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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 torchaudio
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from transformers import AutoFeatureExtractor, AutoModel
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# 1. Load the feature extractor and the codec model
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feature_extractor = AutoFeatureExtractor.from_pretrained("MCplayer/XY_Tokenizer", trust_remote_code=True)
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codec = AutoModel.from_pretrained("MCplayer/XY_Tokenizer", trust_remote_code=True, device_map="auto").eval()
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# 2. Load and preprocess the audio
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# The model expects a 16kHz sample rate.
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wav_form, sampling_rate = torchaudio.load("examples/zh_spk1_moon.wav")
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if sampling_rate != 16000:
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wav_form = torchaudio.functional.resample(wav_form, orig_freq=sampling_rate, new_freq=16000)
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# 3. Encode the audio into discrete codes
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input_spectrum = feature_extractor(wav_form, sampling_rate=16000, return_attention_mask=True, return_tensors="pt")
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# The 'code' dictionary contains the discrete audio codes
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code = codec.encode(input_spectrum)
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# 4. Decode the codes back to an audio waveform
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# The output is high-quality 24kHz audio.
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output_wav = codec.decode(code["audio_codes"], overlap_seconds=10)
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# 5. Save the reconstructed audio
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for i, audio in enumerate(output_wav["audio_values"]):
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torchaudio.save(f"outputs/audio_{i}.wav", audio.cpu(), 24000)
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```
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