription, enabling accurate and efficient audio-to-text conversion. The application is containerized using Docker, ensuring consistent and efficient runtime in any environment.
- FastAPI Backend: A high-performance, easy-to-learn backend.
- Whisper AI Integration: Cutting-edge STT transcription.
- Dockerized: Simplified deployment, scalability, and management.
Whether you want to automate your transcription tasks, enhance accessibility for your content, or explore the world of AI-powered applications, this project has you covered.
- Clone the repository
git clone https://github.com/buildberg/containerized-transcription-api
- Navigate into the project folder
cd containerized-transcription-api
- Build and run Docker container
make docker
Convert Audio to Text
POST /whispergraph LR
setup[make setup]
setup -->|1. Make script executable| setup.sh[setup.sh]
setup -->|2. Run script| setup.sh
install[make install]
install -->|1. Upgrade pip| pip[pip]
install -->|2. Install requirements| requirements[requirements.txt]
format[make format]
format -->|Format all code| black[Black]
lint[make lint]
lint -->|Find and lint .py files| pylint[Pylint]
precommit[make precommit]
precommit -->|1. Install pre-commit hooks| pre-commit-install[pre-commit install]
precommit -->|2. Run hooks on all files| pre-commit-run[pre-commit run]
refactor[make refactor]
refactor --> format
refactor --> precommit
refactor --> lint
push[make push]
push -->|1. Add changes| git-add[git add]
push -->|2. Commit changes| git-commit[git commit]
push -->|3. Push to repository| git-push[git push]
docker[make docker]
docker -->|1. Docker-compose build and up| docker-compose[docker-compose up -d --build]
run[make run]
run -->|1. Execute main script| main-script[python src/main.py]