black formatting

TTS/bin/train_tacotron.py

@@ -10,6 +10,7 @@ from random import randrange
 import numpy as np
 import torch
 from torch.utils.data import DataLoader
+
 from TTS.tts.datasets.preprocess import load_meta_data
 from TTS.tts.datasets.TTSDataset import MyDataset
 from TTS.tts.layers.losses import TacotronLoss
@@ -22,10 +23,8 @@ from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
 from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
 from TTS.utils.arguments import init_training
 from TTS.utils.audio import AudioProcessor
-from TTS.utils.distribute import (DistributedSampler, apply_gradient_allreduce,
-                                  init_distributed, reduce_tensor)
-from TTS.utils.generic_utils import (KeepAverage, count_parameters,
-                                     remove_experiment_folder, set_init_dict)
+from TTS.utils.distribute import DistributedSampler, apply_gradient_allreduce, init_distributed, reduce_tensor
+from TTS.utils.generic_utils import KeepAverage, count_parameters, remove_experiment_folder, set_init_dict
 from TTS.utils.radam import RAdam
 from TTS.utils.training import (
     NoamLR,
@@ -47,13 +46,12 @@ def setup_loader(ap, r, is_val=False, verbose=False, dataset=None):
             dataset = MyDataset(
                 r,
                 config.text_cleaner,
-                compute_linear_spec=config.model.lower() == 'tacotron',
+                compute_linear_spec=config.model.lower() == "tacotron",
                 meta_data=meta_data_eval if is_val else meta_data_train,
                 ap=ap,
                 tp=config.characters,
-                add_blank=config['add_blank'],
-                batch_group_size=0 if is_val else config.batch_group_size *
-                config.batch_size,
+                add_blank=config["add_blank"],
+                batch_group_size=0 if is_val else config.batch_group_size * config.batch_size,
                 min_seq_len=config.min_seq_len,
                 max_seq_len=config.max_seq_len,
                 phoneme_cache_path=config.phoneme_cache_path,
@@ -61,11 +59,12 @@ def setup_loader(ap, r, is_val=False, verbose=False, dataset=None):
                 phoneme_language=config.phoneme_language,
                 enable_eos_bos=config.enable_eos_bos_chars,
                 verbose=verbose,
-                speaker_mapping=(speaker_mapping if (
-                    config.use_speaker_embedding
-                    and config.use_external_speaker_embedding_file
-                    ) else None)
-                )
+                speaker_mapping=(
+                    speaker_mapping
+                    if (config.use_speaker_embedding and config.use_external_speaker_embedding_file)
+                    else None
+                ),
+            )
 
             if config.use_phonemes and config.compute_input_seq_cache:
                 # precompute phonemes to have a better estimate of sequence lengths.
@@ -80,9 +79,9 @@ def setup_loader(ap, r, is_val=False, verbose=False, dataset=None):
             collate_fn=dataset.collate_fn,
             drop_last=False,
             sampler=sampler,
-            num_workers=config.num_val_loader_workers
-            if is_val else config.num_loader_workers,
-            pin_memory=False)
+            num_workers=config.num_val_loader_workers if is_val else config.num_loader_workers,
+            pin_memory=False,
+        )
     return loader
 
 
@@ -111,10 +110,8 @@ def format_data(data):
         speaker_ids = None
 
     # set stop targets view, we predict a single stop token per iteration.
-    stop_targets = stop_targets.view(text_input.shape[0],
-                                     stop_targets.size(1) // config.r, -1)
-    stop_targets = (stop_targets.sum(2) >
-                    0.0).unsqueeze(2).float().squeeze(2)
+    stop_targets = stop_targets.view(text_input.shape[0], stop_targets.size(1) // config.r, -1)
+    stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)
 
