| | --- |
| | language: |
| | - en |
| | tags: |
| | - audio |
| | - music |
| | - codec |
| | - neural-audio |
| | - audio-compression |
| | - transformers |
| | pipeline_tag: audio-to-audio |
| | library_name: transformers |
| | inference: true |
| | --- |
| | |
| |
|
| | # XCodec Mini - Neural Audio Codec |
| |
|
| | ## Model Description |
| |
|
| | XCodec Mini is a state-of-the-art neural audio codec designed for high-quality music compression and reconstruction. It combines semantic and acoustic encoding approaches to achieve efficient compression while maintaining audio quality. |
| |
|
| | ### Key Features |
| |
|
| | - **Dual Encoding Architecture** |
| | - Semantic encoder for high-level musical features |
| | - Acoustic encoder for detailed sound information |
| | - Multi-scale processing for efficient compression |
| |
|
| | - **Advanced Compression** |
| | - Multiple codebooks for flexible quality/size tradeoff |
| | - Support for 44.1kHz high-fidelity audio |
| | - Separate processing paths for vocals and instrumentals |
| |
|
| | - **Technical Specifications** |
| | - Input: Raw audio at 44.1kHz |
| | - Output: Compressed representations and reconstructed audio |
| | - Model Size: [Add total size] |
| | - Compression Ratio: [Add typical ratio] |
| |
|
| | ## Intended Uses |
| |
|
| | - High-quality music compression |
| | - Audio archival and storage |
| | - Music streaming applications |
| | - Audio processing pipelines |
| |
|
| | ## Training Data |
| |
|
| | The model was trained on a diverse dataset of music, including: |
| | - Various genres and styles |
| | - Vocal and instrumental tracks |
| | - High-quality studio recordings |
| |
|
| | ## Performance and Limitations |
| |
|
| | ### Strengths |
| | - High-quality audio reconstruction |
| | - Efficient compression ratios |
| | - Separate handling of vocals and instrumentals |
| | - Support for high sample rates |
| |
|
| | ### Limitations |
| | - Computationally intensive for real-time applications |
| | - Requires significant GPU memory |
| | - Best suited for offline processing |
| | - May introduce artifacts in extreme compression settings |
| |
|
| | ## Technical Specifications |
| |
|
| | ### Model Architecture |
| | 1. **Semantic Encoder** |
| | - Based on HuBERT architecture |
| | - Captures high-level musical features |
| | - Outputs semantic tokens |
| |
|
| | 2. **Acoustic Encoder** |
| | - Multi-scale convolutional architecture |
| | - Processes detailed sound information |
| | - Generates acoustic tokens |
| |
|
| | 3. **Dual Decoders** |
| | - Separate decoders for vocals and instrumentals |
| | - Multi-stage reconstruction process |
| | - Quality-focused design |
| |
|
| | ### Input Requirements |
| | - Audio Format: WAV/MP3 |
| | - Sample Rate: 44.1kHz |
| | - Channels: Mono/Stereo |
| | - Bit Depth: 16-bit |
| |
|
| | ### Output Format |
| | - Reconstructed Audio: 44.1kHz WAV |
| | - Intermediate Representations: Compressed tokens |
| |
|
| | ## Usage Guidelines |
| |
|
| | ### Hardware Requirements |
| | - GPU: NVIDIA GPU with 8GB+ VRAM |
| | - RAM: 16GB+ recommended |
| | - Storage: SSD recommended for faster processing |
| |
|
| | ### Software Requirements |
| | - Python 3.8+ |
| | - PyTorch 2.0+ |
| | - CUDA 11.0+ |
| | - Additional dependencies listed in installation guide |
| |
|
| | ## Ethical Considerations |
| |
|
| | - **Copyright**: Users should ensure they have proper rights to process copyrighted material |
| | - **Attribution**: Proper attribution should be given when using this model |
| | - **Data Privacy**: Consider data privacy implications when processing sensitive audio |
| |
|
| |
|
| | ## Additional Information |
| |
|
| | ### Model Weights |
| | The model requires several checkpoint files: |
| | - Semantic Encoder |
| | - Vocal Decoder |
| | - Instrumental Decoder |
| | - Final Checkpoint |
| |
|
| | ### Contact |
| | For issues and questions, please use the GitHub repository's issue tracker. |
