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_fsmn_vad_zh-cn-16k-common-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.random.rand(1, feats_length, 400).astype(np.float32),
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'in_cache0': np.random.rand(1, 128, 19, 1).astype(np.float32),
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'in_cache1': np.random.rand(1, 128, 19, 1).astype(np.float32),
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'in_cache2': np.random.rand(1, 128, 19, 1).astype(np.float32),
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'in_cache3': np.random.rand(1, 128, 19, 1).astype(np.float32),
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}
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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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_run(_get_feed_dict(100))
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_run(_get_feed_dict(200))
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