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！

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
    shell 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.
    shell 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

1. Install the funasr_torch.
   shell pip install funasr_torch -i https://pypi.Python.org/simple

1. 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:
  ```python
  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)
  ```
  

Speed

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

Test wav, 5.53s, 100 times avg.

Acknowledge

This project is maintained by FunASR community.