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README.md

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 license: apache-2.0
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-
-# **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/tree/main/XY_Tokenizer)
-    - [Hugging Face Repo](https://huggingface.co/spaces/fnlp/MOSS-TTSD/tree/main/XY_Tokenizer)
-
-## 📚 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
-model_id = "fnlp/XY_Tokenizer_TTSD_V0"
-feature_extractor = AutoFeatureExtractor.from_pretrained(model_id, trust_remote_code=True)
-codec = AutoModel.from_pretrained(model_id, trust_remote_code=True).eval().to("cuda")
-
-# 2. Load and preprocess the audio
-# The model expects a 16kHz sample rate.
-wav_form, sampling_rate = torchaudio.load("examples/m1.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_features = 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_features)
-print(code)
-
-# 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"audio_{i}.wav", audio.cpu(), 24000)
-```