a ton of linter updates

TTS/bin/synthesize.py

@@ -170,7 +170,7 @@ def main():
         args.vocoder_name = model_item['default_vocoder'] if args.vocoder_name is None else args.vocoder_name
 
     if args.vocoder_name is not None:
-        vocoder_path, vocoder_config_path, vocoder_item = manager.download_model(args.vocoder_name)
+        vocoder_path, vocoder_config_path, _ = manager.download_model(args.vocoder_name)
 
     # CASE3: load custome models
     if args.model_path is not None:

TTS/bin/train_glow_tts.py

@@ -573,7 +573,7 @@ def main(args):  # pylint: disable=redefined-outer-name
         if c.run_eval:
             target_loss = eval_avg_loss_dict['avg_loss']
         best_loss = save_best_model(target_loss, best_loss, model, optimizer,
-                                    global_step, epoch, c.r, OUT_PATH,
+                                    global_step, epoch, c.r, OUT_PATH, model_characters,
                                     keep_all_best=keep_all_best, keep_after=keep_after)
 
 

TTS/bin/train_speedy_speech.py

@@ -1,8 +1,6 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
-import argparse
-import glob
 import os
 import sys
 import time
@@ -535,7 +533,7 @@ def main(args):  # pylint: disable=redefined-outer-name
         if c.run_eval:
             target_loss = eval_avg_loss_dict['avg_loss']
         best_loss = save_best_model(target_loss, best_loss, model, optimizer,
-                                    global_step, epoch, c.r, OUT_PATH,
+                                    global_step, epoch, c.r, OUT_PATH, model_characters,
                                     keep_all_best=keep_all_best, keep_after=keep_after)
 
 

TTS/bin/train_tacotron.py

@@ -648,12 +648,14 @@ def main(args):  # pylint: disable=redefined-outer-name
             epoch,
             c.r,
             OUT_PATH,
+            model_characters,
             keep_all_best=keep_all_best,
             keep_after=keep_after,
             scaler=scaler.state_dict() if c.mixed_precision else None
         )
 
 
+
 if __name__ == '__main__':
     args = parse_arguments(sys.argv)
     c, OUT_PATH, AUDIO_PATH, c_logger, tb_logger = process_args(

TTS/bin/train_vocoder_gan.py

@@ -50,7 +50,7 @@ def setup_loader(ap, is_val=False, verbose=False):
         sampler = DistributedSampler(dataset, shuffle=True) if num_gpus > 1 else None
         loader = DataLoader(dataset,
                             batch_size=1 if is_val else c.batch_size,
-                            shuffle=False if num_gpus > 1 else True,
+                            shuffle=num_gpus == 0,
                             drop_last=False,
                             sampler=sampler,
                             num_workers=c.num_val_loader_workers

TTS/server/server.py

@@ -59,7 +59,7 @@ if args.list_models:
 # set models by the released models
 if args.model_name is not None:
     tts_checkpoint_file, tts_config_file, tts_json_dict = manager.download_model(args.model_name)
-    args.vocoder_name =  tts_json_dict['default_vocoder'] if args.vocoder_name is None else args.vocoder_name
+    args.vocoder_name = tts_json_dict['default_vocoder'] if args.vocoder_name is None else args.vocoder_name
 
 if args.vocoder_name is not None:
     vocoder_checkpoint_file, vocoder_config_file, vocoder_json_dict = manager.download_model(args.vocoder_name)

TTS/tts/datasets/TTSDataset.py

@@ -1,7 +1,7 @@
 import collections
 import os
 import random
-from multiprocessing import Manager, Pool
+from multiprocessing import Pool
 
 import numpy as np
 import torch

TTS/tts/datasets/preprocess.py

@@ -3,7 +3,7 @@ from glob import glob
 import re
 import sys
 from pathlib import Path
-from typing import List, Tuple
+from typing import List
 
