mirror of
https://github.com/tcsenpai/youlama.git
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144 lines
3.4 KiB
Markdown
144 lines
3.4 KiB
Markdown
# Audio/Video Transcription Web App
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A web application for transcribing audio and video files using WhisperX, with support for YouTube videos and optional summarization using Ollama.
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## Features
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- Transcribe local audio/video files
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- Process YouTube videos (with subtitle extraction when available)
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- Automatic language detection
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- Multiple WhisperX model options
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- Optional text summarization using Ollama
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- Modern web interface with Gradio
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- Configurable settings via config.ini
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## Requirements
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- Python 3.8+
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- CUDA-compatible GPU (recommended)
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- FFmpeg installed on your system
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- Ollama (optional, for summarization)
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## Installation
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1. Clone the repository:
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```bash
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git clone <repository-url>
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cd whisperapp
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```
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2. Install the required packages:
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```bash
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pip install -r requirements.txt
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```
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3. Install FFmpeg (if not already installed):
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- Ubuntu/Debian:
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```bash
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sudo apt update && sudo apt install ffmpeg
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```
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- macOS:
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```bash
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brew install ffmpeg
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```
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- Windows: Download from [FFmpeg website](https://ffmpeg.org/download.html)
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4. Copy the example configuration file:
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```bash
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cp .env.example .env
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```
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5. Edit the configuration files:
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- `.env`: Set your environment variables
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- `config.ini`: Configure WhisperX, Ollama, and application settings
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## Configuration
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### Environment Variables (.env)
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```ini
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# Server configuration
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SERVER_NAME=0.0.0.0
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SERVER_PORT=7860
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SHARE=true
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```
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### Application Settings (config.ini)
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```ini
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[whisper]
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default_model = base
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device = cuda
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compute_type = float32
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batch_size = 16
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vad = true
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[app]
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max_duration = 3600
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server_name = 0.0.0.0
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server_port = 7860
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share = true
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[models]
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available_models = tiny,base,small,medium,large-v1,large-v2,large-v3
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[languages]
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available_languages = en,es,fr,de,it,pt,nl,ja,ko,zh
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[ollama]
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enabled = false
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url = http://localhost:11434
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default_model = mistral
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summarize_prompt = Please provide a comprehensive yet concise summary of the following text. Focus on the main points, key arguments, and important details while maintaining accuracy and completeness. Here's the text to summarize:
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```
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## Usage
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1. Start the application:
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```bash
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python app.py
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```
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2. Open your web browser and navigate to:
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```
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http://localhost:7860
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```
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3. Use the interface to:
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- Upload and transcribe local audio/video files
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- Process YouTube videos
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- Generate summaries (if Ollama is configured)
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## Features in Detail
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### Local File Transcription
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- Supports various audio and video formats
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- Automatic language detection
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- Multiple WhisperX model options
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- Optional summarization with Ollama
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### YouTube Video Processing
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- Supports youtube.com, youtu.be, and invidious URLs
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- Automatically extracts subtitles if available
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- Falls back to transcription if no subtitles found
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- Optional summarization with Ollama
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### Summarization
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- Uses Ollama for text summarization
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- Configurable model selection
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- Customizable prompt
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- Available for both local files and YouTube videos
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## Notes
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- For better accuracy, use larger models (medium, large)
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- Processing time increases with model size
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- GPU is recommended for faster processing
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- Maximum audio duration is configurable (default: 60 minutes)
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- YouTube videos will first try to use available subtitles
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- If no subtitles are available, the video will be transcribed
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- Ollama summarization is optional and requires Ollama to be running
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## License
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This project is licensed under the MIT License - see the LICENSE file for details. |