📢：Good news! 21,800 hours of multi-label Cantonese speech data and 10,000 hours of multi-label Chuan-Yu speech data are also available at ⭐WenetSpeech-Yue⭐ and ⭐WenetSpeech-Chuan⭐.

WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem

Chengyou Wang¹*, Mingchen Shao¹*, Jingbin Hu¹*, Zeyu Zhu¹*, Hongfei Xue¹, Bingshen Mu¹, Xin Xu², Xingyi Duan⁶, Binbin Zhang³, Pengcheng Zhu³, Chuang Ding⁴, Xiaojun Zhang⁵, Hui Bu², Lei Xie^1†

¹ Audio, Speech and Language Processing Group (ASLP@NPU), Northwestern Polytechnical University
² Beijing AISHELL Technology Co., Ltd.
³ WeNet Open Source Community
⁴ Moonstep AI
⁵ Xi'an Jiaotong-Liverpool University
⁶ YK Pao School

📑 Paper    |    🐙 GitHub    |    🤗 HuggingFace
🎤 Demo Page    |    💬 Contact Us

This repository contains the official WenetSpeech-Wu dataset, the WenetSpeech-Wu benchmark, and related models.

📢 Demo Page

The demo page provides audio data samples, ASR and TTS leaderboards, and the TTS samples.

👉 Demo: Demo Page

Download

• The WenetSpeech-Wu dataset will be available at WenetSpeech-Wu.
• The WenetSpeech-Wu benchmark will be available at WenetSpeech-Wu-Bench.
• The ASR and understanding models will be available at WSWu-Understanding.
• The TTS and instruct TTS models will be available at WSWu-Generation.

Dataset

WenetSpeech-Wu is the first large-scale Wu dialect speech corpus with multi-dimensional annotations. It contains rich metadata and annotations, including transcriptions with confidence scores, Wu-to-Mandarin translations, domain and sub-dialect labels, speaker attributes, emotion annotations, and audio quality measures. The dataset comprises approximately 8,000 hours of speech collected from diverse domains and covers eight Wu sub-dialects. To support a wide range of speech processing tasks with heterogeneous quality requirements, we further adopt a task-specific data quality grading strategy.

WenetSpeech-Wu-Bench

We introduce WenetSpeech-Wu-Bench, the first publicly available, manually curated benchmark for Wu dialect speech processing, covering ASR, Wu-to-Mandarin AST, speaker attributes, emotion recognition, TTS, and instruct TTS, and providing a unified platform for fair evaluation.

• ASR: Wu dialect ASR (9.75 hour, Shanghainese, Suzhounese, and Mandarin code-mixed speech). Evaluated by CER.
• Wu→Mandarin AST: Speech translation from Wu dialects to Mandarin (3k utterances, 4.4h). Evaluated by BLEU.
• Speaker & Emotion: Speaker gender/age prediction and emotion recognition on Wu speech. Evaluated by classification accuracy.
• TTS: Wu dialect TTS with speaker prompting (242 sentences, 12 speakers). Evaluated by speaker similarity, CER, and MOS.
• Instruct TTS: Instruction-following TTS with prosodic and emotional control. Evaluated by automatic accuracy and subjective MOS.

Data Construction Pipeline for WenetSpeech-Wu

We propose an automatic and scalable pipeline for constructing a large-scale Wu dialect speech dataset with multi-dimensional annotations, as illustrated in the figure below. The pipeline is designed to enable efficient data collection, robust automatic transcription, and diverse downstream annotations.

ASR & Understanding Leaderboard

Bold and underlined values denote the best and second-best results.

ASR results (CER%) on various test sets

Model	In-House		WS-Wu-Bench
	Dialogue	Reading	ASR
ASR Models
Paraformer	63.13	66.85	64.92
SenseVoice-small	29.20	31.00	46.85
Whisper-medium	79.31	83.94	78.24
FireRedASR-AED-L	51.34	59.92	56.69
Step-Audio2-mini	24.27	24.01	26.72
Qwen3-ASR	23.96	24.13	29.31
Tencent-Cloud-ASR	23.25	25.26	29.48
Gemini-2.5-pro	85.50	84.67	89.99
Conformer-U2pp-Wu ⭐	15.20	12.24	15.14
Whisper-medium-Wu ⭐	14.19	11.09	14.33
Step-Audio2-Wu-ASR ⭐	8.68	7.86	12.85
Annotation Models
Dolphin-small	24.78	27.29	26.93
TeleASR	29.07	21.18	30.81
Step-Audio2-FT	8.02	6.14	15.64
Tele-CTC-FT	11.90	7.23	23.85

Speech understanding performance on WenetSpeech-Wu-Bench

Model	ASR	AST	Gender	Age	Emotion
Qwen3-Omni	44.27	33.31	0.977	0.541	0.667
Step-Audio2-mini	26.72	37.81	0.855	0.370	0.460
Step-Audio2-Wu-Und⭐	13.23	53.13	0.956	0.729	0.712

TTS and Instruct TTS Leaderboard

Bold and underlined values denote the best and second-best results.

