Whisper Transcription API

This project implements a high-performance audio transcription API using Rust, Python, and the Whisper AI model. It combines the efficiency of Rust for the web server with the power of Python's machine learning ecosystem.

Components

1. Rust Web Server (src/main.rs)
2. Python Whisper Integration (python/whisper_ffi.py)
3. Benchmark Script (benchmark_transcription.py)

Prerequisites

• Rust (latest stable version)
• Python 3.8+
• uv (Python package manager)

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/whisper-transcription-api.git
   cd whisper-transcription-api
   

2. Install Rust dependencies:

   cargo build
   

3. Set up the Python environment:

   uv venv
   source .venv/bin/activate  # On Windows use: .venv\Scripts\activate
   uv pip install -r requirements.txt
   

Running the Server

1. Start the Rust server:

   cargo run
   

The server will start on http://localhost:3000.

API Usage

Send a POST request to http://localhost:3000/transcribe with the following JSON body:

{
  "audio_data": [<raw audio bytes as a list of integers>]
}

The API will respond with:

{
  "transcription": "Transcribed text",
  "language": "Detected language",
  "confidence": 0.98
}

Benchmarking

To benchmark the API performance:

1. Update the audio_file_path in benchmark_transcription.py to point to your test audio file.
2. Run the benchmark script:

   python benchmark_transcription.py
   

This will output performance statistics for both sequential and concurrent requests.

Project Structure

whisper-transcription-api/
├── src/
│   └── main.rs
├── python/
│   └── whisper_ffi.py
├── Cargo.toml
├── pyproject.toml
├── requirements.txt
├── benchmark_transcription.py
└── README.md

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.