 from tqdm import tqdm
 

TTS/tts/layers/attentions.py

@@ -367,18 +367,18 @@ class MonotonicDynamicConvolutionAttention(nn.Module):
         beta (float, optional): [description]. Defaults to 0.9 from the paper.
     """
     def __init__(
-        self,
-        query_dim,
-        embedding_dim,  # pylint: disable=unused-argument
-        attention_dim,
-        static_filter_dim,
-        static_kernel_size,
-        dynamic_filter_dim,
-        dynamic_kernel_size,
-        prior_filter_len=11,
-        alpha=0.1,
-        beta=0.9,
-    ):
+            self,
+            query_dim,
+            embedding_dim,  # pylint: disable=unused-argument
+            attention_dim,
+            static_filter_dim,
+            static_kernel_size,
+            dynamic_filter_dim,
+            dynamic_kernel_size,
+            prior_filter_len=11,
+            alpha=0.1,
+            beta=0.9,
+        ):
         super().__init__()
         self._mask_value = 1e-8
         self.dynamic_filter_dim = dynamic_filter_dim
@@ -402,7 +402,7 @@ class MonotonicDynamicConvolutionAttention(nn.Module):
         self.v = nn.Linear(attention_dim, 1, bias=False)
 
         prior = betabinom.pmf(range(prior_filter_len), prior_filter_len - 1,
-                          alpha, beta)
+                              alpha, beta)
         self.register_buffer("prior", torch.FloatTensor(prior).flip(0))
 
     # pylint: disable=unused-argument

TTS/tts/layers/generic/res_conv_bn.py

@@ -97,7 +97,7 @@ class ResidualConv1dBNBlock(nn.Module):
         assert len(dilations) == num_res_blocks
         self.res_blocks = nn.ModuleList()
         for idx, dilation in enumerate(dilations):
-            block = Conv1dBNBlock(in_channels if idx==0 else hidden_channels,
+            block = Conv1dBNBlock(in_channels if idx == 0 else hidden_channels,
                                   out_channels if (idx + 1) == len(dilations) else hidden_channels,
                                   hidden_channels,
                                   kernel_size,

TTS/tts/layers/glow_tts/encoder.py

@@ -98,11 +98,11 @@ class Encoder(nn.Module):
         if encoder_type.lower() == "rel_pos_transformer":
             if use_prenet:
                 self.prenet = ResidualConv1dLayerNormBlock(hidden_channels,
-                                         hidden_channels,
-                                         hidden_channels,
-                                         kernel_size=5,
-                                         num_layers=3,
-                                         dropout_p=0.5)
+                                                           hidden_channels,
+                                                           hidden_channels,
+                                                           kernel_size=5,
+                                                           num_layers=3,
+                                                           dropout_p=0.5)
             self.encoder = RelativePositionTransformer(hidden_channels,
                                                        hidden_channels,
                                                        hidden_channels,
@@ -125,11 +125,11 @@ class Encoder(nn.Module):
         elif encoder_type.lower() == 'time_depth_separable':
             if use_prenet:
                 self.prenet = ResidualConv1dLayerNormBlock(hidden_channels,
-                                         hidden_channels,
-                                         hidden_channels,
-                                         kernel_size=5,
-                                         num_layers=3,
-                                         dropout_p=0.5)
+                                                           hidden_channels,
+                                                           hidden_channels,
+                                                           kernel_size=5,
+                                                           num_layers=3,
+                                                           dropout_p=0.5)
             self.encoder = TimeDepthSeparableConvBlock(hidden_channels,
                                                        hidden_channels,
                                                        hidden_channels,

TTS/tts/layers/glow_tts/transformer.py

@@ -366,8 +366,10 @@ class RelativePositionTransformer(nn.Module):
                 self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
 
             self.ffn_layers.append(
-                FeedForwardNetwork(hidden_channels,
-                    hidden_channels if (idx + 1) != self.num_layers else out_channels,
+                FeedForwardNetwork(
+                    hidden_channels,
+                    hidden_channels if
+                    (idx + 1) != self.num_layers else out_channels,
                     hidden_channels_ffn,
                     kernel_size,
                     dropout_p=dropout_p))

TTS/tts/layers/gst_layers.py

@@ -75,7 +75,7 @@ class ReferenceEncoder(nn.Module):
         # x: 3D tensor [batch_size, post_conv_width,
         #               num_channels*post_conv_height]
         self.recurrence.flatten_parameters()
-        memory, out = self.recurrence(x)
+        _, out = self.recurrence(x)
         # out: 3D tensor [seq_len==1, batch_size, encoding_size=128]
 
         return out.squeeze(0)

