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---
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. |