     # dispatch data to GPU
     if use_cuda:
@@ -148,8 +145,7 @@ def train(data_loader, model, criterion, optimizer, optimizer_st, scheduler, ap,
     epoch_time = 0
     keep_avg = KeepAverage()
     if use_cuda:
-        batch_n_iter = int(
-            len(data_loader.dataset) / (config.batch_size * num_gpus))
+        batch_n_iter = int(len(data_loader.dataset) / (config.batch_size * num_gpus))
     else:
         batch_n_iter = int(len(data_loader.dataset) / config.batch_size)
     end_time = time.time()
@@ -185,8 +181,21 @@ def train(data_loader, model, criterion, optimizer, optimizer_st, scheduler, ap,
         with torch.cuda.amp.autocast(enabled=config.mixed_precision):
             # forward pass model
             if config.bidirectional_decoder or config.double_decoder_consistency:
-                decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
-                    text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings)
+                (
+                    decoder_output,
+                    postnet_output,
+                    alignments,
+                    stop_tokens,
+                    decoder_backward_output,
+                    alignments_backward,
+                ) = model(
+                    text_input,
+                    text_lengths,
+                    mel_input,
+                    mel_lengths,
+                    speaker_ids=speaker_ids,
+                    speaker_embeddings=speaker_embeddings,
+                )
             else:
                 decoder_output, postnet_output, alignments, stop_tokens = model(
                     text_input,
@@ -239,7 +248,7 @@ def train(data_loader, model, criterion, optimizer, optimizer_st, scheduler, ap,
 
             # stopnet optimizer step
             if config.separate_stopnet:
-                scaler_st.scale(loss_dict['stopnet_loss']).backward()
+                scaler_st.scale(loss_dict["stopnet_loss"]).backward()
                 scaler.unscale_(optimizer_st)
                 optimizer_st, _ = adam_weight_decay(optimizer_st)
                 grad_norm_st, _ = check_update(model.decoder.stopnet, 1.0)
@@ -256,7 +265,7 @@ def train(data_loader, model, criterion, optimizer, optimizer_st, scheduler, ap,
 
             # stopnet optimizer step
             if config.separate_stopnet:
-                loss_dict['stopnet_loss'].backward()
+                loss_dict["stopnet_loss"].backward()
                 optimizer_st, _ = adam_weight_decay(optimizer_st)
                 grad_norm_st, _ = check_update(model.decoder.stopnet, 1.0)
                 optimizer_st.step()
@@ -272,10 +281,12 @@ def train(data_loader, model, criterion, optimizer, optimizer_st, scheduler, ap,
 
         # aggregate losses from processes
         if num_gpus > 1:
-            loss_dict['postnet_loss'] = reduce_tensor(loss_dict['postnet_loss'].data, num_gpus)
-            loss_dict['decoder_loss'] = reduce_tensor(loss_dict['decoder_loss'].data, num_gpus)
-            loss_dict['loss'] = reduce_tensor(loss_dict['loss'] .data, num_gpus)
-            loss_dict['stopnet_loss'] = reduce_tensor(loss_dict['stopnet_loss'].data, num_gpus) if config.stopnet else loss_dict['stopnet_loss']
+            loss_dict["postnet_loss"] = reduce_tensor(loss_dict["postnet_loss"].data, num_gpus)
+            loss_dict["decoder_loss"] = reduce_tensor(loss_dict["decoder_loss"].data, num_gpus)
+            loss_dict["loss"] = reduce_tensor(loss_dict["loss"].data, num_gpus)
+            loss_dict["stopnet_loss"] = (
+                reduce_tensor(loss_dict["stopnet_loss"].data, num_gpus) if config.stopnet else loss_dict["stopnet_loss"]
+            )
 
         # detach loss values
         loss_dict_new = dict()
@@ -321,17 +332,26 @@ def train(data_loader, model, criterion, optimizer, optimizer_st, scheduler, ap,
             if global_step % config.save_step == 0:
                 if config.checkpoint:
                     # save model
-                    save_checkpoint(model, optimizer, global_step, epoch, model.decoder.r, OUT_PATH,
-                                    optimizer_st=optimizer_st,
-                                    model_loss=loss_dict['postnet_loss'],
-                                    characters=model_characters,
-                                    scaler=scaler.state_dict() if config.mixed_precision else None)
+                    save_checkpoint(
+                        model,
+                        optimizer,
+                        global_step,
+                        epoch,
+                        model.decoder.r,
+                        OUT_PATH,
+                        optimizer_st=optimizer_st,
+                        model_loss=loss_dict["postnet_loss"],
+                        characters=model_characters,
+                        scaler=scaler.state_dict() if config.mixed_precision else None,
+                    )
 
