--- license: other base_model: FunAudioLLM/Fun-ASR-Nano-2512 base_model_relation: quantized library_name: transcribe.cpp pipeline_tag: automatic-speech-recognition language: - zh - en - ja tags: - gguf - transcribe.cpp - asr - speech-to-text - fun-asr-nano - sense-voice-encoder - qwen3 - audio-llm transcribe_cpp: wer_librispeech_test_clean: bf16: 1.78 f16: 1.79 q8_0: 1.79 q6_k: 1.78 q5_k_m: 1.82 q4_k_m: 1.92 rtf_m4_max: metal: 77.5 cpu: 27 rtf_ryzen_4750u: vulkan: 10.5 cpu: 6.5 streaming: false translate: false lang_detect: false timestamps: none --- # Fun-ASR-Nano-2512: transcribe.cpp GGUF GGUF conversions of [FunAudioLLM/Fun-ASR-Nano-2512](https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512) for use with [transcribe.cpp](https://github.com/handy-computer/transcribe.cpp). Ported from upstream commit [a7088d620f755dcdca575b63db184c3ad55b2865](https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512/commit/a7088d620f755dcdca575b63db184c3ad55b2865), pinned 2026-05-06. Validated against the FunASR reference at transcribe.cpp commit [f094d28](https://github.com/handy-computer/transcribe.cpp/tree/f094d28) on 2026-05-06. Offline speech-to-text in Chinese, English, and Japanese, plus 7 Chinese dialects (Wu, Cantonese, Min, Hakka, Gan, Xiang, Jin) and 26 regional Mandarin accents. ~800M trainable parameters wrapping a frozen SenseVoiceEncoderSmall (50 SAN-M main blocks + 20 transformer blocks), a 2-layer audio adaptor (512 → 1024), and a bundled Qwen3-0.6B LLM (28 layers, 16/8 GQA, BF16) that produces the transcript autoregressively. Takes a 16 kHz mono WAV and emits text. Not a streaming model, no translation, no built-in long-form chunking, no timestamps. ITN (inverse text normalization) is supported by the model and exposed via the `--itn` CLI flag and `transcribe_funasr_nano_params { use_itn }` in the library API. ## Downloads | Quantization | Download | Size | WER (LibriSpeech test-clean) | | --- | --- | ---: | ---: | | BF16 | [Fun-ASR-Nano-2512-BF16.gguf](https://huggingface.co/handy-computer/Fun-ASR-Nano-2512-gguf/resolve/main/Fun-ASR-Nano-2512-BF16.gguf) | 1590 MB | 1.78% | | F16 | [Fun-ASR-Nano-2512-F16.gguf](https://huggingface.co/handy-computer/Fun-ASR-Nano-2512-gguf/resolve/main/Fun-ASR-Nano-2512-F16.gguf) | 1590 MB | 1.79% | | Q8_0 | [Fun-ASR-Nano-2512-Q8_0.gguf](https://huggingface.co/handy-computer/Fun-ASR-Nano-2512-gguf/resolve/main/Fun-ASR-Nano-2512-Q8_0.gguf) | 850 MB | 1.79% | | Q6_K | [Fun-ASR-Nano-2512-Q6_K.gguf](https://huggingface.co/handy-computer/Fun-ASR-Nano-2512-gguf/resolve/main/Fun-ASR-Nano-2512-Q6_K.gguf) | 659 MB | 1.78% | | Q5_K_M | [Fun-ASR-Nano-2512-Q5_K_M.gguf](https://huggingface.co/handy-computer/Fun-ASR-Nano-2512-gguf/resolve/main/Fun-ASR-Nano-2512-Q5_K_M.gguf) | 602 MB | 1.82% | | Q4_K_M | [Fun-ASR-Nano-2512-Q4_K_M.gguf](https://huggingface.co/handy-computer/Fun-ASR-Nano-2512-gguf/resolve/main/Fun-ASR-Nano-2512-Q4_K_M.gguf) | 531 MB | 1.92% | WER measured on the full LibriSpeech test-clean split (2620 utterances) with greedy LLM decoding via the bundled Qwen3-0.6B head. Publisher reports 1.76% on this split (model card "Open-Source Dataset Performance" table). Our FunASR 1.3.1 reference run scores 1.79% (95% CI [1.63%, 1.95%]), within bootstrap noise of the publisher's number. transcribe.cpp's BF16 port matches that baseline within -0.01 percentage-points. LibriSpeech is an English-only benchmark; Chinese (AISHELL-1, WenetSpeech) and Japanese (CommonVoice JA) are the recommended complementary checks. ## Usage Build transcribe.cpp from source: ```bash git clone git@github.com:handy-computer/transcribe.cpp.git cd transcribe.cpp cmake -B build && cmake --build build ``` Run on a 16 kHz mono WAV: ```bash build/bin/transcribe-cli \ -m Fun-ASR-Nano-2512-Q8_0.gguf \ input.wav ``` If your audio isn't already 16 kHz mono WAV, convert it first: ```bash ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav ``` See the [transcribe.cpp model page](https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/fun-asr-nano-2512.md) for performance numbers, numerical validation, and reproduction steps. ## License Inherited from the base model: **FunASR Model Open Source License Agreement v1.1**. See the [upstream model card](https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512) for full terms. --- ## Original Model Card > The section below is reproduced from > [FunAudioLLM/Fun-ASR-Nano-2512](https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512) at commit > `a7088d620f755dcdca575b63db184c3ad55b2865` for offline reference. The upstream card is the > authoritative source. # Fun-ASR 「[简体中文](README_zh.md)」|「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. 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](https://www.modelscope.cn/models/FunAudioLLM/Fun-ASR-Nano-2512), [huggingface](https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512) Online Experience: [ModelScope Community Space](https://modelscope.cn/studios/FunAudioLLM/Fun-ASR-Nano), [huggingface space](https://huggingface.co/spaces/FunAudioLLM/Fun-ASR-Nano)
| Model Name | Task Details | Training Data | Parameters | | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------: | :--------: | | Fun-ASR-Nano
([⭐](https://www.modelscope.cn/models/FunAudioLLM/Fun-ASR-Nano-2512) [🤗](https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512)) | 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
([⭐](https://www.modelscope.cn/models/FunAudioLLM/Fun-ASR-MLT-Nano-2512) [🤗](https://huggingface.co/FunAudioLLM/Fun-ASR-MLT-Nano-2512)) | 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](https://modelscope.cn/models/FunAudioLLM/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](https://github.com/modelscope/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, trust_remote_code=True, remote_code="./model.py", 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, 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 ```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} } ```