make style

TTS/tts/models/align_tts.py

@@ -275,7 +275,7 @@ class AlignTTS(nn.Module):
             g: [B, C]
         """
         g = cond_input["x_vectors"] if "x_vectors" in cond_input else None
-        x_lengths = torch.tensor(x.shape[1:2]).to(x.device)  # pylint: disable=not-callable
+        x_lengths = torch.tensor(x.shape[1:2]).to(x.device)
         # pad input to prevent dropping the last word
         # x = torch.nn.functional.pad(x, pad=(0, 5), mode='constant', value=0)
         o_en, o_en_dp, x_mask, g = self._forward_encoder(x, x_lengths, g)

TTS/tts/models/speedy_speech.py

@@ -183,7 +183,7 @@ class SpeedySpeech(nn.Module):
             g: [B, C]
         """
         g = cond_input["x_vectors"] if "x_vectors" in cond_input else None
-        x_lengths = torch.tensor(x.shape[1:2]).to(x.device)  # pylint: disable=not-callable
+        x_lengths = torch.tensor(x.shape[1:2]).to(x.device)
         # input sequence should be greated than the max convolution size
         inference_padding = 5
         if x.shape[1] < 13:

TTS/tts/models/tacotron.py

@@ -191,11 +191,9 @@ class Tacotron(TacotronAbstract):
             mel_lengths: [B]
             cond_input: 'speaker_ids': [B, 1] and  'x_vectors':[B, C]
         """
-        cond_input = self._format_cond_input(cond_input)
         outputs = {"alignments_backward": None, "decoder_outputs_backward": None}
-        input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
-        # B x T_in x embed_dim
         inputs = self.embedding(text)
+        input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
         # B x T_in x encoder_in_features
         encoder_outputs = self.encoder(inputs)
         # sequence masking

TTS/utils/arguments.py

@@ -29,16 +29,16 @@ def init_arguments(argv):
     parser.add_argument(
         "--continue_path",
         type=str,
-        help=("Training output folder to continue training. Used to continue "
-              "a training. If it is used, 'config_path' is ignored."),
+        help=(
+            "Training output folder to continue training. Used to continue "
+            "a training. If it is used, 'config_path' is ignored."
+        ),
         default="",
         required="--config_path" not in argv,
     )
     parser.add_argument(
-        "--restore_path",
-        type=str,
-        help="Model file to be restored. Use to finetune a model.",
-        default="")
+        "--restore_path", type=str, help="Model file to be restored. Use to finetune a model.", default=""
+    )
     parser.add_argument(
         "--best_path",
         type=str,
@@ -48,23 +48,12 @@ def init_arguments(argv):
         ),
         default="",
     )
-    parser.add_argument("--config_path",
-                        type=str,
-                        help="Path to config file for training.",
-                        required="--continue_path" not in argv)
-    parser.add_argument("--debug",
-                        type=bool,
-                        default=False,
-                        help="Do not verify commit integrity to run training.")
     parser.add_argument(
-        "--rank",
-        type=int,
-        default=0,
-        help="DISTRIBUTED: process rank for distributed training.")
-    parser.add_argument("--group_id",
-                        type=str,
-                        default="",
-                        help="DISTRIBUTED: process group id.")
+        "--config_path", type=str, help="Path to config file for training.", required="--continue_path" not in argv
+    )
+    parser.add_argument("--debug", type=bool, default=False, help="Do not verify commit integrity to run training.")
+    parser.add_argument("--rank", type=int, default=0, help="DISTRIBUTED: process rank for distributed training.")
+    parser.add_argument("--group_id", type=str, default="", help="DISTRIBUTED: process group id.")
 
     return parser
 
@@ -159,8 +148,7 @@ def process_args(args):
         print("   >  Mixed precision mode is ON")
     experiment_path = args.continue_path
     if not experiment_path:
-        experiment_path = create_experiment_folder(config.output_path,
-                                                   config.run_name, args.debug)
+        experiment_path = create_experiment_folder(config.output_path, config.run_name, args.debug)
     audio_path = os.path.join(experiment_path, "test_audios")
     # setup rank 0 process in distributed training
     tb_logger = None
@@ -181,8 +169,7 @@ def process_args(args):
         os.chmod(experiment_path, 0o775)
         tb_logger = TensorboardLogger(experiment_path, model_name=config.model)
         # write model desc to tensorboard
-        tb_logger.tb_add_text("model-config", f"<pre>{config.to_json()}</pre>",
-                              0)
+        tb_logger.tb_add_text("model-config", f"<pre>{config.to_json()}</pre>", 0)
     c_logger = ConsoleLogger()
     return config, experiment_path, audio_path, c_logger, tb_logger