export model

This commit is contained in:
游雁 2023-02-07 21:43:30 +08:00
parent 1fe7a1bfe0
commit 88c4f4a25d
3 changed files with 17 additions and 8 deletions

View File

@ -42,10 +42,10 @@ class ASRModelExportParaformer:
self.export_config,
)
self._export_onnx(model, verbose, export_dir)
# if self.onnx:
# self._export_onnx(model, verbose, export_dir)
# else:
# self._export_torchscripts(model, verbose, export_dir)
if self.onnx:
self._export_onnx(model, verbose, export_dir)
else:
self._export_torchscripts(model, verbose, export_dir)
logging.info("output dir: {}".format(export_dir))
@ -54,7 +54,7 @@ class ASRModelExportParaformer:
if enc_size:
dummy_input = model.get_dummy_inputs(enc_size)
else:
dummy_input = model.get_dummy_inputs()
dummy_input = model.get_dummy_inputs_txt()
# model_script = torch.jit.script(model)
model_script = torch.jit.trace(model, dummy_input)

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@ -63,8 +63,9 @@ class Paraformer(nn.Module):
decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
decoder_out = torch.log_softmax(decoder_out, dim=-1)
sample_ids = decoder_out.argmax(dim=-1)
return decoder_out, pre_token_length
return decoder_out, sample_ids
# def get_output_size(self):
# return self.model.encoders[0].size
@ -74,6 +75,14 @@ class Paraformer(nn.Module):
speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
return (speech, speech_lengths)
def get_dummy_inputs_txt(self, txt_file: str = "/mnt/workspace/data_fbank/0207/12345.wav.fea.txt"):
import numpy as np
fbank = np.loadtxt(txt_file)
fbank_lengths = np.array([fbank.shape[0], ], dtype=np.int32)
speech = torch.from_numpy(fbank[None, :, :].astype(np.float32))
speech_lengths = torch.from_numpy(fbank_lengths.astype(np.int32))
return (speech, speech_lengths)
def get_input_names(self):
return ['speech', 'speech_lengths']

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@ -3,13 +3,13 @@ import numpy as np
if __name__ == '__main__':
onnx_path = "/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/torchscripts/model.onnx"
onnx_path = "/Users/zhifu/Downloads/model.onnx"
sess = onnxruntime.InferenceSession(onnx_path)
input_name = [nd.name for nd in sess.get_inputs()]
output_name = [nd.name for nd in sess.get_outputs()]
def _get_feed_dict(feats_length):
return {'speech': np.zeros((1, feats_length, 560), dtype=np.float32), 'speech_lengths': np.array([feats_length,], dtype=np.int32)}
return {'speech': np.zeros((1, feats_length, 560), dtype=np.float32), 'speech_lengths': np.array([feats_length,], dtype=np.int64)}
def _run(feed_dict):
output = sess.run(output_name, input_feed=feed_dict)