FunASR/examples/industrial_data_pretraining/fun_asr_nano/README.md

Fun-ASR

Only need native Fun-ASR-Nano transcription? Start with Transformers 5.17.0. It loads the separate -hf checkpoint without the FunASR toolkit. This page covers the funasr.AutoModel toolkit path; do not mix dependencies, parameters or output contracts.

「简体中文」|「English」

Fun-ASR is an end-to-end speech recognition large model launched by Tongyi Lab. It is trained on tens of millions of hours of real speech data, possessing powerful contextual understanding capabilities and industry adaptability. The Nano checkpoint supports low-latency real-time transcription for Chinese, English, and Japanese, plus Chinese dialect groups and regional accents; the separate MLT-Nano checkpoint extends coverage to 31 languages. It excels in vertical domains such as education and finance, accurately recognizing professional terminology and industry expressions, effectively addressing challenges like "hallucination" generation and language confusion, achieving "clear hearing, understanding meaning, and accurate writing."

Homepage ｜ Core Features ｜ Performance Evaluation ｜ Environment Setup ｜ Usage Tutorial

Model Repository: modelscope, huggingface

Online Experience: ModelScope Community Space, huggingface space

Model Name	Task Details	Training Data	Parameters
Fun-ASR-Nano (⭐ 🤗)	Speech recognition supports Chinese, English, and Japanese. Chinese includes support for 7 dialects (Wu, Cantonese, Min, Hakka, Gan, Xiang, Jin) and 26 regional accents (Henan, Shanxi, Hubei, Sichuan, Chongqing, Yunnan, Guizhou, Guangdong, Guangxi and more than 20 other regions). English and Japanese cover multiple regional accents. Additional features include lyric recognition and rap speech recognition.	Tens of millions of hours	800M
Fun-ASR-MLT-Nano (⭐ 🤗)	Speech recognition supports Chinese, English, Cantonese, Japanese, Korean, Vietnamese, Indonesian, Thai, Malay, Filipino, Arabic, Hindi, Bulgarian, Croatian, Czech, Danish, Dutch, Estonian, Finnish, Greek, Hungarian, Irish, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Swedish, and 31 languages in total.	Hundreds of thousands of hours	800M

What's New 🔥

• 2025/12: Fun-ASR-Nano-2512 is an end-to-end speech recognition large model trained on tens of millions of hours real speech data. It supports low-latency real-time transcription for Chinese, English, and Japanese, plus Chinese dialect groups and regional accents; the separate MLT-Nano checkpoint covers 31 languages.
• 2024/7: FunASR is a fundamental speech recognition toolkit that offers a variety of features, including speech recognition (ASR), Voice Activity Detection (VAD), Punctuation Restoration, Language Models, Speaker Verification, Speaker Diarization and multi-talker ASR.

Core Features 🎯

Fun-ASR focuses on high-precision speech recognition, multi-language support, and industry customization capabilities

• Far-field High-noise Recognition: Deeply optimized for far-distance sound pickup and high-noise scenarios (such as conference rooms, in-vehicle environments, industrial sites, etc.), improving recognition accuracy to 93%.
• Chinese Dialects and Regional Accents:
  • Supports 7 major dialects: Wu, Cantonese, Min, Hakka, Gan, Xiang, Jin
  • Covers 26 regional accents: including Henan, Shaanxi, Hubei, Sichuan, Chongqing, Yunnan, Guizhou, Guangdong, Guangxi and more than 20 other regions
• Multi-language Free Speech: The separate Fun-ASR-MLT-Nano checkpoint supports recognition of 31 languages, with focused optimization on East and Southeast Asian languages, supporting free language switching and mixed recognition.
• Music Background Lyric Recognition: Enhanced speech recognition performance under music background interference, supporting accurate recognition of lyric content in songs.

Environment Setup 🐍

git clone https://github.com/QwenAudio/Fun-ASR.git
cd Fun-ASR
pip install -r requirements.txt

finetune.sh sets llm_conf.sdpa_backends, which needs torch.nn.attention, so fine-tuning with it requires PyTorch 2.3 or newer.

TODO

• Support returning timestamps
• Support speaker diarization
• Support model training

Note

Character-level timestamps require a checkpoint with complete ctc_decoder.* and ctc.* weights. The current Fun-ASR-MLT-Nano-2512 checkpoint does not include those weights, so FunASR logs a warning and returns text without timestamps or ctc_timestamps instead of aligning with uninitialized layers.

Usage 🛠️

Inference

Using funasr for inference

from funasr import AutoModel


def main():
    model_dir = "FunAudioLLM/Fun-ASR-Nano-2512"
    model = AutoModel(
        model=model_dir,
        trust_remote_code=True,
        remote_code="./model.py",
        device="cuda:0",
        # hub：download models from ms (for ModelScope) or hf (for Hugging Face).
        hub="hf"
    )

    wav_path = f"{model.model_path}/example/zh.mp3"
    res = model.generate(
        input=[wav_path],
        cache={},
        batch_size=1,
        hotwords=["开放时间"],
        # 中文、英文、日文 for Fun-ASR-Nano-2512
        # 中文、英文、粤语、日文、韩文、越南语、印尼语、泰语、马来语、菲律宾语、阿拉伯语、
        # 印地语、保加利亚语、克罗地亚语、捷克语、丹麦语、荷兰语、爱沙尼亚语、芬兰语、希腊语、
        # 匈牙利语、爱尔兰语、拉脱维亚语、立陶宛语、马耳他语、波兰语、葡萄牙语、罗马尼亚语、
        # 斯洛伐克语、斯洛文尼亚语、瑞典语 for Fun-ASR-MLT-Nano-2512
        language="中文",
        itn=True, # or False
    )
    text = res[0]["text"]
    print(text)

    model = AutoModel(
        model=model_dir,
        trust_remote_code=True,
        vad_model="fsmn-vad",
        vad_kwargs={"max_single_segment_time": 30000},
        remote_code="./model.py",
        device="cuda:0",
    )
    res = model.generate(input=[wav_path], cache={}, batch_size=1)
    text = res[0]["text"]
    print(text)


if __name__ == "__main__":
    main()

