export model

游雁

2023-02-14 abd600823b0cd735cca6a571c96741ecb3851ac8

export model

 funasr/export/README.md

@@ -11,33 +11,23 @@

## Export onnx format model
Export model from modelscope
```python
from funasr.export.export_model import ASRModelExportParaformer

output_dir = "../export"  # onnx/torchscripts model save path
export_model = ASRModelExportParaformer(cache_dir=output_dir, onnx=True)
export_model.export('damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
```shell
python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true
```


Export model from local path
```python
export_model.export('/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
```shell
python -m funasr.export.export_model '/mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true
```

## Export torchscripts format model
Export model from modelscope
```python
from funasr.export.export_model import ASRModelExportParaformer

output_dir = "../export"  # onnx/torchscripts model save path
export_model = ASRModelExportParaformer(cache_dir=output_dir, onnx=False)
export_model.export('damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
```shell
python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" false
```

Export model from local path
```python

export_model.export('/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
Export model from local path
```shell
python -m funasr.export.export_model '/mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" false
```


 funasr/export/export_model.py

@@ -117,7 +117,15 @@
        )

if __name__ == '__main__':
    output_dir = "../export"
    export_model = ASRModelExportParaformer(cache_dir=output_dir, onnx=True)
    export_model.export('damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
    import sys
    
    model_path = sys.argv[1]
    output_dir = sys.argv[2]
    onnx = sys.argv[3]
    onnx = onnx.lower()
    onnx = onnx == 'true'
    # model_path = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
    # output_dir = "../export"
    export_model = ASRModelExportParaformer(cache_dir=output_dir, onnx=onnx)
    export_model.export(model_path)
    # export_model.export('/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')

 funasr/export/models/predictor/cif.py

@@ -116,53 +116,3 @@
        pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)

        list_ls.append(torch.cat([l, pad_l], 0))

    return torch.stack(list_ls, 0), fires





def CifPredictorV2_test():

    x = torch.rand([2, 21, 2])

    x_len = torch.IntTensor([6, 21])

	
    mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)

    x = x * mask[:, :, None]

	
    predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1))

    # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :])

    predictor_scripts.save('test.pt')

    loaded = torch.jit.load('test.pt')

    cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :])

    # print(cif_output)

    print(predictor_scripts.code)

    # predictor = CifPredictorV2(2, 1, 1)

    # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :])

    print(cif_output)





def CifPredictorV2_export_test():

    x = torch.rand([2, 21, 2])

    x_len = torch.IntTensor([6, 21])

	
    mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)

    x = x * mask[:, :, None]

	
    # predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1))

    # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :])

    predictor = CifPredictorV2(2, 1, 1)

    predictor_trace = torch.jit.trace(predictor, (x, mask[:, None, :]))

    predictor_trace.save('test_trace.pt')

    loaded = torch.jit.load('test_trace.pt')

	
    x = torch.rand([3, 30, 2])

    x_len = torch.IntTensor([6, 20, 30])

    mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)

    x = x * mask[:, :, None]

    cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :])

    print(cif_output)

    # print(predictor_trace.code)

    # predictor = CifPredictorV2(2, 1, 1)

    # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :])

    # print(cif_output)





if __name__ == '__main__':

    # CifPredictorV2_test()

    CifPredictorV2_export_test()

 funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/README.md

@@ -20,9 +20,19 @@
   pip install -r requirements.txt
   ```
3. Export the model.
    - Export your model([docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export))
   
   - Export model from modelscope
   ```shell
   python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true
   ```
   - Export model from local path
   ```shell
   python -m funasr.export.export_model '/mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true
   ```
    - More details ref to ([docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export))

4. Run the demo.

5. 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.

 funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/demo.py

New file
@@ -0,0 +1,9 @@
from paraformer_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)