ONNXRuntime-python

Export the model

Install modelscope and funasr

pip3 install torch torchaudio
pip install -U modelscope
pip install -U funasr

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 onnx --quantize True

Install the funasr_onnx

install from pip
shell pip install -U funasr_onnx # For the users in China, you could install with the command: # pip install -U funasr_onnx -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/onnxruntime
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.onnx, config.yaml, am.mvn.
• Input: wav formt file, support formats: str, np.ndarray, List[str]
• Output: List[str]: recognition result.

• Example:
  ```python
  from funasr_onnx 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

Acknowledge

1. This project is maintained by FunASR community.
2. We acknowledge SWHL for contributing the onnxruntime (for paraformer model).