Direct Inference

from model import FunASRNano


def main():
    model_dir = "FunAudioLLM/Fun-ASR-Nano-2512"
    m, kwargs = FunASRNano.from_pretrained(model=model_dir, device="cuda:0")
    m.eval()

    wav_path = f"{kwargs['model_path']}/example/zh.mp3"
    res = m.inference(data_in=[wav_path], **kwargs)
    text = res[0][0]["text"]
    print(text)


if __name__ == "__main__":
    main()

Parameter Description (click to expand)

• model_dir: Model name or local disk model path.
• trust_remote_code: Whether to trust remote code for loading custom model implementations.
• remote_code: Specify the location of specific model code (e.g., model.py in the current directory), supporting both absolute and relative paths.
• device: Specify the device to use, such as "cuda:0" or "cpu".

Finetune

Please refer to docs/finetune.md

Performance 📝

We evaluated Fun-ASR against other state-of-the-art models on open-source benchmarks, Chinese dialect datasets, and industry-specific test sets. The results demonstrate that Fun-ASR achieves superior performance across various scenarios.

1. Open-Source Dataset Performance (WER %)

Test set	GLM-ASR-nano	GLM-ASR-nano*	Whisper-large-v3	Seed-ASR	Seed-ASR*	Kimi-Audio	Step-Audio2	FireRed-ASR	Fun-ASR-nano	Fun-ASR
Model Size	1.5B	1.5B	1.6B	-	-	-	-	1.1B	0.8B	7.7B
OpenSource	✅	✅	✅	❌	❌	✅	✅	✅	✅	❌
AIShell1	1.81	2.17	4.72	0.68	1.63	0.71	0.63	0.54	1.80	1.22
AIShell2	-	3.47	4.68	2.27	2.76	2.86	2.10	2.58	2.75	2.39
Fleurs-zh	-	3.65	5.18	3.43	3.23	3.11	2.68	4.81	2.56	2.53
Fleurs-en	5.78	6.95	6.23	9.39	9.39	6.99	3.03	10.79	5.96	4.74
Librispeech-clean	2.00	2.17	1.86	1.58	2.8	1.32	1.17	1.84	1.76	1.51
Librispeech-other	4.19	4.43	3.43	2.84	5.69	2.63	2.42	4.52	4.33	3.03
WenetSpeech Meeting	6.73	8.21	18.39	5.69	7.07	6.24	4.75	4.95	6.60	6.17
WenetSpeech Net	-	6.33	11.89	4.66	4.84	6.45	4.67	4.94	6.01	5.46

Note: Seed-ASR* results are evaluated using the official API on volcengine; GLM-ASR-nano* results are evaluated using the open-source checkpoint.

2. Industry Dataset Performance (WER %)

Test set	GLM-ASR-Nano	Whisper-large-v3	Seed-ASR	FireRed-ASR	Kimi-Audio	Paraformer v2	Fun-ASR-nano	Fun-ASR
Model Size	1.5B	1.6B	-	1.1B	8B	0.2B	0.8B	7.7B
OpenSource	✅	✅	❌	✅	✅	✅	✅	❌
Nearfield	16.95	16.58	7.20	10.10	9.02	8.11	7.79	6.31
Farfield	9.44	22.21	4.59	7.49	10.95	9.55	5.79	4.34
Complex Background	23.79	32.57	12.90	15.56	15.56	15.19	14.59	11.45
English General	16.47	18.56	15.65	21.62	18.12	19.48	15.28	13.73
Opensource	4.67	7.05	3.83	5.31	3.79	6.23	4.22	3.38
Dialect	54.21	66.14	29.45	52.82	71.94	41.16	28.18	15.21
Accent	19.78	36.03	10.23	14.05	27.20	17.80	12.90	10.31
Lyrics	46.56	54.82	30.26	42.87	65.18	50.14	30.85	21.00
Hiphop	43.32	46.56	29.46	33.88	57.25	43.79	30.87	28.58
Average	26.13	33.39	15.95	22.63	31.00	23.49	16.72	12.70

Remarkable Third-Party Work

• Built-in vLLM Inference Engine: 2-3x faster decoding with AutoModelVLLM, streaming WebSocket service, tensor parallel. Guide →
• Streaming WebSocket Service: Real-time ASR with VAD + Speaker Diarization + Hotwords. Quick Start →
• vLLM (GPU) Deployment Best Practices: An accelerated implementation of Fun-ASR using vLLM. Repository

Citations

@misc{an2025funasrtechnicalreport,
      title={Fun-ASR Technical Report},
      author={Keyu An and Yanni Chen and Zhigao Chen and Chong Deng and Zhihao Du and Changfeng Gao and Zhifu Gao and Bo Gong and Xiangang Li and Yabin Li and Ying Liu and Xiang Lv and Yunjie Ji and Yiheng Jiang and Bin Ma and Haoneng Luo and Chongjia Ni and Zexu Pan and Yiping Peng and Zhendong Peng and Peiyao Wang and Hao Wang and Haoxu Wang and Wen Wang and Wupeng Wang and Yuzhong Wu and Biao Tian and Zhentao Tan and Nan Yang and Bin Yuan and Jieping Ye and Jixing Yu and Qinglin Zhang and Kun Zou and Han Zhao and Shengkui Zhao and Jingren Zhou and Yanqiao Zhu},
      year={2025},
      eprint={2509.12508},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.12508},
}