TTS/tts/layers/losses.py

@@ -2,13 +2,12 @@ import math
 import numpy as np
 import torch
 from torch import nn
-from inspect import signature
 from torch.nn import functional
 from TTS.tts.utils.generic_utils import sequence_mask
 from TTS.tts.utils.ssim import ssim
 
 
-# pylint: disable=abstract-method Method
+# pylint: disable=abstract-method
 # relates https://github.com/pytorch/pytorch/issues/42305
 class L1LossMasked(nn.Module):
     def __init__(self, seq_len_norm):
@@ -165,7 +164,7 @@ class BCELossMasked(nn.Module):
         target.requires_grad = False
         if length is not None:
             mask = sequence_mask(sequence_length=length,
-                                max_len=target.size(1)).float()
+                                 max_len=target.size(1)).float()
             x = x * mask
             target = target * mask
             num_items = mask.sum()
@@ -310,10 +309,10 @@ class TacotronLoss(torch.nn.Module):
             if self.postnet_alpha > 0:
                 if self.config.model in ["Tacotron", "TacotronGST"]:
                     postnet_loss = self.criterion(postnet_output, linear_input,
-                                                output_lens)
+                                                  output_lens)
                 else:
                     postnet_loss = self.criterion(postnet_output, mel_input,
-                                                output_lens)
+                                                  output_lens)
         else:
             if self.decoder_alpha > 0:
                 decoder_loss = self.criterion(decoder_output, mel_input)

TTS/tts/layers/speedy_speech/decoder.py

@@ -146,17 +146,17 @@ class Decoder(nn.Module):
 
     # pylint: disable=dangerous-default-value
     def __init__(
-        self,
-        out_channels,
-        in_hidden_channels,
-        decoder_type='residual_conv_bn',
-        decoder_params={
-            "kernel_size": 4,
-            "dilations": 4 * [1, 2, 4, 8] + [1],
-            "num_conv_blocks": 2,
-            "num_res_blocks": 17
-        },
-        c_in_channels=0):
+            self,
+            out_channels,
+            in_hidden_channels,
+            decoder_type='residual_conv_bn',
+            decoder_params={
+                "kernel_size": 4,
+                "dilations": 4 * [1, 2, 4, 8] + [1],
+                "num_conv_blocks": 2,
+                "num_res_blocks": 17
+            },
+            c_in_channels=0):
         super().__init__()
 
         if decoder_type == 'transformer':

TTS/tts/layers/speedy_speech/encoder.py

@@ -73,13 +73,12 @@ class RelativePositionTransformerEncoder(nn.Module):
     def __init__(self, in_channels, out_channels, hidden_channels, params):
         super().__init__()
         self.prenet = ResidualConv1dBNBlock(in_channels,
-                                     hidden_channels,
-                                     hidden_channels,
-                                     kernel_size=5,
-                                     num_res_blocks=3,
-                                     num_conv_blocks=1,
-                                     dilations=[1, 1, 1]
-                                     )
+                                            hidden_channels,
+                                            hidden_channels,
+                                            kernel_size=5,
+                                            num_res_blocks=3,
+                                            num_conv_blocks=1,
+                                            dilations=[1, 1, 1])
         self.rel_pos_transformer = RelativePositionTransformer(
             hidden_channels, out_channels, hidden_channels, **params)
 
@@ -104,9 +103,8 @@ class ResidualConv1dBNEncoder(nn.Module):
     """
     def __init__(self, in_channels, out_channels, hidden_channels, params):
         super().__init__()
-        self.prenet =  nn.Sequential(
-                nn.Conv1d(in_channels, hidden_channels, 1),
-                nn.ReLU())
+        self.prenet = nn.Sequential(nn.Conv1d(in_channels, hidden_channels, 1),
+                                    nn.ReLU())
         self.res_conv_block = ResidualConv1dBNBlock(hidden_channels,
                                                     hidden_channels,
                                                     hidden_channels, **params)
@@ -162,17 +160,17 @@ class Encoder(nn.Module):
             }
     """
     def __init__(
-        self,
-        in_hidden_channels,
-        out_channels,
-        encoder_type='residual_conv_bn',
-        encoder_params={
-            "kernel_size": 4,
-            "dilations": 4 * [1, 2, 4] + [1],
-            "num_conv_blocks": 2,
-            "num_res_blocks": 13
-        },
-        c_in_channels=0):
+            self,
+            in_hidden_channels,
+            out_channels,
+            encoder_type='residual_conv_bn',
+            encoder_params={
+                "kernel_size": 4,
+                "dilations": 4 * [1, 2, 4] + [1],
+                "num_conv_blocks": 2,
+                "num_res_blocks": 13
+            },
+            c_in_channels=0):
         super().__init__()
         self.out_channels = out_channels
         self.in_channels = in_hidden_channels
@@ -183,10 +181,9 @@ class Encoder(nn.Module):
         # init encoder
         if encoder_type.lower() == "transformer":
             # text encoder
-            self.encoder = RelativePositionTransformerEncoder(in_hidden_channels,
-                                                   out_channels,
-                                                   in_hidden_channels,
-                                                   encoder_params)  # pylint: disable=unexpected-keyword-arg
+            self.encoder = RelativePositionTransformerEncoder(
+                in_hidden_channels, out_channels, in_hidden_channels,
+                encoder_params)  # pylint: disable=unexpected-keyword-arg
         elif encoder_type.lower() == 'residual_conv_bn':
             self.encoder = ResidualConv1dBNEncoder(in_hidden_channels,
                                                    out_channels,

TTS/tts/models/speedy_speech.py

@@ -33,32 +33,32 @@ class SpeedySpeech(nn.Module):
         external_c (bool, optional): enable external speaker embeddings. Defaults to False.
         c_in_channels (int, optional): number of channels in speaker embedding vectors. Defaults to 0.
     """
-# pylint: disable=dangerous-default-value
+    # pylint: disable=dangerous-default-value
 
     def __init__(
-        self,
-        num_chars,
-        out_channels,
-        hidden_channels,
-        positional_encoding=True,
-        length_scale=1,
-        encoder_type='residual_conv_bn',
-        encoder_params={
-            "kernel_size": 4,
-            "dilations": 4 * [1, 2, 4] + [1],
-            "num_conv_blocks": 2,
-            "num_res_blocks": 13
-        },
-        decoder_type='residual_conv_bn',
-        decoder_params={
-            "kernel_size": 4,
-            "dilations": 4 * [1, 2, 4, 8] + [1],
-            "num_conv_blocks": 2,
-            "num_res_blocks": 17
-        },
-        num_speakers=0,
-        external_c=False,
-        c_in_channels=0):
+            self,
+            num_chars,
+            out_channels,
+            hidden_channels,
+            positional_encoding=True,
+            length_scale=1,
+            encoder_type='residual_conv_bn',
+            encoder_params={
+                "kernel_size": 4,
+                "dilations": 4 * [1, 2, 4] + [1],
+                "num_conv_blocks": 2,
+                "num_res_blocks": 13
+            },
+            decoder_type='residual_conv_bn',
+            decoder_params={
+                "kernel_size": 4,
+                "dilations": 4 * [1, 2, 4, 8] + [1],
+                "num_conv_blocks": 2,
+                "num_res_blocks": 17
+            },
+            num_speakers=0,
+            external_c=False,
+            c_in_channels=0):
 
         super().__init__()
         self.length_scale = float(length_scale) if isinstance(length_scale, int) else length_scale
@@ -171,7 +171,7 @@ class SpeedySpeech(nn.Module):
         """
         o_en, o_en_dp, x_mask, g = self._forward_encoder(x, x_lengths, g)
         o_dr_log = self.duration_predictor(o_en_dp.detach(), x_mask)
-        o_de, attn= self._forward_decoder(o_en, o_en_dp, dr, x_mask, y_lengths, g=g)
+        o_de, attn = self._forward_decoder(o_en, o_en_dp, dr, x_mask, y_lengths, g=g)
         return o_de, o_dr_log.squeeze(1), attn
 
     def inference(self, x, x_lengths, g=None):  # pylint: disable=unused-argument

TTS/tts/utils/chinese_mandarin/numbers.py

@@ -10,7 +10,7 @@ import re
 import itertools
 
 
-def _num2chinese(num :str, big=False, simp=True, o=False, twoalt=False) -> str:
+def _num2chinese(num: str, big=False, simp=True, o=False, twoalt=False) -> str:
     """Convert numerical arabic numbers (0->9) to chinese hanzi numbers (〇 -> 九)
 
