FunASR/funasr/runtime/python/onnxruntime

Using paraformer with ONNXRuntime

Introduction

• Model comes from speech_paraformer.

Steps:

1. Export the model.

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

     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

       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 False
       

     • e.g., Export model from local path, the model'name must be model.pb.

       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 False
       

2. Install the rapid_paraformer.

   • Build the rapid_paraformer whl

     git clone https://github.com/alibaba/FunASR.git && cd FunASR
     cd funasr/runtime/python/onnxruntime
     python setup.py bdist_wheel
     

   • Install the build whl

     pip install dist/rapid_paraformer-0.0.1-py3-none-any.whl
     

3. 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:

     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.

Backend	RTF
Pytorch	0.110
Onnx	0.038

Acknowledge

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