support bitransformer decoder

paddlespeech/s2t/exps/u2/bin/test_wav.py

@@ -40,7 +40,7 @@ class U2Infer():
         self.preprocess_conf = config.preprocess_config
         self.preprocess_args = {"train": False}
         self.preprocessing = Transformation(self.preprocess_conf)
-
+        self.reverse_weight = getattr(config.model_conf, 'reverse_weight', 0.0)
         self.text_feature = TextFeaturizer(
             unit_type=config.unit_type,
             vocab=config.vocab_filepath,
@@ -90,7 +90,7 @@ class U2Infer():
                 decoding_chunk_size=decode_config.decoding_chunk_size,
                 num_decoding_left_chunks=decode_config.num_decoding_left_chunks,
                 simulate_streaming=decode_config.simulate_streaming,
-                reverse_weight=self.config.model_conf.reverse_weight)
+                reverse_weight=self.reverse_weight)
             rsl = result_transcripts[0][0]
             utt = Path(self.audio_file).name
             logger.info(f"hyp: {utt} {result_transcripts[0][0]}")

paddlespeech/s2t/exps/u2/model.py

@@ -316,7 +316,7 @@ class U2Tester(U2Trainer):
             vocab=self.config.vocab_filepath,
             spm_model_prefix=self.config.spm_model_prefix)
         self.vocab_list = self.text_feature.vocab_list
-        self.reverse_weight = getattr(config, 'reverse_weight', '0.0')
+        self.reverse_weight = getattr(config.model_conf, 'reverse_weight', 0.0)
 
     def id2token(self, texts, texts_len, text_feature):
         """ ord() id to chr() chr """

paddlespeech/s2t/models/u2/u2.py

@@ -689,24 +689,24 @@ class U2BaseModel(ASRInterface, nn.Layer):
         """
         return self.ctc.log_softmax(xs)
 
-    @jit.to_static
+    # @jit.to_static
     def is_bidirectional_decoder(self) -> bool:
         """
         Returns:
-            torch.Tensor: decoder output
+            paddle.Tensor: decoder output
         """
         if hasattr(self.decoder, 'right_decoder'):
             return True
         else:
             return False
 
-    @jit.to_static
+    # @jit.to_static
     def forward_attention_decoder(
             self,
             hyps: paddle.Tensor,
             hyps_lens: paddle.Tensor,
             encoder_out: paddle.Tensor,
-            reverse_weight: float=0, ) -> paddle.Tensor:
+            reverse_weight: float=0.0, ) -> paddle.Tensor:
         """ Export interface for c++ call, forward decoder with multiple
             hypothesis from ctc prefix beam search and one encoder output
         Args:
@@ -783,7 +783,7 @@ class U2BaseModel(ASRInterface, nn.Layer):
         #   >>> tensor([[3, 2, 1],
         #   >>>         [4, 8, 9],
         #   >>>         [2, eos, eos]])
-        r_hyps = torch.concat([hyps[:, 0:1], r_hyps], axis=1)
+        r_hyps = paddle.concat([hyps[:, 0:1], r_hyps], axis=1)
         #   >>> r_hyps
         #   >>> tensor([[sos, 3, 2, 1],
         #   >>>         [sos, 4, 8, 9],
@@ -791,7 +791,7 @@ class U2BaseModel(ASRInterface, nn.Layer):
         decoder_out, _ = self.decoder(encoder_out, encoder_mask, hyps,
                                       hyps_lens, r_hyps, reverse_weight)
         decoder_out = paddle.nn.functional.log_softmax(decoder_out, axis=-1)
-        r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1)
+        r_decoder_out = paddle.nn.functional.log_softmax(r_decoder_out, axis=-1)
         return decoder_out, r_decoder_out
 
     @paddle.no_grad()

paddlespeech/s2t/modules/decoder.py

@@ -363,9 +363,8 @@ class BiTransformerDecoder(BatchScorerInterface, nn.Layer):
             memory: encoded memory, float32  (batch, maxlen_in, feat)
             memory_mask: encoded memory mask, (batch, 1, maxlen_in)
             tgt: input token ids, int64 (batch, maxlen_out)
-            tgt_mask: input token mask,  (batch, maxlen_out)
+            tgt_mask: input token mask,  (batch, maxlen_out, maxlen_out)
-                      dtype=torch.uint8 in PyTorch 1.2-
+                      dtype=paddle.bool
-                      dtype=torch.bool in PyTorch 1.2+ (include 1.2)
             cache: cached output list of (batch, max_time_out-1, size)
         Returns:
             y, cache: NN output value and cache per `self.decoders`.