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Update pictures, metrics and usage in README.

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README.md

@@ -6,21 +6,36 @@ tags:
   - audio-tokenizer
   - neural-codec
   - moss-tts-family
-  - CAT
+  - MOSS Audio Tokenizer
   - speech-tokenizer
   - trust-remote-code
 ---
 
 # MossAudioTokenizer
 
-MossAudioTokenizer is a neural audio codec model for audio tokenization and synthesis.
+MossAudioTokenizer is a Transformer-based neural audio tokenizer model jointly optimizing the encoder, quantizer, and decoder from scratch for high-fidelity reconstruction of general audio, audio tokenization and synthesis. 
+Both the encoder and decoder of MossAudioTokenizer contain approximately 0.8 billion parameters each, totaling about 1.6 billion. MossAudioTokenizer operates at 12.5 Hz, uses a 32-layer residual vector quantizer (RVQ), and supports variable-codebook decoding.
 
 This repository contains a lightweight remote-code implementation that mirrors the current 🤗 Transformers
 `transformers.models.moss_audio_tokenizer` module. It is intended to be uploaded to a Hugging Face Hub model repository
 and loaded with `trust_remote_code=True` when needed.
 
+<br>
+<p align="center">
+    <img src="images/arch.png" width="95%"> <br>
+    Architecture of MossAudioTokenizer
+</p>
+<br>
+
 ## Usage
 
+### Installation
+
+```bash
+cd MOSS-Audio-Tokenizer
+pip install -r requirements.txt
+```
+
 ### Quickstart
 
 ```python
@@ -32,7 +47,36 @@ model = AutoModel.from_pretrained(repo_id, trust_remote_code=True).eval()
 
 audio = torch.randn(1, 1, 3200)  # dummy waveform
 enc = model.encode(audio, return_dict=True)
+print(f"enc.audio_codes.shape: {enc.audio_codes.shape}")
+dec = model.decode(enc.audio_codes, return_dict=True)
+print(f"dec.audio.shape: {dec.audio.shape}")
+```
+
+### Quickstart (Waveform I/O)
+
+```python
+import torch
+from transformers import AutoModel
+import torchaudio
+
+repo_id = "OpenMOSS-Team/MOSS-Audio-Tokenizer"
+model = AutoModel.from_pretrained(repo_id, trust_remote_code=True).eval()
+
+wav, sr = torchaudio.load('demo/demo_gt.wav')
+if sr != model.sampling_rate:
+    wav = torchaudio.functional.resample(wav, sr, model.sampling_rate)
+wav = wav.unsqueeze(0)
+enc = model.encode(wav, return_dict=True)
+print(f"enc.audio_codes.shape: {enc.audio_codes.shape}")
 dec = model.decode(enc.audio_codes, return_dict=True)
+print(f"dec.audio.shape: {dec.audio.shape}")
+wav = dec.audio.squeeze(0)
+torchaudio.save("demo/demo_rec.wav", wav, sample_rate=model.sampling_rate)
+
+# Decode using only the first 8 layers of the RVQ
+dec_rvq8 = model.decode(enc.audio_codes[:8], return_dict=True)
+wav_rvq8 = dec_rvq8.audio.squeeze(0)
+torchaudio.save("demo/demo_rec_rvq8.wav", wav_rvq8, sample_rate=model.sampling_rate)
 ```
 