TTS results on WenetSpeech-Wu-Bench.

Model	CER (%)↓	SIM ↑	IMOS ↑	SMOS ↑	AMOS ↑	CER (%)↓	SIM ↑	IMOS ↑	SMOS ↑	AMOS ↑
Qwen3-TTS†	5.95	--	4.35	--	4.19	16.45	--	4.03	--	3.91
DiaMoE-TTS	57.05	0.702	3.11	3.43	3.52	82.52	0.587	2.83	3.14	3.22
CosyVoice2	10.33	0.713	3.83	3.71	3.84	82.49	0.618	3.24	3.42	3.37
CosyVoice2-Wu-CPT⭐	6.35	0.727	4.01	3.84	3.92	32.97	0.620	3.72	3.55	3.63
CosyVoice2-Wu-SFT⭐	6.19	0.726	4.32	3.78	4.11	25.00	0.601	3.96	3.48	3.76
CosyVoice2-Wu-SS⭐	5.42	--	4.37	--	4.21	15.45	--	4.04	--	3.88

Performance of instruct TTS model.

Type	Metric	CosyVoice2-Wu-SFT⭐	CosyVoice2-Wu-instruct⭐
Emotion	Happy ↑	0.87	0.94
	Angry ↑	0.83	0.87
	Sad ↑	0.84	0.88
	Surprised ↑	0.67	0.73
	EMOS ↑	3.66	3.83
Prosody	Pitch ↑	0.24	0.74
	Speech Rate ↑	0.26	0.82
	PMOS ↑	2.13	3.68

ASR & Speech Understanding Inference

This section describes the inference procedures for different speech models used in our experiments, including Conformer-U2pp-Wu, Whisper-Medium-Wu, Step-Audio2-Wu-ASR and Step-Audio2-Wu-Und. Different models are trained and inferred under different frameworks, with corresponding data formats.

---

Conformer-U2pp-Wu

dir=exp
data_type=raw
decode_checkpoint=$dir/u2++.pt
decode_modes="attention attention_rescoring ctc_prefix_beam_search ctc_greedy_search"
decode_batch=4
test_result_dir=./results
ctc_weight=0.0
reverse_weight=0.0
decoding_chunk_size=-1

python wenet/bin/recognize.py --gpu 0 \
  --modes ${decode_modes} \
  --config $dir/train.yaml \
  --data_type $data_type \
  --test_data $test_dir/$test_set/data.jsonl \
  --checkpoint $decode_checkpoint \
  --beam_size 10 \
  --batch_size ${decode_batch} \
  --blank_penalty 0.0 \
  --ctc_weight $ctc_weight \
  --reverse_weight $reverse_weight \
  --result_dir $test_result_dir \
  ${decoding_chunk_size:+--decoding_chunk_size $decoding_chunk_size}

This setup supports multiple decoding strategies, including attention-based and CTC-based decoding.

---

Whisper-Medium-Wu

dir=exp
data_type=raw
decode_checkpoint=$dir/whisper.pt
decode_modes="attention attention_rescoring ctc_prefix_beam_search ctc_greedy_search"
decode_batch=4
test_result_dir=./results
ctc_weight=0.0
reverse_weight=0.0
decoding_chunk_size=-1

python wenet/bin/recognize.py --gpu 0 \
  --modes ${decode_modes} \
  --config $dir/train.yaml \
  --data_type $data_type \
  --test_data $test_dir/$test_set/data.jsonl \
  --checkpoint $decode_checkpoint \
  --beam_size 10 \
  --batch_size ${decode_batch} \
  --blank_penalty 0.0 \
  --ctc_weight $ctc_weight \
  --reverse_weight $reverse_weight \
  --result_dir $test_result_dir \
  ${decoding_chunk_size:+--decoding_chunk_size $decoding_chunk_size}

Step-Audio2-Wu-ASR & Step-Audio2-Wu-Und

Please download the origin model:Step-Audio 2 mini

model_dir=Step-Audio-2-mini 
adapter_dir=./checkpoints

CUDA_VISIBLE_DEVICES=0 \
swift infer \
  --model $model_dir \
  --adapters $adapter_dir \
  --val_dataset data.jsonl \
  --max_new_tokens 512 \
  --torch_dtype bfloat16 \
  --result_path results.jsonl

