README.md

---
license: apache-2.0
library_name: transformers
tags:
  - audio
  - audio-tokenizer
  - neural-codec
  - moss-tts-family
  - MOSS Audio Tokenizer
  - speech-tokenizer
  - trust-remote-code
---

# MossAudioTokenizer

**MOSSAudioTokenizer** is a unified discrete audio tokenizer based on the **Cat** (**C**ausal **A**udio **T**okenizer with **T**ransformer) architecture. Scaling to 1.6 billion parameters, it functions as a unified discrete interface, delivering both lossless-quality reconstruction and high-level semantic alignment.

**Key Features:**

*   **Extreme Compression & Variable Bitrate**: It compresses 24kHz raw audio into a remarkably low frame rate of 12.5Hz. Utilizing a 32-layer Residual Vector Quantizer (RVQ), it supports high-fidelity reconstruction across a wide range of bitrates, from 0.125kbps to 4kbps.
*   **Pure Transformer Architecture**: The model features a "CNN-free" homogeneous architecture built entirely from Causal Transformer blocks. With 1.6B combined parameters (Encoder + Decoder), it ensures exceptional scalability and supports low-latency streaming inference.
*   **Large-Scale General Audio Training**: Trained on 3 million hours of diverse audio data, the model excels at encoding and reconstructing all audio domains, including speech, sound effects, and music.
*   **Unified Semantic-Acoustic Representation**: While achieving state-of-the-art reconstruction quality, Cat produces discrete tokens that are "semantic-rich," making them ideal for downstream tasks like speech understanding (ASR) and generation (TTS).
*   **Fully Trained From Scratch**: Cat does not rely on any pretrained encoders (such as HuBERT or Whisper) or distillation from teacher models. All representations are learned autonomously from raw data.
*   **End-to-End Joint Optimization**: All components—including the encoder, quantizer, decoder, discriminator, and a decoder-only LLM for semantic alignment—are optimized jointly in a single unified training pipeline.

**Summary:**
By combining a simple, scalable architecture with massive-scale data, the Cat architecture overcomes the bottlenecks of traditional audio tokenizers. It provides a robust, high-fidelity, and semantically grounded interface for the next generation of native audio foundation models.

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

### Quickstart

```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

`MossAudioTokenizerModel.encode` and `MossAudioTokenizerModel.decode` support simple streaming via a `chunk_duration`
argument.

- `chunk_duration` is expressed in seconds.
- It must be <= `MossAudioTokenizerConfig.causal_transformer_context_duration`.
- `chunk_duration * MossAudioTokenizerConfig.sampling_rate` must be divisible by `MossAudioTokenizerConfig.downsample_rate`.
- Streaming chunking only supports `batch_size=1`.

```python
import torch
from transformers import AutoModel

repo_id = "OpenMOSS-Team/MOSS-Audio-Tokenizer"
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True).eval()
audio = torch.randn(1, 1, 3200)  # dummy waveform

# 0.08s @ 24kHz = 1920 samples, divisible by downsample_rate=1920
enc = model.encode(audio, return_dict=True, chunk_duration=0.08)
dec = model.decode(enc.audio_codes, return_dict=True, chunk_duration=0.08)
```

## Repository layout

- `configuration_moss_audio_tokenizer.py`
- `modeling_moss_audio_tokenizer.py`
- `__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.).
- Nq denotes the number of quantizers.

| Model | bps | Frame rate | Nq | 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

```