README.md

---
license: apache-2.0
pipeline_tag: audio-to-audio
library_name: transformers
---

# XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs

## Introduction

**`XY-Tokenizer`** is a speech codec that simultaneously models both semantic and acoustic aspects of speech, converting audio into discrete tokens and decoding them back to high-quality audio. It achieves efficient speech representation at only 1kbps with RVQ8 quantization at 12.5Hz frame rate. 

-   **Paper:** [Read on arXiv](https://arxiv.org/abs/2506.23325)
-   **Source Code:** [GitHub Repo](https://github.com/OpenMOSS/MOSS-TTSD)

## 📚 Related Project: **[MOSS-TTSD](https://huggingface.co/fnlp/MOSS-TTSD-v0.5)**

**`XY-Tokenizer`** serves as the underlying neural codec for **`MOSS-TTSD`**, our 1.7B Audio Language Model. \
Explore **`MOSS-TTSD`** for advanced text-to-speech and other audio generation tasks on [GitHub](https://github.com/OpenMOSS/MOSS-TTSD), [Blog](http://www.open-moss.com/en/moss-ttsd/), [博客](https://www.open-moss.com/cn/moss-ttsd/), and [Space Demo](https://huggingface.co/spaces/fnlp/MOSS-TTSD).

## ✨ Features

- **Dual-channel modeling**: Simultaneously captures semantic meaning and acoustic details
- **Efficient representation**: 1kbps bitrate with RVQ8 quantization at 12.5Hz
- **High-quality audio tokenization**: Convert speech to discrete tokens and back with minimal quality loss
- **Long audio support**: Process audio files longer than 30 seconds using chunking with overlap
- **Batch processing**: Efficiently process multiple audio files in batches
- **24kHz output**: Generate high-quality 24kHz audio output


## 🚀 Installation

```bash
git clone https://github.com/OpenMOSS/MOSS-TTSD.git
cd MOSS-TTSD
conda create -n xy_tokenizer python=3.10 -y && conda activate xy_tokenizer
pip install -r XY_Tokenizer/requirements.txt
```

## 💻 Quick Start

Here's how to use **`XY-Tokenizer`** with `transformers` to encode an audio file into discrete tokens and decode it back into a waveform.

```python
import torchaudio
from transformers import AutoFeatureExtractor, AutoModel

# 1. Load the feature extractor and the codec model
feature_extractor = AutoFeatureExtractor.from_pretrained("MCplayer/XY_Tokenizer", trust_remote_code=True)
codec = AutoModel.from_pretrained("MCplayer/XY_Tokenizer", trust_remote_code=True, device_map="auto").eval()

# 2. Load and preprocess the audio
# The model expects a 16kHz sample rate.
wav_form, sampling_rate = torchaudio.load("examples/zh_spk1_moon.wav")
if sampling_rate != 16000:
    wav_form = torchaudio.functional.resample(wav_form, orig_freq=sampling_rate, new_freq=16000)

# 3. Encode the audio into discrete codes
input_spectrum = feature_extractor(wav_form, sampling_rate=16000, return_attention_mask=True, return_tensors="pt")
# The 'code' dictionary contains the discrete audio codes
code = codec.encode(input_spectrum)

# 4. Decode the codes back to an audio waveform
# The output is high-quality 24kHz audio.
output_wav = codec.decode(code["audio_codes"], overlap_seconds=10)

# 5. Save the reconstructed audio
for i, audio in enumerate(output_wav["audio_values"]):
    torchaudio.save(f"outputs/audio_{i}.wav", audio.cpu(), 24000)

```

## Available Models 🗂️

| Model Name | Hugging Face | Training Data |
|:----------:|:-------------:|:---------------:|
| XY-Tokenizer | [🤗](https://huggingface.co/fdugyt/XY_Tokenizer) | Emilia |
| XY-Tokenizer-TTSD-V0 (used in [MOSS-TTSD](https://github.com/OpenMOSS/MOSS-TTSD)) | [🤗](https://huggingface.co/fnlp/XY_Tokenizer_TTSD_V0/) | Emilia + Internal Data (containing general audio) |

## Demos 🎮

See our blog for more demos at [Blog](http://www.open-moss.com/en/moss-ttsd/)

## License 📜

XY-Tokenizer is released under the Apache 2.0 license.

## Citation 📚

```
@misc{gong2025xytokenizermitigatingsemanticacousticconflict,
      title={XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs}, 
      author={Yitian Gong and Luozhijie Jin and Ruifan Deng and Dong Zhang and Xin Zhang and Qinyuan Cheng and Zhaoye Fei and Shimin Li and Xipeng Qiu},
      year={2025},
      eprint={2506.23325},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2506.23325}, 
}
```