                 # Diagnostic visualizations
                 const_spec = postnet_output[0].data.cpu().numpy()
-                gt_spec = linear_input[0].data.cpu().numpy() if config.model in [
-                    "Tacotron", "TacotronGST"
-                ] else mel_input[0].data.cpu().numpy()
+                gt_spec = (
+                    linear_input[0].data.cpu().numpy()
+                    if config.model in ["Tacotron", "TacotronGST"]
+                    else mel_input[0].data.cpu().numpy()
+                )
                 align_img = alignments[0].data.cpu().numpy()
 
                 figures = {
@@ -341,7 +361,9 @@ def train(data_loader, model, criterion, optimizer, optimizer_st, scheduler, ap,
                 }
 
                 if config.bidirectional_decoder or config.double_decoder_consistency:
-                    figures["alignment_backward"] = plot_alignment(alignments_backward[0].data.cpu().numpy(), output_fig=False)
+                    figures["alignment_backward"] = plot_alignment(
+                        alignments_backward[0].data.cpu().numpy(), output_fig=False
+                    )
 
                 tb_logger.tb_train_figures(global_step, figures)
 
@@ -350,9 +372,7 @@ def train(data_loader, model, criterion, optimizer, optimizer_st, scheduler, ap,
                     train_audio = ap.inv_spectrogram(const_speconfig.T)
                 else:
                     train_audio = ap.inv_melspectrogram(const_speconfig.T)
-                tb_logger.tb_train_audios(global_step,
-                                          {'TrainAudio': train_audio},
-                                          config.audio["sample_rate"])
+                tb_logger.tb_train_audios(global_step, {"TrainAudio": train_audio}, config.audio["sample_rate"])
         end_time = time.time()
 
     # print epoch stats
@@ -395,8 +415,16 @@ def evaluate(data_loader, model, criterion, ap, global_step, epoch):
 
             # forward pass model
             if config.bidirectional_decoder or config.double_decoder_consistency:
-                decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
-                    text_input, text_lengths, mel_input, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings)
+                (
+                    decoder_output,
+                    postnet_output,
+                    alignments,
+                    stop_tokens,
+                    decoder_backward_output,
+                    alignments_backward,
+                ) = model(
+                    text_input, text_lengths, mel_input, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings
+                )
             else:
                 decoder_output, postnet_output, alignments, stop_tokens = model(
                     text_input, text_lengths, mel_input, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings
@@ -438,10 +466,10 @@ def evaluate(data_loader, model, criterion, ap, global_step, epoch):
 
             # aggregate losses from processes
             if num_gpus > 1:
-                loss_dict['postnet_loss'] = reduce_tensor(loss_dict['postnet_loss'].data, num_gpus)
-                loss_dict['decoder_loss'] = reduce_tensor(loss_dict['decoder_loss'].data, num_gpus)
+                loss_dict["postnet_loss"] = reduce_tensor(loss_dict["postnet_loss"].data, num_gpus)
+                loss_dict["decoder_loss"] = reduce_tensor(loss_dict["decoder_loss"].data, num_gpus)
                 if config.stopnet:
-                    loss_dict['stopnet_loss'] = reduce_tensor(loss_dict['stopnet_loss'].data, num_gpus)
+                    loss_dict["stopnet_loss"] = reduce_tensor(loss_dict["stopnet_loss"].data, num_gpus)
 
