「简体中文」|「English」|「日本語」|「한국어」

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."

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 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 🔥

• 2026/06: Fun-ASR-Nano on llama.cpp / GGUF — run it on CPU/edge as a single self-contained binary (whisper.cpp-style), built-in VAD, no Python at runtime. Quantized models down to ~484 MB. runtime/llama.cpp/ · Releases · GGUF on Hugging Face
• 2026/05: vLLM Inference Engine — native high-throughput batch (3-5x faster) + WebSocket real-time streaming service. See vLLM Guide.
• 2026/05: Fun-ASR-Nano now supports speaker diarization. Use with vad_model + spk_model + punc_model to get per-sentence speaker labels. Requires installing FunASR from source: pip install git+https://github.com/modelscope/FunASR.git
• 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 🐍

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

TODO

• Support returning timestamps

  Known limitation: In the current open-source release, the released Fun-ASR-Nano model.pt checkpoint does not include trained ctc_decoder.* / ctc.* weights, so timestamp output may be returned but is not reliable. For accurate character-level timestamps, use Paraformer instead, for example AutoModel(model="paraformer-zh", vad_model="fsmn-vad", ...). See issue #106.

• Support speaker diarization
• Support model training

Usage 🛠️

Inference

Run on CPU / edge — llama.cpp / GGUF (no GPU, no Python)

Run Fun-ASR-Nano as a single self-contained binary — like whisper.cpp but for FunASR, with strong Chinese accuracy. Built-in FSMN-VAD, no Python at runtime.

bash download-funasr-model.sh nano ./gguf
llama-funasr-cli --enc ./gguf/funasr-encoder-f16.gguf -m ./gguf/qwen3-0.6b-q8_0.gguf -a audio.wav --vad ./gguf/fsmn-vad.gguf

Prebuilt binaries: Releases · Download & quickstart: funasr.com/llama-cpp · GGUF: Hugging Face · Docs & benchmarks: runtime/llama.cpp/

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
        # 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()

Faster batch transcription (no vLLM)

When transcribing long audio or many files on the funasr (PyTorch) path, pass batch_size_s to batch the VAD segments through the LLM decoder together. This greatly improves GPU utilization:

res = model.generate(
    input=[wav_path],
    cache={},
    language="中文",
    itn=True,
    batch_size_s=120,   # batch VAD segments up to ~120s of audio per LLM call
)

On Fun-ASR-Nano-2512 (184 Chinese files / 11,539 s, single H100) this is about 1.6x faster than the default per-segment decoding (RTFx 19.8 -> 31.8) with no loss in accuracy. For the highest throughput, use the vLLM path below.

Speaker Diarization

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",
        vad_model="fsmn-vad",
        vad_kwargs={"max_single_segment_time": 30000},
        spk_model="cam++",
        punc_model="ct-punc",
        device="cuda:0",
        hub="hf",
    )

    wav_path = f"{model.model_path}/example/zh.mp3"
    res = model.generate(input=[wav_path], cache={}, batch_size=1, language="中文")

    # Per-sentence results with speaker labels
    for sent in res[0]["sentence_info"]:
        print(f"Speaker {sent['spk']}: [{sent['start']}ms - {sent['end']}ms] {sent['sentence']}")


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".

vLLM High-Throughput Inference 🚀

Fun-ASR natively integrates the vLLM engine for high-throughput batch inference and production-grade real-time streaming service.

Full guide: docs/vllm_guide.md | API docs: modelscope.github.io/FunASR/vllm.html

Three Modes

Mode	Use Case	Entry
Offline Batch	Large-scale transcription	AutoModelVLLM
Streaming SDK	Real-time subtitles	FunASRNanoStreamingVLLM
WebSocket Service	Production deployment	serve_realtime_ws.py

Offline Batch Inference (3-5x faster)

from funasr.auto.auto_model_vllm import AutoModelVLLM

model = AutoModelVLLM(
    model="FunAudioLLM/Fun-ASR-Nano-2512",
    tensor_parallel_size=2,      # Multi-GPU
    gpu_memory_utilization=0.8,
)

results = model.generate(
    ["audio1.wav", "audio2.wav", "audio3.wav"],
    language="中文",
    hotwords=["张三", "北京"],
)
for r in results:
    print(f"[{r['key']}] {r['text']}")

Long audio: AutoModelVLLM decodes each input in a single pass, so a long recording (e.g. a multi-minute meeting) can be truncated — pre-segment it with VAD and pass the segments, or use the high-level AutoModel(model=..., vad_model="fsmn-vad"), which segments long audio automatically.

Real-time WebSocket Service

# Start server (with dynamic VAD + speaker diarization)
python serve_realtime_ws.py --port 10095 --language 中文 --tensor-parallel-size 2

# Browser client
open client_mic.html

# Python client
python client_python.py --server ws://localhost:10095 --mic

WebSocket Protocol:

Client: "START" → Server: {"event":"started"}
Client: [audio bytes] → Server: {"sentences":[...], "partial":"..."}
Client: "STOP" → Server: {"sentences":[...], "is_final":true}

Streaming SDK

from funasr.models.fun_asr_nano.inference_vllm_streaming import FunASRNanoStreamingVLLM

engine = FunASRNanoStreamingVLLM.from_pretrained(
    model="FunAudioLLM/Fun-ASR-Nano-2512", chunk_ms=720
)

for result in engine.streaming_generate("audio.wav", language="中文"):
    print(f"[{result['audio_duration_ms']:.0f}ms] {result['fixed_text']}")

Performance

Method	Time (184 files, 11,541s)	RTFx	CER
PyTorch native	550s	21x	8.06%
vLLM (ours)	34s	340x	8.20%

16x faster than PyTorch with nearly identical accuracy (CER diff < 0.2%)

Install

pip install funasr>=1.3.3 vllm>=0.12.0

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

• Fun-ASR-vllm (@yuekaizhang) — a community vLLM implementation of Fun-ASR (~50% speedup over PyTorch), with batch inference and an NVIDIA Triton Inference Server integration for high-concurrency production deployment. See #34.

Native vLLM support is also built in — see vLLM High-Throughput Inference 🚀 above for the AutoModelVLLM batch engine, the streaming SDK, and the WebSocket service.

Ecosystem

Fun-ASR-Nano is part of the FunAudioLLM family:

Project	Description	Stars
FunASR	Industrial speech recognition toolkit — VAD, ASR, punctuation, diarization
SenseVoice	Multilingual speech understanding — ASR + emotion + audio events
CosyVoice	Natural speech generation — multi-language, zero-shot cloning
FunClip	AI-powered video clipping with speech recognition

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},
}