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--- |
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title: Audio Processing Pipeline for TTS |
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emoji: 🎵 |
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colorFrom: purple |
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colorTo: blue |
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sdk: gradio |
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sdk_version: 5.49.1 |
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app_file: app.py |
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pinned: false |
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license: mit |
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--- |
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# Audio Processing Pipeline for TTS |
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Complete audio processing pipeline for TTS dataset creation. |
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## Features |
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- **Demucs Vocal Separation**: Extract clean vocals using state-of-the-art AI |
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- **Noise Reduction**: Remove background noise with spectral gating |
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- **Loudness Normalization**: Normalize to target dBFS |
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- **Resampling**: Convert to target sample rate (24kHz for TTS) |
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- **Mono Conversion**: Automatic stereo to mono |
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## Usage |
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1. Upload your audio file (WAV format, 44.1kHz recommended) |
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2. Configure options: |
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- Target sample rate (24kHz for TTS) |
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- Target loudness (-20 dBFS recommended) |
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- Enable/disable Demucs vocal separation |
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- Enable/disable noise reduction |
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3. Click "Process Audio" |
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4. Download the processed result |
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## Pipeline |
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``` |
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Input WAV |
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↓ |
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Demucs Vocal Separation (optional) |
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↓ |
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Noise Reduction (optional) |
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↓ |
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Loudness Normalization |
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↓ |
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Resample to Target SR |
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↓ |
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Convert to Mono |
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↓ |
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Output Clean WAV |
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``` |
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## Technical Details |
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- **Demucs Model**: htdemucs (hybrid transformer) |
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- **Denoising**: Spectral gating with noisereduce |
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- **Output Format**: Mono WAV, normalized loudness |
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- **GPU**: Supported for faster processing |
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## Next Steps |
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After processing your audio: |
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1. **Diarization**: Use Pyannote to separate speakers |
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2. **Transcription**: Use Whisper for text generation |
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3. **Dataset**: Package for TTS model training |
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## Tips |
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- Use 44.1kHz WAV input for best quality |
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- Enable Demucs for podcasts with music/background |
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- Enable denoise for noisy recordings |
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- 24kHz output is ideal for TTS training |
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- Processing takes ~30-60 seconds per 5 minutes (CPU mode) |
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## License |
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MIT |
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## Credits |
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- **Demucs**: Meta AI (Facebook Research) |
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- **noisereduce**: Tim Sainburg |
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- **PyTorch Audio**: PyTorch Team |
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- **Gradio**: Hugging Face |
