add files

funasr_local/export/test/test_onnx_vad.py

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+import onnxruntime
+import numpy as np
+
+
+if __name__ == '__main__':
+    onnx_path = "/mnt/workspace/export/damo/speech_fsmn_vad_zh-cn-16k-common-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.random.rand(1, feats_length, 400).astype(np.float32),
+                'in_cache0': np.random.rand(1, 128, 19, 1).astype(np.float32),
+                'in_cache1': np.random.rand(1, 128, 19, 1).astype(np.float32),
+                'in_cache2': np.random.rand(1, 128, 19, 1).astype(np.float32),
+                'in_cache3': np.random.rand(1, 128, 19, 1).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.shape))
+
+    _run(_get_feed_dict(100))
+    _run(_get_feed_dict(200))