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Speech-to-Text Service

Overview

Speech-to-Text service using gRPC and the Whisper model from Hugging Face's Transformers library. The service allows users to send audio data and receive transcribed text in response.

Features

  • gRPC-based communication for efficient data transfer.
  • Utilizes the Whisper model for high-quality speech recognition.
  • Includes a client for sending audio data and receiving transcriptions.
  • Unit tests to ensure the functionality of the service.

Requirements

  • Python 3.10 or higher

Setup Instructions

  1. Clone the Repository

    git clone https://github.com/your-username/speech-to-text-service.git
cd speech-to-text-service

  2. Install Dependencies Make sure to install the required packages as mentioned above:

    pip install -r requirements.txt
pip install -r requirements-dev.txt

  3. Install Pre-commit To install pre-commit, run:

    pip install pre-commit

  4. Environment Variables Create a .env file in the root directory and set the following variables for local environment:

    ENVIRONMENT=local
PORT=50051

    For non-local environments (development or production), set the following variables:

    ENVIRONMENT=development  # or production
SSL_PRIVATE_KEY_PATH=<path-to-private-key>
SSL_CERTIFICATE_CHAIN_PATH=<path-to-certificate-chain>

  5. Run the gRPC Server Start the server by running:

    python main.py

  6. Run the Client In a separate terminal, run the client to send audio data:

    python src/client/client.py

  7. Run Pre-commit Hooks To ensure code quality, install and run pre-commit hooks:

    pre-commit run --all-files

  8. Code Formatting and Linting Use ruff for linting and black for code formatting:

    ruff check .
black .

Testing

To run the unit tests, execute the following command:

python -m unittest discover -s src/tests

Audio Data

Place your test audio files in the data directory. The client will look for test_audio.wav by default.

Contributing

Feel free to submit issues or pull requests. Contributions are welcome!

License

This project is licensed under the MIT License.