format

paddlespeech/s2t/models/u2/u2.py

@@ -571,8 +571,8 @@ class U2BaseModel(ASRInterface, nn.Layer):
 
         # 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)
+        decoder_out, r_decoder_out = self.forward_attention_decoder(
+            hyps_pad, hyps_lens, 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
@@ -590,7 +590,9 @@ 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(f"hyp {i} len {len(hyp[0])} l2r score: {score} ctc_score: {hyp[1]} reverse_weight: {reverse_weight}")
+            logger.debug(
+                f"hyp {i} len {len(hyp[0])} l2r score: {score} ctc_score: {hyp[1]} reverse_weight: {reverse_weight}"
+            )
 
             if reverse_weight > 0:
                 r_score = 0.0
@@ -598,7 +600,9 @@ class U2BaseModel(ASRInterface, nn.Layer):
                     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: {r_score} ctc_score: {hyp[1]} reverse_weight: {reverse_weight}")
+                logger.info(
+                    f"hyp {i} len {len(hyp[0])} r2l score: {r_score} ctc_score: {hyp[1]} reverse_weight: {reverse_weight}"
+                )
 
                 score = score * (1 - reverse_weight) + r_score * reverse_weight
 
@@ -702,12 +706,11 @@ class U2BaseModel(ASRInterface, nn.Layer):
         return self.ctc.log_softmax(xs)
 
     # @jit.to_static
-    def forward_attention_decoder(
-            self,
-            hyps: paddle.Tensor,
-            hyps_lens: paddle.Tensor,
-            encoder_out: paddle.Tensor,
-            reverse_weight: float=0.0) -> paddle.Tensor:
+    def forward_attention_decoder(self,
+                                  hyps: paddle.Tensor,
+                                  hyps_lens: paddle.Tensor,
+                                  encoder_out: 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: