benny-png/SWAHILI-TTS

# Swahili Text-to-Speech API A FastAPI-based REST API that provides text-to-speech (TTS) capabilities for Swahili text using fine-tuned Hugging Face models. ## Features - Converts Swahili text to speech using two different models: - Benny model: `Benjamin-png/swahili-mms-tts-finetuned` - Briget model: `Benjamin-png/swahili-mms-tts-Briget_580_clips-finetuned` - Automatic Swahili language detection - CORS support for cross-origin requests - Efficient model caching - Returns audio in WAV format ## Prerequisites ```bash pip install -r requirements.txt ``` ## Running the Server To start the server: ```bash python tts_linkedin.py ``` The server will run on `0.0.0.0` ## API Endpoints ### 1. Generate Speech using Benny Model **Endpoint:** `POST /tts/benny` **Request Body:** ```json { "text": "Your Swahili text here" } ``` ### 2. Generate Speech using Briget Model **Endpoint:** `POST /tts/briget` **Request Body:** ```json { "text": "Your Swahili text here" } ``` ### Response Both endpoints return: - Content-Type: `audio/wav` - Binary audio data in WAV format ### Error Responses - `400 Bad Request`: If the provided text is not in Swahili - Standard HTTP error codes for other failure cases ## Example Usage Using curl: ```bash curl -X POST "localhost" \ -H "Content-Type: application/json" \ -d '{"text":"Kijana huyu ni msataarabu sana sana"}' \ --output output.wav ``` Using Python requests: ```python import requests response = requests.post( "localhost", json={"text": "Kijana huyu ni msataarabu sana sana"} ) with open("output.wav", "wb") as f: f.write(response.content) ``` ## Technical Details - Uses PyTorch for model inference - Models are automatically loaded on first use and cached - Runs on GPU if available, falls back to CPU - Audio is generated at the model's native sampling rate - Output is converted to 16-bit PCM WAV format ## Implementation Notes - The API uses `lru_cache` for efficient model loading …