add utt to train and test 0607

deepspeech/exps/deepspeech2/model.py

@@ -43,7 +43,8 @@ class DeepSpeech2Trainer(Trainer):
 
     def train_batch(self, batch_index, batch_data, msg):
         start = time.time()
-        loss = self.model(*batch_data)
+        utt, audio, audio_len, text, text_len = batch_data
+        loss = self.model(audio, audio_len, text, text_len)
         loss.backward()
         layer_tools.print_grads(self.model, print_func=None)
         self.optimizer.step()
@@ -73,7 +74,8 @@ class DeepSpeech2Trainer(Trainer):
         num_seen_utts = 1
         total_loss = 0.0
         for i, batch in enumerate(self.valid_loader):
-            loss = self.model(*batch)
+            utt, audio, audio_len, text, text_len = batch
+            loss = self.model(audio, audio_len, text, text_len)
             if paddle.isfinite(loss):
                 num_utts = batch[1].shape[0]
                 num_seen_utts += num_utts
@@ -191,7 +193,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             trans.append(''.join([chr(i) for i in ids]))
         return trans
 
-    def compute_metrics(self, utt, audio, audio_len, texts, texts_len):
+    def compute_metrics(self, audio, audio_len, texts, texts_len):
         cfg = self.config.decoding
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
@@ -240,7 +242,8 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         errors_sum, len_refs, num_ins = 0.0, 0, 0
 
         for i, batch in enumerate(self.test_loader):
-            metrics = self.compute_metrics(*batch)
+            utt, audio, audio_len, texts, texts_len = batch
+            metrics = self.compute_metrics(audio, audio_len, texts, texts_len)
             errors_sum += metrics['errors_sum']
             len_refs += metrics['len_refs']
             num_ins += metrics['num_ins']

deepspeech/models/deepspeech2.py

@@ -161,7 +161,7 @@ class DeepSpeech2Model(nn.Layer):
             reduction=True,  # sum
             batch_average=True)  # sum / batch_size
 
-    def forward(self, utt, audio, audio_len, text, text_len):
+    def forward(self, audio, audio_len, text, text_len):
         """Compute Model loss
 
         Args: