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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: audio-to-audio |
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tags: |
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- music |
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- code |
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--- |
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# QuarkAudio-HCodec: A Unified Discrete Audio Tokenizer for High-Fidelity, Multitask Audio Generation |
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<p align="center"> |
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<a href="https://arxiv.org/pdf/2512.20151"> |
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<img src="https://img.shields.io/badge/Paper-ArXiv-red.svg" alt="Paper"> |
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</a> |
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<a href="https://github.com/alibaba/unified-audio/tree/main/QuarkAudio-HCodec/"> |
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<img src="https://img.shields.io/badge/GitHub-Code-green.svg" alt="GitHub"> |
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</a> |
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<a href="https://huggingface.co/QuarkAudio/QuarkAudio-HCodec/"> |
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<img src="https://img.shields.io/badge/Model-Hugging%20Face-yellow.svg" alt="Hugging Face"> |
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</a> |
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<a href="https://www.modelscope.cn/models/QuarkAudio/QuarkAudio-HCodec/"> |
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<img src="https://img.shields.io/badge/Model-%20%E9%AD%94%E6%90%AD-orange.svg" alt="ModelScope"> |
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</a> |
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</p> |
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<p align="center"> |
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<a href="https://arxiv.org/pdf/2512.20151"><img src="HCodec.jpg" width="70%" /></a> |
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</p> |
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> 🔊 **H-Codec**: *A Unified, Dual-Stream Neural Audio Codec with Adaptive Frame Rate and 48kHz Support* |
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> Enabling high-fidelity, efficient, and semantically rich audio tokenization for next-generation LLM-based audio generation. |
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🚀 **Key Highlights**: |
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- ✅ **Dual-Stream Tokenization**: Separately quantizes acoustic and semantic features into independent codebooks — preserving both signal fidelity and linguistic content. |
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- 🔄 **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. |
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- ⚙️ **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. |
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- 🌍 **Unified Foundation**: Designed as a core component for multimodal LLMs, supporting diverse downstream tasks: TTS, VC, Editing, TTA, SE, and more. |
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📄 **Paper**: [arXiv:2510.26372](https://arxiv.org/pdf/2512.20151) | 🤗 **Model**: [Hugging Face Spaces](https://huggingface.co/QuarkAudio/QuarkAudio-HCodec/) |
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--- |
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## 📦 Overview |
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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. |
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We extend the original H-Codec (*aka* H-Codec-1.0) in *UniTok-Audio (Liu et al., 2025)* into two advanced variants: |
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| Version | Key Feature | Sampling Rate | Frame Rate | |
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|---------------|----------------------------------|---------------|----------------| |
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| **H-Codec-1.0** | Dual-stream quantization | 16 kHz | Fixed | |
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| **H-Codec-1.5** | Dynamic frame rate adaptation | 16 kHz | Adaptive | |
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| **H-Codec-2.0** | Full-bandwidth 48kHz support | 48 kHz | Fixed | |
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These improvements significantly enhance **audio fidelity**, **temporal efficiency**, and **applicability** across speech, music, and general audio. |
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🔧 **Architecture Core Components**: |
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1. **Encoder**: Extracts continuous representations from waveform and SSL model (e.g., WavLM). |
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2. **Quantizer Module**: Two independent codebooks — one for acoustic details, one for semantic meaning. |
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3. **Decoder**: Reconstructs high-quality audio from discrete token sequences. |
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💡 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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<!-- --- |
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## 🧰 Installation |
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### Option 1: Using pip |
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```bash |
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pip install -r requirements.txt --> |
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--- |
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## 🎯 Quick Start: Run Inference in 3 Minutes |
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### 1. Clone Repository |
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```bash |
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git clone https://github.com/alibaba/unified-audio.git |
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cd QuarkAudio-HCodec |
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``` |
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### 2. Create a Conda environment and install dependencies |
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```bash |
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conda create -n unise python=3.10 |
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conda activate unise |
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pip install -r requirements.txt |
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``` |
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## 3. Tokenizer |
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```bash |
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#!/bin/bash |
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python audio_tokenizer.py |
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``` |
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