---
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.