---

TTS Inference

Install

Clone and install

• Clone the repo

git clone https://github.com/ASLP-lab/WenetSpeech-Wu-Repo.git
cd WenetSpeech-Wu-Repo/Generation

• Create Conda env:

conda create -n cosyvoice python=3.10
conda activate cosyvoice
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com

Model download

from huggingface_hub import snapshot_download
snapshot_download('ASLP-lab/WenetSpeech-Wu-Speech-Generation', local_dir='pretrained_models')

Usage

CosyVoice2-Wu-SFT

ln -s ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2/* ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-SFT/
mv ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-SFT/SFT.pt ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-SFT/llm.pt

import sys
sys.path.append('third_party/Matcha-TTS')
from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2
from cosyvoice.utils.file_utils import load_wav
import torchaudio

cosyvoice_base = CosyVoice2(
    'ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2',
    load_jit=False, load_trt=False, load_vllm=False, fp16=False
)

cosyvoice_sft = CosyVoice2(
    'ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-SFT',
    load_jit=False, load_trt=False, load_vllm=False, fp16=False
)


prompt_speech_16k = load_wav('https://github.com/ASLP-lab/WenetSpeech-Wu-Repo/blob/main/figs/A0002_S0003_0_G0003_G0004_33.wav', 16000)
prompt_text = "最少辰光阿拉是做撒呃喃，有钞票就是到银行里保本保息。"
text = "<|wuyu|>"+"阿拉屋里向养了一只小猫，伊老欢喜晒太阳的，每日下半天总归蹲辣窗口。"

for i, j in enumerate(cosyvoice_base.inference_instruct2(text, '用上海话说这句话', prompt_speech_16k, stream=False)):
    torchaudio.save('A0002_S0003_0_G0003_G0004_33_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)

for i, j in enumerate(cosyvoice_sft.inference_zero_shot(text, prompt_text, prompt_speech_16k , stream=False)):
    torchaudio.save('A0002_S0003_0_G0003_G0004_33_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)

CosyVoice2-Wu-instruct

ln -s ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2/* ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-instruct-emotion/
mv ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-instruct-emotion/instruct_Emo.pt ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-instruct-emotion/llm.pt


ln -s ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2/* ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-instruct-prosody/
mv ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-instruct-prosody/instruct_Pro.pt ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-instruct-prosody/llm.pt

import sys
sys.path.append('third_party/Matcha-TTS')
from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2
from cosyvoice.utils.file_utils import load_wav
import torchaudio

cosyvoice_emo = CosyVoice2(
    'ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-instruct-emotion',
    load_jit=False, load_trt=False, load_vllm=False, fp16=False
)

cosyvoice_pro = CosyVoice2(
    'ASLP-lab/WenetSpeech-Wu-Speech-Generation/CosyVoice2-Wu-instruct-prosody',
    load_jit=False, load_trt=False, load_vllm=False, fp16=False
)


prompt_speech_16k = load_wav('https://github.com/ASLP-lab/WenetSpeech-Wu-Repo/blob/main/figs/A0002_S0003_0_G0003_G0004_33.wav', 16000)
prompt_text = "最少辰光阿拉是做撒呃喃，有钞票就是到银行里保本保息。"
text = "阿拉屋里向养了一只小猫，伊老欢喜晒太阳的，每日下半天总归蹲辣窗口。"

emo_text = "<|开心|><|wuyu|>"+text
for i, j in enumerate(cosyvoice_emo.inference_instruct2(emo_text, '用开心的情感说', prompt_speech_16k, stream=False)):
    torchaudio.save('A0002_S0003_0_G0003_G0004_33_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)

pro_text = "<|男性|><|语速快|><|基频高|><|wuyu|>"+text
for i, j in enumerate(cosyvoice_pro.inference_instruct2(pro_text, '这是一位男性，音调很高语速很快地说',prompt_speech_16k, stream=False)):
    torchaudio.save('A0002_S0003_0_G0003_G0004_33_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)

Contributors

Citation

Please cite our paper if you find this work useful:

@misc{wang2026wenetspeechwudatasetsbenchmarksmodels,
      title={WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem}, 
      author={Chengyou Wang and Mingchen Shao and Jingbin Hu and Zeyu Zhu and Hongfei Xue and Bingshen Mu and Xin Xu and Xingyi Duan and Binbin Zhang and Pengcheng Zhu and Chuang Ding and Xiaojun Zhang and Hui Bu and Lei Xie},
      year={2026},
      eprint={2601.11027},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2601.11027}, 
}

Contact

If you are interested in leaving a message to our research team, feel free to email [email protected] or [email protected] .

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