README.md

metadata

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.

Architecture of MossAudioTokenizer

Usage

Installation

cd MOSS-Audio-Tokenizer
pip install -r requirements.txt

Quickstart

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)

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.

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.

SIM	STOI
PESQ-NB	PESQ-WB

Citation

If you use this code or result in your paper, please cite our work as: