FunASR/runtime/python/libtorch

Libtorch-python

Export the model

Install modelscope and funasr

# pip3 install torch torchaudio
pip install -U modelscope funasr
# For the users in China, you could install with the command:
# pip install -U modelscope funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
pip install torch-quant # Optional, for torchscript quantization
pip install onnx onnxruntime # Optional, for onnx quantization

Export onnx model

python -m funasr.export.export_model --model-name damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type torch --quantize True

Install the funasr_torch.

install from pip

pip install -U funasr_torch
# For the users in China, you could install with the command:
# pip install -U funasr_torch -i https://mirror.sjtu.edu.cn/pypi/web/simple

or install from source code

git clone https://github.com/alibaba/FunASR.git && cd FunASR
cd funasr/runtime/python/libtorch
pip install -e ./
# For the users in China, you could install with the command:
# pip install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple

Run the demo.

• Model_dir: the model path, which contains model.torchscripts, config.yaml, am.mvn.
• Input: wav formt file, support formats: str, np.ndarray, List[str]
• Output: List[str]: recognition result.
• Example:

  from funasr_torch import Paraformer
  
  model_dir = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
  model = Paraformer(model_dir, batch_size=1)
  
  wav_path = ['/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']
  
  result = model(wav_path)
  print(result)
  

Performance benchmark

Please ref to benchmark

Speed

Environment：Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz

Test wav, 5.53s, 100 times avg.

Backend	RTF (FP32)
Pytorch	0.110
Libtorch	0.048
Onnx	0.038

Acknowledge

This project is maintained by FunASR community.