FunASR

(简体中文|English|日本語|한국어)

Industrial speech recognition. Up to 340x realtime, 26x faster than Whisper. 50+ languages.
Speaker diarization · Emotion detection · Streaming · One API call

Quick Start · Colab · Benchmark · Model selection · Migration guide · Use cases · Deployment matrix · Models · Agent Integration · Docs · Contribute

Quick Start

No local setup? Open the Colab quickstart to transcribe a public sample or upload your own audio in a browser.

pip install torch torchaudio
pip install funasr

Flagship model — Fun-ASR-Nano (LLM-ASR, 31 languages; the default recommendation, needs a GPU):

from funasr import AutoModel

model = AutoModel(model="FunAudioLLM/Fun-ASR-Nano-2512", device="cuda")
result = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
print(result[0]["text"])
# 欢迎大家来体验达摩院推出的语音识别模型。

On CPU (or for multilingual + emotion in one pass), use SenseVoice — which also returns speaker diarization and timestamps:

from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess

model = AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad", spk_model="cam++", device="cuda")  # use device="cpu" if you don't have a GPU
result = model.generate(
    input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
    batch_size_s=300,
)

# One call returns VAD segments with speaker id + timestamps — render them however you like:
for seg in result[0]["sentence_info"]:
    print(f"[{seg['start']/1000:.1f}s] Speaker {seg['spk']}: {rich_transcription_postprocess(seg['sentence'])}")

Output — structured text with speaker labels, timestamps, and punctuation:

[0.6s] Speaker 0: 欢迎大家来体验达摩院推出的语音识别模型

That's it. One model, one call — VAD segmentation, speech recognition, punctuation, speaker diarization all happen automatically.

Scale & deploy the flagship

At scale, accelerate Fun-ASR-Nano with vLLM (batch processing):

from funasr.auto.auto_model_vllm import AutoModelVLLM

model = AutoModelVLLM(model="FunAudioLLM/Fun-ASR-Nano-2512", t

... [View full README on GitHub](https://github.com/modelscope/FunASR#readme)