MOSS-Audio-Tokenizer-Nano-ONNX

This repository provides the ONNX exports of MOSS-Audio-Tokenizer-Nano, the lightweight audio tokenizer used by MOSS-TTS-Nano. It is intended for torch-free deployment with ONNX Runtime and ONNX Runtime Web.

Overview

The Nano variant is a lightweight tokenizer with about 20M parameters, designed to reduce deployment cost while preserving strong perceptual quality.

MOSS-Audio-Tokenizer-Nano supports:

• 48 kHz, stereo audio
• 12.5 Hz token rate
• 16 RVQ codebooks
• high-fidelity reconstruction across variable bitrates

This ONNX repository is designed for lightweight inference pipelines such as:

• local CPU deployment with onnxruntime
• browser deployment with onnxruntime-web
• companion audio encoding/decoding for MOSS-TTS-Nano-100M-ONNX

Supported Backends

Backend	Runtime	Use Case
ONNX Runtime (CPU)	onnxruntime	Local CPU inference
ONNX Runtime Web	onnxruntime-web	Browser-based deployment

Repository Contents

File	Description
moss_audio_tokenizer_encode.onnx	Encoder graph for waveform -> discrete audio codes
moss_audio_tokenizer_encode.data	External weights for the encoder graph
moss_audio_tokenizer_decode_full.onnx	Full decoder graph for audio codes -> waveform
moss_audio_tokenizer_decode_step.onnx	Streaming decoder-step graph for incremental decode
moss_audio_tokenizer_decode_shared.data	External weights shared by the decoder graphs
codec_browser_onnx_meta.json	Metadata for browser / ONNX runtime integration

Quick Start

huggingface-cli download OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano-ONNX \
    --local-dir weights/MOSS-Audio-Tokenizer-Nano-ONNX

This repository is typically used together with OpenMOSS-Team/MOSS-TTS-Nano-100M-ONNX for fully torch-free MOSS-TTS-Nano deployment.

Main Repositories

Repository	Description
OpenMOSS/MOSS-TTS-Nano	MOSS-TTS-Nano source code and inference pipeline
OpenMOSS-Team/MOSS-TTS-Nano	PyTorch MOSS-TTS-Nano weights
OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano	PyTorch MOSS-Audio-Tokenizer-Nano weights
OpenMOSS-Team/MOSS-TTS-Nano-100M-ONNX	Companion ONNX TTS weights

About MOSS-Audio-Tokenizer-Nano

MOSS-Audio-Tokenizer-Nano serves as the lightweight codec backbone for MOSS-TTS-Nano. It keeps the same unified audio-token interface used across the MOSS-TTS family while reducing inference cost for CPU and browser deployment scenarios.

For the original PyTorch implementation, setup instructions, and more background, see:

• MOSS-Audio-Tokenizer Repository
• MOSS-TTS-Nano Repository

Citation

If you use the MOSS-TTS work in your research or product, please cite:

@misc{openmoss2026mossttsnano,
  title={MOSS-TTS-Nano},
  author={OpenMOSS Team},
  year={2026},
  howpublished={GitHub repository},
  url={https://github.com/OpenMOSS/MOSS-TTS-Nano}
}

@misc{gong2026mossttstechnicalreport,
  title={MOSS-TTS Technical Report},
  author={Yitian Gong and Botian Jiang and Yiwei Zhao and Yucheng Yuan and Kuangwei Chen and Yaozhou Jiang and Cheng Chang and Dong Hong and Mingshu Chen and Ruixiao Li and Yiyang Zhang and Yang Gao and Hanfu Chen and Ke Chen and Songlin Wang and Xiaogui Yang and Yuqian Zhang and Kexin Huang and ZhengYuan Lin and Kang Yu and Ziqi Chen and Jin Wang and Zhaoye Fei and Qinyuan Cheng and Shimin Li and Xipeng Qiu},
  year={2026},
  eprint={2603.18090},
  archivePrefix={arXiv},
  primaryClass={cs.SD},
  url={https://arxiv.org/abs/2603.18090}
}

@misc{gong2026mossaudiotokenizerscalingaudiotokenizers,
  title={MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models},
  author={Yitian Gong and Kuangwei Chen and Zhaoye Fei and Xiaogui Yang and Ke Chen and Yang Wang and Kexin Huang and Mingshu Chen and Ruixiao Li and Qingyuan Cheng and Shimin Li and Xipeng Qiu},
  year={2026},
  eprint={2602.10934},
  archivePrefix={arXiv},
  primaryClass={cs.SD},
  url={https://arxiv.org/abs/2602.10934}
}