Upload Hcodec readme


README.md

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 - music
 - code
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+# 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/)
+
+---
+
+## 📦 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.
+
+<!-- ---
+
+## 🧰 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
+```