HCodec 1.5 Adaptive

--- license: apache-2.0 language: - en pipeline_tag: audio-to-audio --- # QuarkAudio-HCodec-1.5: A Unified Discrete Audio Tokenizer with adaptive frame rate for High-Fidelity, Multitask Audio Generation <p align="center"> <a href="https://arxiv.org/pdf/2512.20151"> <img src="https://img.shields.io/badge/Paper-ArXiv-red.svg" alt="Paper"> </a> <a href="https://github.com/alibaba/unified-audio/tree/main/QuarkAudio-UniSE"> <img src="https://img.shields.io/badge/GitHub-Code-green.svg" alt="GitHub"> </a> <a href="https://github.com/alibaba/unified-audio/tree/main/QuarkAudio-HCodec/HCodec-1.5/"> <img src="https://img.shields.io/badge/Model-Hugging%20Face-yellow.svg" alt="Hugging Face"> </a> <a href="https://www.modelscope.cn/models/QuarkAudio/QuarkAudio-HCodec/"> <img src="https://img.shields.io/badge/Model-%20%E9%AD%94%E6%90%AD-orange.svg" alt="ModelScope"> </a> </p> <p align="center"> <a href="https://arxiv.org/pdf/2512.20151"><img src="HCodec-1.5.png" width="70%" /></a> </p> ## 🎯 Quick Start: Run Inference in 3 Minutes ## 1. Installation 1. Install dependencies from requirement.txt via pypi 2. Download pretrained weights from Huggingface &#x1F917;: [QuarkAudio/HCodec-1.5-adaptive](https://huggingface.co/QuarkAudio/HCodec-1.5-adaptive) and save them to ./checkpoints/ 3. confirm the `ckpt_path` in file `conf/config_adaptive_v3.yaml` is valid ### 2. Clone Repository ```bash git clone https://github.com/alibaba/unified-audio.git cd QuarkAudio-HCodec ``` ### 3. Create a Conda environment and install dependencies ```bash conda create -n unise python=3.10 conda activate unise pip install -r requirements.txt ``` ## 4. Tokenizer ```bash #!/bin/bash python audio_tokenizer.py ``` ## 5. Optional configuration + Customize your testing options about adaptive frame rate ```yaml # hyperparameter configuration in conf/config_adaptive_v3.yaml training: false # keep false when testing use_similarity_alignment: true use_dynamic_similarity_threshold: false infer_using_dynamic_threshold: true # work when manual_threshold is null similarity_threshold: 0.7 similarity_threshold_lower: 0.7 similarity_threshold_upper: 1.0 # valid interval of dynamic threshold when 'infer_using_dynamic_threshold' turns on max_tokens_per_group: 8 manual_threshold: 0.6 # set to a fixed value when evaluate specific threshold ``` ## 😘 Acknowlegement We would like to th...