mirror of
https://github.com/harivansh-afk/sep.git
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170 lines
3.2 KiB
Markdown
170 lines
3.2 KiB
Markdown
# Audio Separator API
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REST API for separating audio into vocal and instrumental stems using ML models.
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## Quick Start
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```bash
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# Clone and install
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git clone <repo-url>
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cd sep
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chmod +x install.sh test.sh
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sudo ./install.sh
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# Run tests
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./test.sh
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# Start the API
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.venv/bin/uvicorn app:app --host 0.0.0.0 --port 8000
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```
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## Requirements
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- Python 3.10+
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- FFmpeg
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- 10GB+ disk space (for models)
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- NVIDIA GPU with CUDA (optional, but recommended)
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## API Endpoints
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### Health Check
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```bash
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curl http://localhost:8000/health
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```
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Response:
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```json
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{
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"status": "healthy",
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"cuda_available": true,
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"cuda_device": "NVIDIA GeForce RTX 5090"
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}
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```
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### Separate Audio
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```bash
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curl -X POST http://localhost:8000/separate \
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-F "file=@song.mp3" \
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-F "output_format=mp3"
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```
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Response:
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```json
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{
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"job_id": "a1b2c3d4",
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"status": "completed",
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"vocals_url": "/download/song_(Vocals)_model_bs_roformer.mp3",
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"instrumental_url": "/download/song_(Instrumental)_model_bs_roformer.mp3"
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}
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```
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### Download Stems
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```bash
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curl -O http://localhost:8000/download/song_(Vocals)_model_bs_roformer.mp3
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```
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### List Models
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```bash
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curl http://localhost:8000/models
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```
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## Configuration
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### Output Formats
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- `mp3` (default) - Good compression, iOS compatible
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- `wav` - Lossless, larger files
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- `flac` - Lossless compression
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### Models
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| Model | Quality | Speed | Best For |
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|-------|---------|-------|----------|
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| BS-RoFormer (default) | Highest | Slow | Production use |
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| UVR_MDXNET_KARA_2 | Good | Fast | Karaoke |
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| Kim_Vocal_2 | Good | Medium | Vocal isolation |
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## VM Deployment
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### Using systemd (Linux)
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The install script creates a systemd service:
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```bash
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sudo systemctl enable audio-separator
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sudo systemctl start audio-separator
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sudo systemctl status audio-separator
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```
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### Manual Start
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```bash
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.venv/bin/uvicorn app:app --host 0.0.0.0 --port 8000 --workers 1
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```
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Note: Use `--workers 1` because the ML model is not thread-safe.
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## GPU Support
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The API automatically detects CUDA GPUs. To verify:
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```bash
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./test.sh
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```
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Look for:
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```
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[PASS] CUDA available: NVIDIA GeForce RTX 5090 (32.0GB VRAM)
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```
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### CUDA Installation (Ubuntu)
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```bash
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# Add NVIDIA repo
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wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
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sudo dpkg -i cuda-keyring_1.1-1_all.deb
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sudo apt-get update
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sudo apt-get install -y cuda-toolkit-12-1
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```
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## iOS Integration
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The API returns MP3 files by default, which are natively supported on iOS.
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Example Swift code:
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```swift
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func separateAudio(fileURL: URL) async throws -> (vocals: URL, instrumental: URL) {
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var request = URLRequest(url: URL(string: "http://your-vm:8000/separate")!)
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request.httpMethod = "POST"
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// Upload file and get response with download URLs
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// ...
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}
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```
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## File Cleanup
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Uploaded and output files are automatically deleted after 5 minutes.
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## Troubleshooting
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### "CUDA not available"
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1. Check NVIDIA drivers: `nvidia-smi`
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2. Reinstall PyTorch with CUDA:
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```bash
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uv pip install torch --index-url https://download.pytorch.org/whl/cu121
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```
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### "Model download failed"
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Check network access to huggingface.co and github.com.
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### "Out of memory"
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Reduce batch size or use a smaller model like `UVR_MDXNET_KARA_2`.
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