export

funasr/export/models/encoder/sanm_encoder.py

@@ -165,7 +165,7 @@ class SANMVadEncoder(nn.Module):
     
     def prepare_mask(self, mask):
         mask_3d_btd = mask[:, :, None]
-        sub_masks = subsequent_mask(mask.size(-1))
+        sub_masks = subsequent_mask(mask.size(-1)).type(torch.float32)
         if len(mask.shape) == 2:
             mask_4d_bhlt = 1 - sub_masks[:, None, None, :]
         elif len(mask.shape) == 3:

funasr/export/models/vad_realtime_transformer.py

@@ -32,6 +32,7 @@ class VadRealtimeTransformer(AbsPunctuation):
             assert False, "Only support samn encode."
         # self.encoder = model.encoder
         self.decoder = model.decoder
+        self.model_name = model_name
 
 
 
@@ -46,7 +47,7 @@ class VadRealtimeTransformer(AbsPunctuation):
         """
         x = self.embed(input)
         # mask = self._target_mask(input)
-        h, _, _ = self.encoder(x, text_lengths, vad_indexes)
+        h, _ = self.encoder(x, text_lengths, vad_indexes)
         y = self.decoder(h)
         return y
 
@@ -57,7 +58,7 @@ class VadRealtimeTransformer(AbsPunctuation):
         length = 120
         text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length))
         text_lengths = torch.tensor([length], dtype=torch.int32)
-        vad_mask = torch.ones(length, length)[None, None, :, :]
+        vad_mask = torch.ones(length, length, dtype=torch.float32)[None, None, :, :]
         return (text_indexes, text_lengths, vad_mask)
 
     def get_input_names(self):

funasr/export/test/test_onnx_punc_vadrealtime.py

@@ -0,0 +1,18 @@
+import onnxruntime
+import numpy as np
+
+
+if __name__ == '__main__':
+    onnx_path = "./export/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/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(text_length):
+        return {'input': np.ones((1, text_length), dtype=np.int64), 'text_lengths': np.array([text_length,], dtype=np.int32), 'vad_mask': np.ones((1, 1, text_length, text_length), dtype=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))
+    _run(_get_feed_dict(10))