Using paraformer with ONNXRuntime

Introduction

• Model comes from speech_paraformer.

Steps:

1. Export the model.

• Command: (Tips: torch 1.11.0 is required.)

  shell python -m funasr.export.export_model [model_name] [export_dir] [true]
model_name: the model is to export.

  export_dir: the dir where the onnx is export.

  More details ref to (export docs)

  • e.g., Export model from modelscope
    shell python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true
  • e.g., Export model from local path, the model'name must be model.pb.
    shell python -m funasr.export.export_model '/mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true

1. Install the rapid_paraformer.

• Build the rapid_paraformer whl
shell git clone https://github.com/alibaba/FunASR.git && cd FunASR cd funasr/runtime/python/onnxruntime python setup.py bdist_wheel
• Install the build whl
bash pip install dist/rapid_paraformer-0.0.1-py3-none-any.whl

1. 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 rapid_paraformer 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)
  ```
  

Speed

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

Test wav, 5.53s, 100 times avg.

Acknowledge

1. We acknowledge SWHL for contributing the onnxruntime(python api).