Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language:
+- en
+tags:
+- audio
+- music
+- codec
+- neural-audio
+- audio-compression
+license: apache-2.0
+pipeline_tag: audio-to-audio
+inference: false
+---
+
+# 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: `semantic_ckpts/hf_1_325000/pytorch_model.bin`
+- Vocal Decoder: `decoders/decoder_131000.pth`
+- Instrumental Decoder: `decoders/decoder_151000.pth`
+- Final Checkpoint: `final_ckpt/ckpt_00360000.pth`
+
+### Contact
+For issues and questions, please use the GitHub repository's issue tracker.