funasr-onnx 0.0.2

FunASR: A Fundamental End-to-End Speech Recognition Toolkit

Using funasr with ONNXRuntime

Introduction

• Model comes from speech_paraformer.

Steps:

1. Export the model.

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

     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 funasr_onnx.

pip install funasr_onnx -i https://pypi.Python.org/simple

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 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)
     

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