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 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
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
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

`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.).
- $\boldsymbol{N}_{\mathrm{VQ}}$ 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

```