 ### Streaming
@@ -65,3 +109,67 @@ dec = model.decode(enc.audio_codes, return_dict=True, chunk_duration=0.08)
 - `__init__.py`
 - `config.json`
 - model weights
+
+## Evaluation Metrics
+
+The table below compares the reconstruction quality of open-source audio tokenizers with MossAudioTokenizer on speech and audio/music data.
+
+- Speech metrics are evaluated on LibriSpeech test-clean (English) and AISHELL-2 (Chinese), reported as EN/ZH.
+- Audio metrics are evaluated on the AudioSet evaluation subset, while music metrics are evaluated on MUSDB, reported as audio/music.
+- STFT-Dist. denotes the STFT distance.
+- Higher is better for speech metrics, while lower is better for audio/music metrics (Mel-Loss, STFT-Dist.).
+- $\boldsymbol{N}_{\mathrm{VQ}}$ denotes the number of quantizers.
+
+| Model | bps | Frame rate | $\boldsymbol{N}_{\mathrm{VQ}}$ | Speech: SIM ↑ (EN/ZH) | Speech: STOI ↑ (EN/ZH) | Speech: PESQ-NB ↑ (EN/ZH) | Speech: PESQ-WB ↑ (EN/ZH) | Audio/Music: Mel-Loss ↓ | Audio/Music: STFT-Dist. ↓ |
+| --- | ---: | ---: | ---: | --- | --- | --- | --- | --- | --- |
+| **XCodec2.0** | 800 | 50 | 1 | 0.82 / 0.74 | 0.92 / 0.86 | 3.04 / 2.46 | 2.43 / 1.96 | -- / -- | -- / -- |
+| **MiMo Audio Tokenizer** | 850 | 25 | 4 | 0.80 / 0.74 | 0.91 / 0.87 | 2.94 / 2.62 | 2.39 / 2.14 | **0.82** / 0.81 | 2.33 / 2.23 |
+| **Higgs Audio Tokenizer** | 1000 | 25 | 4 | 0.77 / 0.68 | 0.83 / 0.82 | 3.03 / 2.61 | 2.48 / 2.14 | 0.83 / **0.80** | 2.20 / 2.05 |
+| **SpeechTokenizer** | 1000 | 50 | 2 | 0.36 / 0.25 | 0.77 / 0.68 | 1.59 / 1.38 | 1.25 / 1.17 | -- / -- | -- / -- |
+| **XY-Tokenizer** | 1000 | 12.5 | 8 | 0.85 / 0.79 | 0.92 / 0.87 | 3.10 / 2.63 | 2.50 / 2.12 | -- / -- | -- / -- |
+| **BigCodec** | 1040 | 80 | 1 | 0.84 / 0.69 | 0.93 / 0.88 | 3.27 / 2.55 | 2.68 / 2.06 | -- / -- | -- / -- |
+| **Mimi** | 1100 | 12.5 | 8 | 0.74 / 0.59 | 0.91 / 0.85 | 2.80 / 2.24 | 2.25 / 1.78 | 1.24 / 1.19 | 2.62 / 2.49 |
+| **MOSS Audio Tokenizer (Ours)** | 750 | 12.5 | 6 | 0.82 / 0.75 | 0.93 / 0.89 | 3.14 / 2.73 | 2.60 / 2.22 | 0.86 / 0.85 | 2.21 / 2.10 |
+| **MOSS Audio Tokenizer (Ours)** | 1000 | 12.5 | 8 | **0.88** / **0.81** | **0.94** / **0.91** | **3.38** / **2.96** | **2.87** / **2.43** | **0.82** / **0.80** | **2.16** / **2.04** |
+| **—** | **—** | **—** | **—** | **—** | **—** | **—** | **—** | **—** | **—** |
+| **DAC** | 1500 | 75 | 2 | 0.48 / 0.41 | 0.83 / 0.79 | 1.87 / 1.67 | 1.48 / 1.37 | -- / -- | -- / -- |
+| **Encodec** | 1500 | 75 | 2 | 0.60 / 0.45 | 0.85 / 0.81 | 1.94 / 1.80 | 1.56 / 1.48 | 1.12 / 1.04 | 2.60 / 2.42 |
+| **Higgs Audio Tokenizer** | 2000 | 25 | 8 | 0.90 / 0.83 | 0.85 / 0.85 | 3.59 / 3.22 | 3.11 / 2.73 | 0.74 / 0.70 | 2.07 / 1.92 |
+| **SpeechTokenizer** | 2000 | 50 | 4 | 0.66 / 0.50 | 0.88 / 0.80 | 2.38 / 1.79 | 1.92 / 1.49 | -- / -- | -- / -- |
