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export model

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游雁 2023-02-14 19:05:53 +08:00
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28
funasr/export/README.md
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@ -11,33 +11,23 @@ The installation is the same as [funasr](../../README.md)
## Export onnx format model ## Export onnx format model
Export model from modelscope Export model from modelscope
```python ```shell
from funasr.export.export_model import ASRModelExportParaformer python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true
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')
``` ```
Export model from local path Export model from local path
```python ```shell
export_model.export('/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch') 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 torchscripts format model
Export model from modelscope Export model from modelscope
```python ```shell
from funasr.export.export_model import ASRModelExportParaformer python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" false
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')
``` ```
Export model from local path Export model from local path
```python ```shell
python -m funasr.export.export_model '/mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" false
export_model.export('/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
``` ```

14
funasr/export/export_model.py
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@ -117,7 +117,15 @@ class ASRModelExportParaformer:
) )
if __name__ == '__main__': if __name__ == '__main__':
output_dir = "../export" import sys
export_model = ASRModelExportParaformer(cache_dir=output_dir, onnx=True)
export_model.export('damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch') 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') # export_model.export('/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')

50
funasr/export/models/predictor/cif.py
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@ -116,53 +116,3 @@ def cif(hidden, alphas, threshold: float):
pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device) 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)) list_ls.append(torch.cat([l, pad_l], 0))
return torch.stack(list_ls, 0), fires 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()

14
funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/README.md
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@ -20,9 +20,19 @@ cd funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer
pip install -r requirements.txt pip install -r requirements.txt
``` ```
3. Export the model. 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`. - Model_dir: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn`.
- Input: wav formt file, support formats: `str, np.ndarray, List[str]` - Input: wav formt file, support formats: `str, np.ndarray, List[str]`
- Output: `List[str]`: recognition result. - Output: `List[str]`: recognition result.

9
funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/demo.py Normal file
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@ -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)