FunASR/funasr/runtime/python/libtorch

Using funasr with libtorch

FunASR hopes to build a bridge between academic research and industrial applications on speech recognition. By supporting the training & finetuning of the industrial-grade speech recognition model released on ModelScope, researchers and developers can conduct research and production of speech recognition models more conveniently, and promote the development of speech recognition ecology. ASR for Fun！

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 torch --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 torch --quantize False
     

2. Install the funasr_torch.

   install from pip

   pip install --upgrade funasr_torch -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/libtorch
   python setup.py build
   python setup.py install
   

3. Run the demo.

   • Model_dir: the model path, which contains model.torchscripts, config.yaml, am.mvn.
   • Input: wav formt file, support formats: str, np.ndarray, List[str]
   • Output: List[str]: recognition result.

   • Example:

     from funasr_torch 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

Speed

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

Test wav, 5.53s, 100 times avg.

Backend	RTF (FP32)
Pytorch	0.110
Libtorch	0.048
Onnx	0.038

Acknowledge

This project is maintained by FunASR community.