tencent/StableToken

StableToken: A Noise-Robust Semantic Speech Tokenizer for Resilient SpeechLLMs (ICLR 2026)

StableToken is a noise-robust semantic speech tokenizer that performs discrete speech representation learning, achieving state-of-the-art stability in noisy environments.

📄 Paper | 💻 GitHub

For code and more detailed information, please refer to the corresponding GitHub repository.

Model Details

| Attribute | Value | |:----------|:------| | Frame Rate | 25 Hz | | Codebook Size | 8,192 | | BPS (Bits Per Second) | 325 |

Quick Start

To use StableToken, please clone the official repository and install dependencies.

Installation

git clone --recursive https://github.com/Tencent/StableToken.git
cd StableToken && pip install -r requirements.txt

Inference

import os
from huggingface_hub import snapshot_download
from transformers import WhisperFeatureExtractor
from src.model.modeling_whisper import WhisperLFQEncoder
from src.utils.flow_inference import AudioDecoder
from src.utils.utils import extract_speech_token, speech_token_to_wav

# 1. Download & Load Models
model_dir = snapshot_download("tencent/StableToken")

# Load Tokenizer
tokenizer = WhisperLFQEncoder.from_pretrained(os.path.join(model_dir, "tokenizer")).eval().cuda()
feature_extractor = WhisperFeatureExtractor.from_pretrained(os.path.join(model_dir, "tokenizer"))

# Load Decoder
decoder = AudioDecoder(
config_path=os.path.join(model_dir, "decoder", "config.yaml"),
flow_ckpt_path=os.path.join(model_dir, "decoder", "flow.pt"),
hift_ckpt_path=os.path.join(model_dir, "decoder", "hift.pt"),
device="cuda"
)

# 2. Tokenize
tokens = extract_speech_token(tokenizer, feature_extractor, ["/path/to/audio.wav"], device="cuda")[0]

# 3. Reconstruct
tts_speech, sampling_rate = speech_token_to_wav(decoder, tokens)

Performance

StableToken achieves 60% lower UED (Unit Edit Distance) than best existing supervised semantic tokenizers.

Noise Robustness (UED ↓)

| Model | Frame Rate | Codebook Size | UED (%, ↓) | |:---|:---:|:---:|:---:| | GLM-4-Voice-Tokenizer | 12.5Hz | 16,384 | 31.10 | | S3 Tokenizer | 25Hz | 4,096 | 26.17 | | CosyVoice2 | 25Hz | 6,561 | 38.66 | | StableToken | 25Hz | 8,192 | 10.17 🏆 |

Reconstruction Quality

Measurements on LibriSpeech (LS) and SEED benchmarks.

| Model | Frame Rate | BPS | WER (↓) LS-clean | WER (↓) LS-other | WER (↓) SEED-en | WER (↓) SEED-zh | MOS (↑) LS-clean | MOS (↑) LS-other | MOS (↑) SEED-en | MOS (↑) SEED-zh | |:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | GLM-4-Voice-Tokenizer | 12.5Hz | 175 | 4.04 | 9.33 | 3.54 | 3.23 | 4.07 | 3.99 | 4.16 | 4.10 | | S3 Tokenizer | 25Hz | 300 | 5.78 | 13.38 | 5.91 | 4.26 | 3.40 | 3.31 | 3.40 | 3.31 | | CosyVoice2 | 25Hz | 325 | 4.25 | 9.68 | 4.34 | 2.75 | 3.36 | 3.25 | 3.31 | 3.58 | | StableToken | 25Hz | 325 | 3.84 | 7.99 | 3.44 | 2.62 | 4.09 | 3.83 | 4.01 | 4.18 |

Citation

@article{song2025stabletoken,
title={StableToken: A Noise-Robust Semantic Speech Tokenizer for Resilient SpeechLLMs},
author={Song, Yuhan and Zhang, Linhao and Wu, Chuhan and Liu, Aiwei and Jia, Wei and Wang, Houfeng and Zhou, Xiao},
journal={arXiv preprint arXiv:2509.22220},
year={2025}
}

License

This project is licensed under the [License Term of StableToken](LICENSE).