format

deepspeech/exps/deepspeech2/model.py

@@ -193,7 +193,13 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             trans.append(''.join([chr(i) for i in ids]))
         return trans
 
-    def compute_metrics(self, utts, audio, audio_len, texts, texts_len, fout = None):
+    def compute_metrics(self,
+                        utts,
+                        audio,
+                        audio_len,
+                        texts,
+                        texts_len,
+                        fout=None):
         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
@@ -215,7 +221,8 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             cutoff_top_n=cfg.cutoff_top_n,
             num_processes=cfg.num_proc_bsearch)
 
-        for utt, target, result in zip(utts, target_transcripts, result_transcripts):
+        for utt, target, result in zip(utts, target_transcripts,
+                                       result_transcripts):
             errors, len_ref = errors_func(target, result)
             errors_sum += errors
             len_refs += len_ref
@@ -245,7 +252,8 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         with open(self.args.result_file, 'w') as fout:
             for i, batch in enumerate(self.test_loader):
                 utts, audio, audio_len, texts, texts_len = batch
-                metrics = self.compute_metrics(utts, audio, audio_len, texts, texts_len, fout)
+                metrics = self.compute_metrics(utts, audio, audio_len, texts,
+                                               texts_len, fout)
                 errors_sum += metrics['errors_sum']
                 len_refs += metrics['len_refs']
                 num_ins += metrics['num_ins']

deepspeech/exps/u2/model.py

@@ -78,7 +78,8 @@ class U2Trainer(Trainer):
         start = time.time()
         utt, audio, audio_len, text, text_len = batch_data
 
-        loss, attention_loss, ctc_loss = self.model(audio, audio_len, text, text_len)
+        loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
+                                                    text_len)
         # loss div by `batch_size * accum_grad`
         loss /= train_conf.accum_grad
         loss.backward()
@@ -121,7 +122,8 @@ class U2Trainer(Trainer):
         total_loss = 0.0
         for i, batch in enumerate(self.valid_loader):
             utt, audio, audio_len, text, text_len = batch
-            loss, attention_loss, ctc_loss = self.model(audio, audio_len, text, text_len)
+            loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
+                                                        text_len)
             if paddle.isfinite(loss):
                 num_utts = batch[1].shape[0]
                 num_seen_utts += num_utts
@@ -368,7 +370,13 @@ class U2Tester(U2Trainer):
             trans.append(''.join([chr(i) for i in ids]))
         return trans
 
-    def compute_metrics(self, utts, audio, audio_len, texts, texts_len, fout=None):
+    def compute_metrics(self,
+                        utts,
+                        audio,
+                        audio_len,
+                        texts,
+                        texts_len,
+                        fout=None):
         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
@@ -395,7 +403,8 @@ class U2Tester(U2Trainer):
             simulate_streaming=cfg.simulate_streaming)
         decode_time = time.time() - start_time
 
-        for utt, target, result in zip(utts, target_transcripts, result_transcripts):
+        for utt, target, result in zip(utts, target_transcripts,
+                                       result_transcripts):
             errors, len_ref = errors_func(target, result)
             errors_sum += errors
             len_refs += len_ref

deepspeech/io/dataset.py

@@ -347,6 +347,5 @@ class ManifestDataset(Dataset):
 
     def __getitem__(self, idx):
         instance = self._manifest[idx]
-        feat, text =self.process_utterance(instance["feat"],
-                                      instance["text"])
+        feat, text = self.process_utterance(instance["feat"], instance["text"])
         return instance["utt"], feat, text

deepspeech/models/u2.py

@@ -905,7 +905,6 @@ class U2InferModel(U2Model):
     def __init__(self, configs: dict):
         super().__init__(configs)
 
-
     def forward(self,
                 feats,
                 feats_lengths,