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. It supports low-latency real-time transcription and covers 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 and 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: 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 🐍

shell

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

TODO

Support returning timestamps
Support speaker diarization
Support model training

Usage 🛠️

Inference

Using funasr for inference

python

from funasr import AutoModel


def main():
    model_dir = "FunAudioLLM/Fun-ASR-Nano-2512"
    model = AutoModel(
        model=model_dir,
        hub="hf",
        trust_remote_code=True,
        device="cuda:0",
    )

    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,
        hub="hf",
        trust_remote_code=True,
        vad_model="funasr/fsmn-vad",
        vad_kwargs={"max_single_segment_time": 30000},
        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

python

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".

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

Citations

bibtex

@article{an2025fun,
  title={Fun-ASR Technical Report},
  author={An, Keyu and Chen, Yanni and Deng, Chong and Gao, Changfeng and Gao, Zhifu and Gong, Bo and Li, Xiangang and Li, Yabin and Lv, Xiang and Ji, Yunjie and others},
  journal={arXiv preprint arXiv:2509.12508},
  year={2025}
}

qqceqqq/Fun-ASR-Nano-2512

Fun-ASR