FunASR

(简体中文|English|日本語|한국어)

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

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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.

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# 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,
)