funasr-onnx 0.0.5

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

Using funasr with ONNXRuntime

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

install from pip

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

or install from source code

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

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)
     

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).