+| **Qwen3 TTS Tokenizer** | 2200 | 12.5 | 16 | **0.95** / 0.88 | **0.96** / 0.93 | 3.66 / 3.10 | 3.19 / 2.62 | -- / -- | -- / -- |
+| **MiMo Audio Tokenizer** | 2250 | 25 | 12 | 0.89 / 0.83 | 0.95 / 0.92 | 3.57 / 3.25 | 3.05 / 2.71 | **0.70** / **0.68** | 2.21 / 2.10 |
+| **Mimi** | 2475 | 12.5 | 18 | 0.89 / 0.76 | 0.94 / 0.91 | 3.49 / 2.90 | 2.97 / 2.35 | 1.10 / 1.06 | 2.45 / 2.32 |
+| **MOSS Audio Tokenizer (Ours)** | 1500 | 12.5 | 12 | 0.92 / 0.86 | 0.95 / 0.93 | 3.64 / 3.27 | 3.20 / 2.74 | 0.77 / 0.74 | 2.08 / 1.96 |
+| **MOSS Audio Tokenizer (Ours)** | 2000 | 12.5 | 16 | **0.95** / **0.89** | **0.96** / **0.94** | **3.78** / **3.46** | **3.41** / **2.96** | 0.73 / 0.70 | **2.03** / **1.90** |
+| **—** | **—** | **—** | **—** | **—** | **—** | **—** | **—** | **—** | **—** |
+| **DAC** | 3000 | 75 | 4 | 0.74 / 0.67 | 0.90 / 0.88 | 2.76 / 2.47 | 2.31 / 2.07 | 0.86 / 0.83 | 2.23 / 2.10 |
+| **MiMo Audio Tokenizer** | 3650 | 25 | 20 | 0.91 / 0.85 | 0.95 / 0.93 | 3.73 / 3.44 | 3.25 / 2.89 | 0.66 / 0.65 | 2.17 / 2.06 |
+| **SpeechTokenizer** | 4000 | 50 | 8 | 0.85 / 0.69 | 0.92 / 0.85 | 3.05 / 2.20 | 2.60 / 1.87 | -- / -- | -- / -- |
+| **Mimi** | 4400 | 12.5 | 32 | 0.94 / 0.83 | 0.96 / 0.94 | 3.80 / 3.31 | 3.43 / 2.78 | 1.02 / 0.98 | 2.34 / 2.21 |
+| **Encodec** | 4500 | 75 | 6 | 0.86 / 0.75 | 0.92 / 0.91 | 2.91 / 2.63 | 2.46 / 2.15 | 0.91 / 0.84 | 2.33 / 2.17 |
+| **DAC** | 6000 | 75 | 8 | 0.89 / 0.84 | 0.95 / 0.94 | 3.75 / 3.57 | 3.41 / 3.20 | **0.65** / **0.63** | 1.97 / 1.87 |
+| **MOSS Audio Tokenizer (Ours)** | 3000 | 12.5 | 24 | 0.96 / 0.92 | **0.97** / **0.96** | 3.90 / 3.64 | 3.61 / 3.20 | 0.69 / 0.66 | 1.98 / 1.84 |
+| **MOSS Audio Tokenizer (Ours)** | 4000 | 12.5 | 32 | **0.97** / **0.93** | **0.97** / **0.96** | **3.95** / **3.71** | **3.69** / **3.30** | 0.68 / 0.64 | **1.96** / **1.82** |
+
+### LibriSpeech Speech Metrics (MOSS Audio Tokenizer vs. Open-source Tokenizers)
+
+The plots below compare our MOSS Audio Tokenizer model with other open-source speech tokenizers on the LibriSpeech dataset, evaluated with SIM, STOI, PESQ-NB, and PESQ-WB (higher is better).
+We control the bps of the same model by adjusting the number of RVQ codebooks used during inference.
+
+<table>
+  <tr>
+    <td align="center"><b>SIM</b><br><img src="images/sim.png" width="100%"></td>
+    <td align="center"><b>STOI</b><br><img src="images/stoi.png" width="100%"></td>
+  </tr>
+  <tr>
+    <td align="center"><b>PESQ-NB</b><br><img src="images/pesq-nb.png" width="100%"></td>
+    <td align="center"><b>PESQ-WB</b><br><img src="images/pesq-wb.png" width="100%"></td>
+  </tr>
+</table>
+
+
+## Citation
+If you use this code or result in your paper, please cite our work as:
+```tex
+
+```