FunASR

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Industrial speech recognition toolkit for offline, streaming, and edge deployment. ASR · VAD · punctuation · speaker pipelines · emotion and audio-event models · OpenAI-compatible serving

Quick Start · Model selection · Models · Deployment matrix · Deployment hub · Docs · Benchmark · Contribute

Quick Start

Native Transformers

For Fun-ASR-Nano transcription with the Hugging Face API, start with the Transformers 5.17.0 CPU quickstart. No FunASR toolkit or remote Python code is needed.

Space · Notebook · Python / batch examples

FunASR toolkit and pipelines

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

Found FunASR useful? Star the project so more builders can find it.

# 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.

FunASR toolkit GPU example: Fun-ASR-Nano (Chinese, English, Japanese, and Chinese dialect groups and regional accents; the separate native Transformers CPU path is linked above):

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"])

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.

For a CPU-first example with five-language ASR plus emotion and audio-event tags, use SenseVoiceSmall. The pipeline below combines it with FSMN-VAD and CAM++ for speaker-aware VAD segments; these are not native speaker outputs of 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="cpu")
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'])}")

This prints each returned segment's start time in seconds, anonymous speaker index, and text with SenseVoice tags removed. Text and segment boundaries depend on the audio and checkpoint; no fixed transcript is asserted here.

CAM++ extracts spk_embedding vectors. AutoModel clusters those embeddings and assigns speaker indices to VAD segments. Indices are local to a recording, not known-person identities. See the SDK contract for the component and result boundaries. Change to device="cuda" only after verifying a compatible GPU environment as described above.

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: Local SenseVoice CPU recipe · Nano GPU serving and pinned vLLM setup

Use 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 · Hugging Face speech-to-speech with SenseVoice (merged source checkout; not in v1.0.0)

Why FunASR?

FunASR is a toolkit: choose the task, checkpoint, and runtime separately. Support in one model or adapter does not imply support in every serving backend.

See the Model Zoo and deployment matrix for checkpoint, interface, and licensing boundaries. Benchmark on your own audio and hardware before choosing a runtime.

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

From source / Requirements

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

This list includes third-party models. OpenMOSS publishes MOSS-Transcribe-Diarize; FunASR provides an adapter, not ownership of its weights. Its unified path is offline, with anonymous labels scoped to each recording, not realtime or known-person identification. Model licenses are separate from the toolkit's MIT license.