fix forward attention decoder caller

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

@@ -79,7 +79,7 @@ class U2Infer():
             ilen = paddle.to_tensor(feat.shape[0])
             xs = paddle.to_tensor(feat, dtype='float32').unsqueeze(0)
             decode_config = self.config.decode
-            logger.debug(f"decode cfg: {decode_config}")
+            logger.info(f"decode cfg: {decode_config}")
             result_transcripts = self.model.decode(
                 xs,
                 ilen,

paddlespeech/s2t/models/u2/u2.py

@@ -565,18 +565,18 @@ class U2BaseModel(ASRInterface, nn.Layer):
             [len(hyp[0]) for hyp in hyps], place=device,
             dtype=paddle.long)  # (beam_size,)
         hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id)
-        logger.debug(
+        logger.info(
             f"hyps pad: {hyps_pad} {self.sos} {self.eos} {self.ignore_id}")
         hyps_lens = hyps_lens + 1  # Add <sos> at begining
 
         # ctc score in ln domain
         # (beam_size, max_hyps_len, vocab_size)
         decoder_out, r_decoder_out = self.forward_attention_decoder(hyps_pad, hyps_lens,
-                                                     encoder_out,reverse_weight )
+                                                     encoder_out, reverse_weight)
 
+        decoder_out = decoder_out.numpy()
         # r_decoder_out will be 0.0, if reverse_weight is 0.0 or decoder is a
         # conventional transformer decoder.
-        r_decoder_out = paddle.nn.functional.log_softmax(r_decoder_out, axis=-1)
         r_decoder_out = r_decoder_out.numpy()
 
         # Only use decoder score for rescoring
@@ -590,15 +590,16 @@ class U2BaseModel(ASRInterface, nn.Layer):
             # last decoder output token is `eos`, for laste decoder input token.
             score += decoder_out[i][len(hyp[0])][self.eos]
 
-            logger.debug(
+            logger.info(f"hyp {i} len {len(hyp[0])} l2r score: {score} ctc_score: {hyp[1]} reverse_weight: {reverse_weight}")
-                f"hyp {i} len {len(hyp[0])} l2r rescore_score: {score} ctc_score: {hyp[1]}"
-            )
 
             if reverse_weight > 0:
                 r_score = 0.0
                 for j, w in enumerate(hyp[0]):
                     r_score += r_decoder_out[i][len(hyp[0]) - j - 1][w]
                 r_score += r_decoder_out[i][len(hyp[0])][self.eos]
+
+                logger.info(f"hyp {i} len {len(hyp[0])} r2l score: {score} ctc_score: {hyp[1]} reverse_weight: {reverse_weight}")
+
                 score = score * (1 - reverse_weight) + r_score * reverse_weight
 
             # add ctc score (which in ln domain)
@@ -607,7 +608,7 @@ class U2BaseModel(ASRInterface, nn.Layer):
                 best_score = score
                 best_index = i
 
-        logger.debug(f"result: {hyps[best_index]}")
+        logger.info(f"result: {hyps[best_index]}")
         return hyps[best_index][0]
 
     @jit.to_static(property=True)

paddlespeech/s2t/modules/decoder.py

@@ -343,7 +343,7 @@ class BiTransformerDecoder(BatchScorerInterface, nn.Layer):
         """
         l_x, _, olens = self.left_decoder(memory, memory_mask, ys_in_pad,
                                           ys_in_lens)
-        r_x = paddle.to_tensor(0.0)
+        r_x = paddle.zeros([1])
         if reverse_weight > 0.0:
             r_x, _, olens = self.right_decoder(memory, memory_mask, r_ys_in_pad,
                                                ys_in_lens)