             # detach loss values
             loss_dict_new = dict()
@@ -465,9 +493,11 @@ def evaluate(data_loader, model, criterion, ap, global_step, epoch):
             # Diagnostic visualizations
             idx = np.random.randint(mel_input.shape[0])
             const_spec = postnet_output[idx].data.cpu().numpy()
-            gt_spec = linear_input[idx].data.cpu().numpy() if config.model in [
-                "Tacotron", "TacotronGST"
-            ] else mel_input[idx].data.cpu().numpy()
+            gt_spec = (
+                linear_input[idx].data.cpu().numpy()
+                if config.model in ["Tacotron", "TacotronGST"]
+                else mel_input[idx].data.cpu().numpy()
+            )
             align_img = alignments[idx].data.cpu().numpy()
 
             eval_figures = {
@@ -481,8 +511,7 @@ def evaluate(data_loader, model, criterion, ap, global_step, epoch):
                 eval_audio = ap.inv_spectrogram(const_speconfig.T)
             else:
                 eval_audio = ap.inv_melspectrogram(const_speconfig.T)
-            tb_logger.tb_eval_audios(global_step, {"ValAudio": eval_audio},
-                                     config.audio["sample_rate"])
+            tb_logger.tb_eval_audios(global_step, {"ValAudio": eval_audio}, config.audio["sample_rate"])
 
             # Plot Validation Stats
 
@@ -510,13 +539,17 @@ def evaluate(data_loader, model, criterion, ap, global_step, epoch):
         test_figures = {}
         print(" | > Synthesizing test sentences")
         speaker_id = 0 if config.use_speaker_embedding else None
-        speaker_embedding = speaker_mapping[list(speaker_mapping.keys())[randrange(len(speaker_mapping)-1)]]['embedding'] if config.use_external_speaker_embedding_file and config.use_speaker_embedding else None
+        speaker_embedding = (
+            speaker_mapping[list(speaker_mapping.keys())[randrange(len(speaker_mapping) - 1)]]["embedding"]
+            if config.use_external_speaker_embedding_file and config.use_speaker_embedding
+            else None
+        )
         style_wav = config.get("gst_style_input")
         if style_wav is None and config.use_gst:
             # inicialize GST with zero dict.
             style_wav = {}
             print("WARNING: You don't provided a gst style wav, for this reason we use a zero tensor!")
-            for i in range(config.gst['gst_num_style_tokens']):
+            for i in range(config.gst["gst_num_style_tokens"]):
                 style_wav[str(i)] = 0
         style_wav = config.get("gst_style_input")
         for idx, test_sentence in enumerate(test_sentences):
@@ -531,7 +564,7 @@ def evaluate(data_loader, model, criterion, ap, global_step, epoch):
                     speaker_embedding=speaker_embedding,
                     style_wav=style_wav,
                     truncated=False,
-                    enable_eos_bos_chars=config.enable_eos_bos_chars, #pylint: disable=unused-argument
+                    enable_eos_bos_chars=config.enable_eos_bos_chars,  # pylint: disable=unused-argument
                     use_griffin_lim=True,
                     do_trim_silence=False,
                 )
@@ -546,8 +579,7 @@ def evaluate(data_loader, model, criterion, ap, global_step, epoch):
             except:  # pylint: disable=bare-except
                 print(" !! Error creating Test Sentence -", idx)
                 traceback.print_exc()
-        tb_logger.tb_test_audios(global_step, test_audios,
-                                 config.audio['sample_rate'])
+        tb_logger.tb_test_audios(global_step, test_audios, config.audio["sample_rate"])
         tb_logger.tb_test_figures(global_step, test_figures)
     return keep_avg.avg_values
 
@@ -564,8 +596,7 @@ def main(args):  # pylint: disable=redefined-outer-name
 
     # DISTRUBUTED
     if num_gpus > 1:
-        init_distributed(args.rank, num_gpus, args.group_id,
-                         config.distributed["backend"], config.distributed["url"])
+        init_distributed(args.rank, num_gpus, args.group_id, config.distributed["backend"], config.distributed["url"])
     num_chars = len(phonemes) if config.use_phonemes else len(symbols)
     model_characters = phonemes if config.use_phonemes else symbols
 
@@ -573,10 +604,10 @@ def main(args):  # pylint: disable=redefined-outer-name
     meta_data_train, meta_data_eval = load_meta_data(config.datasets)
 
