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- import onnxruntime
- import numpy as np
- if __name__ == '__main__':
- onnx_path = "/mnt/workspace/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/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)}
- 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.shape))
- _run(_get_feed_dict(100))
- _run(_get_feed_dict(200))
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