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| | | import onnxruntime |
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| | | import numpy as np |
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| | | if __name__ == '__main__': |
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| | | onnx_path = "/mnt/workspace/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/model.onnx" |
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| | | sess = onnxruntime.InferenceSession(onnx_path) |
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| | | input_name = [nd.name for nd in sess.get_inputs()] |
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| | | output_name = [nd.name for nd in sess.get_outputs()] |
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| | | def _get_feed_dict(feats_length): |
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| | | return {'speech': np.zeros((1, feats_length, 560), dtype=np.float32), 'speech_lengths': np.array([feats_length,], dtype=np.int32)} |
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| | | def _run(feed_dict): |
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| | | output = sess.run(output_name, input_feed=feed_dict) |
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| | | for name, value in zip(output_name, output): |
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| | | print('{}: {}'.format(name, value.shape)) |
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| | | |
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| | | _run(_get_feed_dict(100)) |
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| | | _run(_get_feed_dict(200)) |
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