     # set the portion of the data used for training
-    if config.has('train_portion'):
-        meta_data_train = meta_data_train[:int(len(meta_data_train) * config.train_portion)]
-    if config.has('eval_portion'):
-        meta_data_eval = meta_data_eval[:int(len(meta_data_eval) * config.eval_portion)]
+    if config.has("train_portion"):
+        meta_data_train = meta_data_train[: int(len(meta_data_train) * config.train_portion)]
+    if config.has("eval_portion"):
+        meta_data_eval = meta_data_eval[: int(len(meta_data_eval) * config.eval_portion)]
 
     # parse speakers
     num_speakers, speaker_embedding_dim, speaker_mapping = parse_speakers(config, args, meta_data_train, OUT_PATH)
@@ -590,9 +621,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     params = set_weight_decay(model, config.wd)
     optimizer = RAdam(params, lr=config.lr, weight_decay=0)
     if config.stopnet and config.separate_stopnet:
-        optimizer_st = RAdam(model.decoder.stopnet.parameters(),
-                             lr=config.lr,
-                             weight_decay=0)
+        optimizer_st = RAdam(model.decoder.stopnet.parameters(), lr=config.lr, weight_decay=0)
     else:
         optimizer_st = None
 
@@ -606,7 +635,7 @@ def main(args):  # pylint: disable=redefined-outer-name
             model.load_state_dict(checkpoint["model"])
             # optimizer restore
             print(" > Restoring Optimizer...")
-            optimizer.load_state_dict(checkpoint['optimizer'])
+            optimizer.load_state_dict(checkpoint["optimizer"])
             if "scaler" in checkpoint and config.mixed_precision:
                 print(" > Restoring AMP Scaler...")
                 scaler.load_state_dict(checkpoint["scaler"])
@@ -622,10 +651,9 @@ def main(args):  # pylint: disable=redefined-outer-name
             del model_dict
 
         for group in optimizer.param_groups:
-            group['lr'] = config.lr
-        print(" > Model restored from step %d" % checkpoint['step'],
-              flush=True)
-        args.restore_step = checkpoint['step']
+            group["lr"] = config.lr
+        print(" > Model restored from step %d" % checkpoint["step"], flush=True)
+        args.restore_step = checkpoint["step"]
     else:
         args.restore_step = 0
 
@@ -638,9 +666,7 @@ def main(args):  # pylint: disable=redefined-outer-name
         model = apply_gradient_allreduce(model)
 
     if config.noam_schedule:
-        scheduler = NoamLR(optimizer,
-                           warmup_steps=config.warmup_steps,
-                           last_epoch=args.restore_step - 1)
+        scheduler = NoamLR(optimizer, warmup_steps=config.warmup_steps, last_epoch=args.restore_step - 1)
     else:
         scheduler = None
 
@@ -693,9 +719,9 @@ def main(args):  # pylint: disable=redefined-outer-name
         # eval one epoch
         eval_avg_loss_dict = evaluate(eval_loader, model, criterion, ap, global_step, epoch)
         c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
-        target_loss = train_avg_loss_dict['avg_postnet_loss']
+        target_loss = train_avg_loss_dict["avg_postnet_loss"]
         if config.run_eval:
-            target_loss = eval_avg_loss_dict['avg_postnet_loss']
+            target_loss = eval_avg_loss_dict["avg_postnet_loss"]
         best_loss = save_best_model(
             target_loss,
             best_loss,
@@ -708,11 +734,11 @@ def main(args):  # pylint: disable=redefined-outer-name
             model_characters,
             keep_all_best=keep_all_best,
             keep_after=keep_after,
-            scaler=scaler.state_dict() if config.mixed_precision else None
+            scaler=scaler.state_dict() if config.mixed_precision else None,
         )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     args, config, OUT_PATH, AUDIO_PATH, c_logger, tb_logger = init_training(sys.argv)
     try:
         main(args)

TTS/utils/arguments.py

@@ -6,8 +6,8 @@ import argparse
 import glob
 import json
 import os
-import sys
 import re
+import sys
 
 from TTS.tts.utils.text.symbols import parse_symbols
 from TTS.utils.console_logger import ConsoleLogger
@@ -46,24 +46,14 @@ def init_arguments(argv):
             "Best model file to be used for extracting best loss."
             "If not specified, the latest best model in continue path is used"
         ),
-        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.")
+        default="",
+    )
     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
 
