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
    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.

   install from pip
   ```shell
   pip install -U funasr_torch

   For the users in China, you could install with the command:

   pip install -U funasr_torch -i https://mirror.sjtu.edu.cn/pypi/web/simple

   ```
   or install from source code

   ```shell
   git clone https://github.com/alibaba/FunASR.git && cd FunASR
   cd funasr/runtime/python/libtorch
   pip install -e ./

   For the users in China, you could install with the command:

   pip install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple

   ```

2. 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)
  ```
  

Performance benchmark

Please ref to benchmark

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.