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---
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license: apache-2.0
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---
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# **Introduction**
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**`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.
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- **Paper:** [Read on arXiv](https://arxiv.org/abs/2506.23325)
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- **Source Code:**
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- [GitHub Repo](https://github.com/OpenMOSS/MOSS-TTSD/tree/main/XY_Tokenizer)
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- [Hugging Face Repo](https://huggingface.co/spaces/fnlp/MOSS-TTSD/tree/main/XY_Tokenizer)
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## 📚 Related Project: **[MOSS-TTSD](https://huggingface.co/fnlp/MOSS-TTSD-v0.5)**
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**`XY-Tokenizer`** serves as the underlying neural codec for **`MOSS-TTSD`**, our 1.7B Audio Language Model. \
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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).
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## ✨ Features
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- **Dual-channel modeling**: Simultaneously captures semantic meaning and acoustic details
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- **Efficient representation**: 1kbps bitrate with RVQ8 quantization at 12.5Hz
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- **High-quality audio tokenization**: Convert speech to discrete tokens and back with minimal quality loss
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- **Long audio support**: Process audio files longer than 30 seconds using chunking with overlap
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- **Batch processing**: Efficiently process multiple audio files in batches
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- **24kHz output**: Generate high-quality 24kHz audio output
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## 🚀 Installation
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```bash
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git clone https://github.com/OpenMOSS/MOSS-TTSD.git
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cd MOSS-TTSD
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conda create -n xy_tokenizer python=3.10 -y && conda activate xy_tokenizer
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pip install -r XY_Tokenizer/requirements.txt
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```
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## 💻 Quick Start
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Here's how to use **`XY-Tokenizer`** with `transformers` to encode an audio file into discrete tokens and decode it back into a waveform.
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```python
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import os
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import torchaudio
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from transformers import AutoModelForCausalLM
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from transformers.models.moss_ttsd.processor_moss_ttsd import MossTTSDProcessor
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processor = MossTTSDProcessor.from_pretrained(
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"fnlp/MOSS-TTSD-v0.5",
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codec_path="gaoyang07/XY_Tokenizer",
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trust_remote_code=True
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)
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model = AutoModelForCausalLM.from_pretrained(
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"fnlp/MOSS-TTSD-v0.5",
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trust_remote_code=True
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).eval()
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data = [{
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"base_path": "./examples",
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"text": "[S1]单元009，你到底能不能好好工作？我劝你一句，这个时代，不跟上AI浪潮，就会被彻底淘汰！[S2]这个嘛，那我得先问问硅基之主",
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"system_prompt": "你是一个根据文本生成对应音频的语音合成器。",
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"prompt_text": "[S1]嘎子，你听叔的，你听叔的，其实你跟所有人PK，有的时候我也在看，我也在看，无非两，两件事，一个是面子，不想输。[S2]你别说，那天潘老师有一个徒弟开直播，给我开专场，潘老师一徒弟开直播给我开专场，给我一顿骂。",
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"prompt_audio": "panchangjiang_gazi.wav",
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}]
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# Try to use the ExtractorIterator as an iterator
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print("Trying iterator approach...", flush=True)
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inputs = processor(data)
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token_ids = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])
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text, audios = processor.batch_decode(token_ids)
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if not os.path.exists("outputs/"):
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os.mkdir("outputs/")
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for i, data in enumerate(audios):
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for j, fragment in enumerate(data):
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print(f"Saving audio_{i}_{j}.wav...", flush=True)
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torchaudio.save(f"outputs/audio_{i}_{j}.wav", fragment.cpu(), 24000)
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```
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