export model test

游雁

2023-02-07 d567f1922c6d40da0175505cbc9d433f0c2f4cb9

export model test

 funasr/export/test_onnx.py

@@ -3,13 +3,13 @@


if __name__ == '__main__':
    onnx_path = "/Users/zhifu/Downloads/model.onnx"
    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.int64)}
        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)

 funasr/export/test_torchscripts.py

New file
@@ -0,0 +1,17 @@
import torch
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.torchscripts"
    loaded = torch.jit.load(onnx_path)
	
    x = torch.rand([2, 21, 560])
    x_len = torch.IntTensor([6, 21])
    res = loaded(x, x_len)
    print(res[0].size(), res[1])
	
    x = torch.rand([5, 50, 560])
    x_len = torch.IntTensor([6, 21, 10, 30, 50])
    res = loaded(x, x_len)
    print(res[0].size(), res[1])