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| title: Transcription | |
| emoji: 👀 | |
| colorFrom: yellow | |
| colorTo: pink | |
| sdk: gradio | |
| sdk_version: 5.15.0 | |
| app_file: app.py | |
| pinned: false | |
| short_description: This tool is intended to help transcribing interviews. | |
| # Audio Transcription App | |
| A Gradio-based web application for transcribing audio files (MP3 or M4A) using OpenAI's Whisper model. Perfect for transcribing interviews and long audio recordings with features like silence removal and audio chunking. | |
| ## Features | |
| - **Multiple Audio File Support**: Process multiple MP3 or M4A files simultaneously | |
| - **Silence Removal**: Option to remove silence from audio to reduce processing time and improve accuracy | |
| - **Audio Chunking**: Split long audio files into manageable chunks for better processing | |
| - **Multiple Language Support**: Supports German (de), English (en), French (fr), Spanish (es), and Italian (it) | |
| - **Multiple Whisper Models**: Choose from various Whisper model sizes (tiny to large-v3-turbo) based on your needs | |
| - **Detailed Output**: Get both full transcriptions and segment-wise transcriptions with timestamps | |
| - **Download Results**: All processed files and transcripts are provided in a convenient ZIP file | |
| ## Setup | |
| 1. Clone the repository | |
| 2. Install the required dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Make sure you have ffmpeg installed on your system | |
| ## Usage | |
| 1. Run the application: | |
| ```bash | |
| python app.py | |
| ``` | |
| 2. Open the provided local URL in your web browser | |
| 3. Upload your audio file(s) | |
| 4. Configure the settings: | |
| - Enable/disable silence removal | |
| - Enable/disable audio chunking | |
| - Select the Whisper model size | |
| - Choose the target language | |
| 5. Click "Process" to start transcription | |
| 6. View the results and download the ZIP file containing all processed files | |
| ## Settings | |
| ### Silence Removal | |
| - **Minimum Silence Length**: 100-2000ms (default: 500ms) | |
| - **Silence Threshold**: -70 to -30dB (default: -50dB) | |
| ### Chunking | |
| - **Chunk Duration**: 60-3600 seconds (default: 600 seconds/10 minutes) | |
| - **FFmpeg Path**: Path to ffmpeg executable (default: "ffmpeg") | |
| ### Transcription | |
| - **Model Size**: Choose from tiny, base, small, medium, large, large-v2, large-v3, turbo, or large-v3-turbo | |
| - **Language**: German (de), English (en), French (fr), Spanish (es), Italian (it) | |
| ## Output | |
| - **Full Transcription**: Complete text of the audio file | |
| - **Segmented Transcription**: Text segments with timestamps | |
| - **ZIP File**: Contains: | |
| - Processed audio files | |
| - Individual transcript files | |
| - Combined transcript file | |
| ## Deployment on Hugging Face Spaces | |
| 1. Create a new Space on Hugging Face | |
| 2. Choose "Gradio" as the SDK | |
| 3. Upload the following files: | |
| - app.py | |
| - requirements.txt | |
| 4. The app will automatically deploy and be available at your Space's URL | |
| ## Requirements | |
| - Python 3.7+ | |
| - ffmpeg | |
| - See requirements.txt for Python package dependencies | |
| ## License | |
| This project is open source and available under the MIT License. | |
| ## Acknowledgments | |
| - [OpenAI Whisper](https://github.com/openai/whisper) | |
| - [Gradio](https://gradio.app/) | |
| - [FFmpeg](https://ffmpeg.org/) |