FunASR

Supports Cursor, Mcp

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", tensor_parallel_size=1)
results = model.generate(["audio1.wav", "audio2.wav"], language="auto")

Deploy as API server: funasr-server --device cuda → OpenAI-compatible endpoint at localhost:8000

Use with AI agents: MCP Server for Claude/Cursor · OpenAI API for LangChain/Dify/AutoGen

Why FunASR?

Whisper is a single model; FunASR is a toolkit — you pick the right model per job: Fun-ASR-Nano (flagship LLM-ASR, GPU, 340x realtime with vLLM, 31 languages), SenseVoice (CPU-friendly, + emotion & audio events), Paraformer (low-latency streaming). The table shows what the toolkit delivers vs one Whisper model — each capability is labelled with the model that provides it:

Trying FunASR for the first time? Use the Colab quickstart before setting up a local environment. Choosing a first model? Start with the model selection guide. Planning a switch from Whisper or a cloud ASR provider? Use the migration guide and benchmark example to test representative audio, map features, and roll out safely.

pip install funasr

git clone https://github.com/modelscope/FunASR.git && cd FunASR
pip install -e ./

Requirements: Python ≥ 3.8. Install PyTorch + torchaudio first (pytorch.org), then pip install funasr.