yuyunfrancis/kinyarwanda-english-autocaptioning

# Kinyarwanda to English Translation Pipeline This project provides a pipeline for transcribing, translating, and captioning Kinyarwanda audio files into English. The pipeline can process single audio files or directories containing multiple audio files. ## Prerequisites - Python 3.8 or higher - FFmpeg - Virtual environment (optional but recommended) ## Setup 1. **Clone the repository**: ```bash git clone github.com cd kinyarwanda-english-autocaptioning ``` 2. **Create and activate a virtual environment** (optional but recommended): ```bash python3 -m venv venv source venv/bin/activate ``` 3. **Install the required dependencies**: ```bash pip install -r requirements.txt ``` 4. **Install FFmpeg**: ```bash sudo apt update sudo apt install ffmpeg ``` 5. **Create a .env file** in the root directory and add your Hugging Face token: ``` HUGGING_FACE_TOKEN=your_hugging_face_token_here ``` ## Usage To run the pipeline, use the following command: ```bash python main.py --audio [AUDIO_PATH] --output [OUTPUT_DIR] --config [CONFIG_PATH] --mode [MODE] ``` ### Arguments - `--audio`: Path to the audio file or directory containing audio files. - `--output`: Output directory for results (default: output). - `--config`: Path to the configuration file (default: config.yaml). - `--video`: Path to the video file if captioning a video (optional). - `--mode`: Mode of operation (transcribe, translate, caption, full). ### Example ```bash python main.py --audio ./data/kinyarwanda_1.mp3 --output ./output --config ./config.yaml --mode full ``` ## Configuration The configuration file (`config.yaml`) should contain the necessary settings for the transcription, translation, and captioning processes. Here is an example configuration: ```yaml transcription: model_name: "mbazaNLP/Whisper-Small-Kinyarwanda" chunk_size: 30 overlap: 5 language: "sw" task: "transcribe" translation: model_name: "Helsinki-NLP/opus-mt-sw-en …