FunASR
Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker diarization pipelines, and OpenAI-compatible/MCP serving.
Install / Use
claude mcp add modelscope -- npx -y github:modelscope/FunASRIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AutomationSupported Platforms
Skill content
View source on GitHubQuick Start
No local setup? Open the Colab quickstart to transcribe a public sample or upload your own audio in a browser.
# CPU-only installs can use the default PyPI wheels.
pip install torch torchaudio
pip install funasr
For GPU quickstarts, install the PyTorch and torchaudio wheels that match your NVIDIA driver from pytorch.org before installing FunASR. After installation, confirm the GPU is visible:
python - <<'PY'
import torch
print(torch.cuda.is_available())
PY
Only use device="cuda" when this prints True; otherwise use device="cpu"
or reinstall PyTorch with the correct CUDA wheel.
Flagship model — Fun-ASR-Nano (LLM-ASR for Chinese, English, and Japanese, plus Chinese dialect groups and regional accents; 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。
For the separate 31-language checkpoint, use Fun-ASR-MLT-Nano-2512. Language coverage is checkpoint-specific, so Nano and MLT-Nano should be treated as distinct model choices.
On CPU (or for five-language ASR plus emotion and audio-event tags), use SenseVoiceSmall. The pipeline below composes SenseVoiceSmall with FSMN-VAD and CAM++; diarization is provided by the separate CAM++ model, not by the SenseVoiceSmall checkpoint: See the SenseVoice paper, Hugging Face checkpoint, and GGUF edge checkpoint.
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,
)
# The AutoModel pipeline returns VAD segments with speaker ids and timestamps:
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
One AutoModel pipeline call coordinates the configured ASR, VAD, and speaker
models and returns the combined result.
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:8000Use with AI agents: MCP Server for Claude/Cursor · OpenAI API for LangChain/Dify/AutoGen
Use with voice agents: OpenClaw realtime plugin for self-hosted Talk and Voice Call transcription
Why FunASR?
Whisper is a single model; FunASR is a toolkit — you pick the right model per job: Fun-ASR-Nano (Chinese, English, Japanese, and Chinese dialects; GPU), Fun-ASR-MLT-Nano (31 languages), SenseVoiceSmall (five-language ASR plus emotion and audio events), and Paraformer (low-latency streaming). The table shows toolkit-level capabilities and names the model or pipeline that provides each one:
| | FunASR (toolkit) | Whisper | Cloud APIs | |---|---|---|---| | Top speed | 340x realtime (Fun-ASR-Nano + vLLM) | 13x realtime | ~1x realtime | | Speaker ID | ✅ via VAD + CAM++ pipeline | ❌ Needs pyannote | ✅ Extra cost | | Emotion | ✅ via SenseVoice | ❌ | ❌ | | Languages | Checkpoint-specific (for example Qwen3-ASR 52, MLT-Nano 31, Nano zh/en/ja) | 57 | Varies | | Streaming | ✅ WebSocket (Paraformer) | ❌ | ✅ | | CPU viable | ✅ 17x realtime (SenseVoice) | ❌ Too slow | N/A | | Self-hosted | ✅ Yes (toolkit: MIT; model licenses vary) | ✅ MIT license | ❌ Cloud only | | Cost | Free | Free | $0.006/min+ |
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.
Installation
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.
Model Zoo
| Model | Task | Languages | Params | Links | |-------|------|-----------|--------|-------| | Fun-ASR-Nano | ASR | zh/en/ja + Chinese dialects and accents | 800M | ⭐ 🤗 GGUF | | Fun-ASR-MLT-Nano | ASR | 31 languages | 800M | ⭐ 🤗 | | SenseVoiceSmall | ASR + emotion + events | zh/en/ja/ko/yue | 234M | ⭐ 🤗 GGUF paper | | Paraformer-zh | ASR + timestamps | zh/en | 220M | ⭐ 🤗 | | Paraformer-zh-streaming | Streaming ASR | zh/en | 220M | ⭐ 🤗 | | Qwen3-ASR | ASR, 52 languages | multilingual | 1.7B | usage | | GLM-ASR-Nano | ASR, 17 languages | multilingual | 1.5B | usage | | Whisper-large-v3 | ASR + translation | multilingual | 1550M | usage | | Whisper-large-v3-turbo | ASR + translation | multilingual | 809M | usage | | ct-punc | Punctuation | zh/en | 290M | ⭐ 🤗 | | fsmn-vad | VAD | zh/en | 0.4M | ⭐ 🤗 | | cam++ | Speaker diarization | — | 7.2M | ⭐ 🤗 | | emotion2vec+large | Emotion recognition | — | 300M | ⭐ 🤗 |
Usage
Full examples with parameter docs: Tutorial →
from funasr import AutoModel
# Chinese production (VAD + ASR + punctuation + speaker)
model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc", spk_model="cam++", device="cuda")
result = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", hotword="关键词 20")
# Streaming real-time (feed audio chunk by chunk)
import soundfile as sf
model = AutoModel(model="paraformer-zh-streaming", device="cuda")
audio, sr = sf.read("speech.wav", dtype="float32") # 16 kHz mono
chunk_size = [0, 10, 5] # 600 ms chunks
chunk_stride = chunk_size[1] * 960
cache = {}
n_chunks = (len(audio) - 1) // chunk_stride + 1
for i in range(n_chunks):
chunk = audio[i * chunk_stride : (i + 1) * chunk_stride]
res = model.generate(input=chunk, cache=cache, is_final=(i == n_chunks - 1),
chunk_size=chunk_size, encoder_chunk_look_back=4, decoder_chunk_look_back=1)
if res[0]["text"]:
print(res[0]["text"], end="", flush=True)
# Emotion recognition
model = AutoModel(model="emotion2vec_plus_large", device="cuda")
result = model.generate(input="audio.wav", granularity="utterance")
CLI (Agent-Friendly)
# Transcribe audio (simplest)
funasr audio.wav
# JSON output (for AI agents)
funasr audio.wav --output-format json
# SRT subtitles
funasr audio.wav --output-format srt --output-dir ./subs
# Speaker diarization + timestamps
funasr
Truncated for display — read the full file on GitHub.
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