MOSS-Audio-Tokenizer-Nano

This repository contains the Hugging Face remote-code implementation and weights for MOSS-Audio-Tokenizer-Nano, the lightweight audio tokenizer used by MOSS-TTS-Nano.

MOSS-Audio-Tokenizer-Nano is a compact discrete audio tokenizer based on the Cat (Causal Audio Tokenizer with Transformer) architecture from MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models. The checkpoint in this repository has 21,969,664 parameters (approximately 22M), making it much smaller than the full-size MOSS-Audio-Tokenizer while preserving the 48 kHz stereo tokenizer interface used by the MOSS-TTS family.

Key Features

• Small model size: approximately 22M parameters, including about 10.45M encoder parameters, 10.45M decoder parameters, and 1.07M quantizer parameters.
• Native high-resolution audio: supports 48 kHz input and output with 2-channel stereo audio, helping reduce compression loss and improve listening quality.
• Low-frame-rate discrete codes: compresses 48 kHz stereo audio into a 12.5 Hz token stream with a downsample rate of 7,680 samples.
• Variable bitrate reconstruction: uses a residual quantizer stack with 16 codebooks and 1,024 entries per codebook. Each codebook contributes about 0.125 kbps, for an inference range from 0.125 kbps to 2 kbps.
• Transformer-based tokenizer: uses causal Transformer blocks and supports low-latency streaming encode/decode.
• MOSS-TTS family interface: designed as the audio tokenizer backbone for MOSS-TTS-Nano and compatible MOSS-TTS-family workflows.

Summary: By combining a compact causal Transformer tokenizer with native 48 kHz stereo modeling, MOSS-Audio-Tokenizer-Nano reduces the deployment cost of the MOSS audio tokenizer interface while keeping high-fidelity reconstruction for speech, general audio, and music. It provides a lightweight, low-frame-rate, and streaming-friendly discrete audio representation for MOSS-TTS-Nano and other real-time speech generation workflows.

This repository contains a lightweight remote-code implementation that mirrors the current Hugging Face Transformers transformers.models.moss_audio_tokenizer module. Load it with trust_remote_code=True when needed.

Evaluation Metrics

The table below compares the reconstruction quality of MOSS-Audio-Tokenizer-Nano with open-source audio tokenizers with no more than 120M parameters on speech, audio, and music data. MOSS-Audio-Tokenizer-Nano keeps one of the smallest model sizes in the comparison while supporting 48 kHz stereo reconstruction.

• 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.).
• Ch. denotes the number of input/output channels supported by the audio tokenizer: ch=1 means mono audio, and ch=2 means stereo audio.
• Nvq denotes the number of quantizers.

Usage

Quickstart

import torchaudio
from transformers import AutoModel

repo_id = "OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano"
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)

# The public waveform interface expects stereo audio.
if wav.shape[0] == 1:
    wav = wav.repeat(model.config.number_channels, 1)
else:
    wav = wav[: model.config.number_channels]

wav = wav.unsqueeze(0)
en