@@ -157,8 +147,7 @@ def process_args(args):
     if config.mixed_precision:
         print("   >  Mixed precision mode is ON")
     if not os.path.exists(config.output_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)
     else:
         experiment_path = config.output_path
     audio_path = os.path.join(experiment_path, "test_audios")
@@ -172,17 +161,15 @@ def process_args(args):
         # if model characters are not set in the config file
         # save the default set to the config file for future
         # compatibility.
-        if config.has('characters_config'):
+        if config.has("characters_config"):
             used_characters = parse_symbols()
-            new_fields['characters'] = used_characters
+            new_fields["characters"] = used_characters
         copy_model_files(config, args.config_path, experiment_path, new_fields)
         os.chmod(audio_path, 0o775)
         os.chmod(experiment_path, 0o775)
-        tb_logger = TensorboardLogger(experiment_path,
-                                      model_name=config.model)
+        tb_logger = TensorboardLogger(experiment_path, model_name=config.model)
         # write model desc to tensorboard
-        tb_logger.tb_add_text("model-description", config["run_description"],
-                              0)
+        tb_logger.tb_add_text("model-description", config["run_description"], 0)
     c_logger = ConsoleLogger()
     return config, experiment_path, audio_path, c_logger, tb_logger
 

TTS/utils/generic_utils.py

@@ -73,14 +73,14 @@ def count_parameters(model):
 
 def to_camel(text):
     text = text.capitalize()
-    text = re.sub(r'(?!^)_([a-zA-Z])', lambda m: m.group(1).upper(), text)
-    text = text.replace('Tts', 'TTS')
+    text = re.sub(r"(?!^)_([a-zA-Z])", lambda m: m.group(1).upper(), text)
+    text = text.replace("Tts", "TTS")
     return text
 
 
 def find_module(module_path: str, module_name: str) -> object:
     module_name = module_name.lower()
-    module = importlib.import_module(module_path+'.'+module_name)
+    module = importlib.import_module(module_path + "." + module_name)
     class_name = to_camel(module_name)
     return getattr(module, class_name)
 
@@ -156,4 +156,3 @@ class KeepAverage:
     def update_values(self, value_dict):
         for key, value in value_dict.items():
             self.update_value(key, value)
-

TTS/utils/io.py

@@ -5,6 +5,7 @@ import re
 from shutil import copyfile
 
 import yaml
+
 from TTS.utils.generic_utils import find_module
 
 from .generic_utils import find_module
@@ -32,8 +33,8 @@ def read_json_with_comments(json_path):
     with open(json_path, "r", encoding="utf-8") as f:
         input_str = f.read()
     # handle comments
-    input_str = re.sub(r'\\\n', '', input_str)
-    input_str = re.sub(r'//.*\n', '\n', input_str)
+    input_str = re.sub(r"\\\n", "", input_str)
+    input_str = re.sub(r"//.*\n", "\n", input_str)
     data = json.loads(input_str)
     return data
 
@@ -44,20 +45,19 @@ def load_config(config_path: str) -> None:
     if ext in (".yml", ".yaml"):
         with open(config_path, "r", encoding="utf-8") as f:
             data = yaml.safe_load(f)
-    elif ext == '.json':
+    elif ext == ".json":
         with open(config_path, "r", encoding="utf-8") as f:
             input_str = f.read()
         data = json.loads(input_str)
     else:
-        raise TypeError(f' [!] Unknown config file type {ext}')
+        raise TypeError(f" [!] Unknown config file type {ext}")
     config_dict.update(data)
-    config_class = find_module('TTS.tts.configs', config_dict['model'].lower()+'_config')
+    config_class = find_module("TTS.tts.configs", config_dict["model"].lower() + "_config")
     config = config_class()
     config.from_dict(config_dict)
     return config
 
 
-
 def copy_model_files(c, config_file, out_path, new_fields):
     """Copy config.json and other model files to training folder and add
     new fields.