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Browse files# QuarkAudio-HCodec: A Unified Discrete Audio Tokenizer for High-Fidelity, Multitask Audio Generation
<p align="center">
<a href="https://arxiv.org/abs/2510.26372">
<img src="https://img.shields.io/badge/Paper-ArXiv-red.svg" alt="Paper">
</a>
<a href="https://huggingface.co/spaces/QuarkAudio/">
<img src="https://img.shields.io/badge/Model-Hugging%20Face-yellow.svg" alt="Hugging Face">
</a>
</p>
<p align="center">
<a href="https://arxiv.org/abs/2510.26372"><img src="HCodec.jpg" width="70%" /></a>
</p>
> 🔊 **H-Codec**: *A Unified, Dual-Stream Neural Audio Codec with Adaptive Frame Rate and 48kHz Support*
> Enabling high-fidelity, efficient, and semantically rich audio tokenization for next-generation LLM-based audio generation.
🚀 **Key Highlights**:
- ✅ **Dual-Stream Tokenization**: Separately quantizes acoustic and semantic features into independent codebooks — preserving both signal fidelity and linguistic content.
- 🔄 **Dynamic Frame Rate (H-Codec-1.5)**: Introduces an adaptive temporal resolution mechanism built upon H-Codec-1.0, enabling variable frame rates based on content complexity.
- ⚙️ **Multi-Sampling Rate (H-Codec-2.0)**: Extends the sampling rate from **16kHz to 48kHz** under a fixed frame rate, significantly improving audio fidelity and high-frequency detail preservation.
- 🌍 **Unified Foundation**: Designed as a core component for multimodal LLMs, supporting diverse downstream tasks: TTS, VC, Editing, TTA, SE, and more.
📄 **Paper**: [arXiv:2510.26372](https://arxiv.org/abs/2510.26372) | 🎤 **Listen**: [Demo Page](https://hyyan2k.github.io/UniSE/) | 🤗 **Model**: [Hugging Face Spaces](https://huggingface.co/spaces/QuarkAudio/)
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## 📦 Overview
This project introduces **H-Codec**, a unified discrete audio tokenizer that integrates self-supervised learning (SSL) representations into the codec architecture to enable **dual-stream (acoustic + semantic) tokenization**. Unlike prior work that fuses modalities before quantization (e.g., X-Codec), H-Codec employs **separate codebooks** for acoustic and semantic streams, allowing independent optimization and better reconstruction quality.
We extend the original H-Codec (*aka* H-Codec-1.0) in *UniTok-Audio (Liu et al., 2025)* into two advanced variants:
| Version | Key Feature | Sampling Rate | Frame Rate |
|---------------|----------------------------------|---------------|----------------|
| **H-Codec-1.0** | Dual-stream quantization | 16 kHz | Fixed |
| **H-Codec-1.5** | Dynamic frame rate adaptation | 16 kHz | Adaptive |
| **H-Codec-2.0** | Full-bandwidth 48kHz support | 48 kHz | Fixed |
These improvements significantly enhance **audio fidelity**, **temporal efficiency**, and **applicability** across speech, music, and general audio.
🔧 **Architecture Core Components**:
1. **Encoder**: Extracts continuous representations from waveform and SSL model (e.g., WavLM).
2. **Quantizer Module**: Two independent codebooks — one for acoustic details, one for semantic meaning.
3. **Decoder**: Reconstructs high-quality audio from discrete token sequences.
💡 H-Codec is designed as a foundational module for **LLM-based audio generation**, seamlessly integrating with autoregressive language models for end-to-end training and inference.
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## 🧰 Installation
### Option 1: Using pip
```bash
pip install -r requirements.txt -->
---
## 🎯 Quick Start: Run Inference in 3 Minutes
### 1. Clone Repository
```bash
git clone https://github.com/alibaba/unified-audio.git
cd QuarkAudio-HCodec
```
### 2. Create a Conda environment and install dependencies
```bash
conda create -n unise python=3.10
conda activate unise
pip install -r requirements.txt
```
## 3. Tokenizer
```bash
#!/bin/bash
python audio_tokenizer.py
```
