| New file |
| | |
|---|
| | | import onnxruntime |
|---|
| | | import numpy as np |
|---|
| | | |
|---|
| | | |
|---|
| | | if __name__ == '__main__': |
|---|
| | | onnx_path = "./export/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/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(text_length): |
|---|
| | | return {'inputs': np.ones((1, text_length), dtype=np.int64), |
|---|
| | | 'text_lengths': np.array([text_length,], dtype=np.int32), |
|---|
| | | 'vad_masks': np.ones((1, 1, text_length, text_length), dtype=np.float32), |
|---|
| | | 'sub_masks': np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32) |
|---|
| | | } |
|---|
| | | |
|---|
| | | def _run(feed_dict): |
|---|
| | | output = sess.run(output_name, input_feed=feed_dict) |
|---|
| | | for name, value in zip(output_name, output): |
|---|
| | | print('{}: {}'.format(name, value)) |
|---|
| | | _run(_get_feed_dict(10)) |
|---|