     Args:
@@ -32,7 +32,7 @@ def _num2chinese(num :str, big=False, simp=True, o=False, twoalt=False) -> str:
     nd = str(num)
     if abs(float(nd)) >= 1e48:
         raise ValueError('number out of range')
-    elif 'e' in nd:
+    if 'e' in nd:
         raise ValueError('scientific notation is not supported')
     c_symbol = '正负点' if simp else '正負點'
     if o:  # formal
@@ -69,7 +69,7 @@ def _num2chinese(num :str, big=False, simp=True, o=False, twoalt=False) -> str:
             if int(unit) == 0:  # 0000
                 intresult.append(c_basic[0])
                 continue
-            elif nu > 0 and int(unit) == 2:  # 0002
+            if nu > 0 and int(unit) == 2:  # 0002
                 intresult.append(c_twoalt + c_unit2[nu - 1])
                 continue
             ulist = []

TTS/tts/utils/generic_utils.py

@@ -135,7 +135,7 @@ def setup_model(num_chars, num_speakers, c, speaker_embedding_dim=None):
     return model
 
 def is_tacotron(c):
-    return False if c['model'] in ['speedy_speech', 'glow_tts'] else True
+    return not c['model'] in ['speedy_speech', 'glow_tts']
 
 def check_config_tts(c):
     check_argument('model', c, enum_list=['tacotron', 'tacotron2', 'glow_tts', 'speedy_speech'], restricted=True, val_type=str)

TTS/tts/utils/io.py

@@ -7,7 +7,7 @@ from TTS.utils.io import RenamingUnpickler
 
 
 
-def load_checkpoint(model, checkpoint_path, amp=None, use_cuda=False, eval=False):
+def load_checkpoint(model, checkpoint_path, amp=None, use_cuda=False, eval=False):  # pylint: disable=redefined-builtin
     """Load ```TTS.tts.models``` checkpoints.
 
     Args:
@@ -98,7 +98,7 @@ def save_checkpoint(model, optimizer, current_step, epoch, r, output_folder,
 
 
 def save_best_model(target_loss, best_loss, model, optimizer, current_step,
-                    epoch, r, output_folder, characters,  **kwargs):
+                    epoch, r, output_folder, characters, **kwargs):
     """Save model checkpoint, intended for saving the best model after each epoch.
     It compares the current model loss with the best loss so far and saves the
     model if the current loss is better.

TTS/tts/utils/speakers.py

@@ -63,8 +63,8 @@ def parse_speakers(c, args, meta_data_train, OUT_PATH):
             speaker_embedding_dim = None
         save_speaker_mapping(OUT_PATH, speaker_mapping)
         num_speakers = len(speaker_mapping)
-        print(" > Training with {} speakers: {}".format(len(speakers),
-                                                     ", ".join(speakers)))
+        print(" > Training with {} speakers: {}".format(
+            len(speakers), ", ".join(speakers)))
     else:
         num_speakers = 0
         speaker_embedding_dim = None

TTS/tts/utils/ssim.py

@@ -17,17 +17,22 @@ def create_window(window_size, channel):
     window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
     return window
 
-def _ssim(img1, img2, window, window_size, channel, size_average = True):
-    mu1 = F.conv2d(img1, window, padding = window_size//2, groups = channel)
-    mu2 = F.conv2d(img2, window, padding = window_size//2, groups = channel)
+
+def _ssim(img1, img2, window, window_size, channel, size_average=True):
+    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
+    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
 
     mu1_sq = mu1.pow(2)
     mu2_sq = mu2.pow(2)
     mu1_mu2 = mu1*mu2
 
-    sigma1_sq = F.conv2d(img1*img1, window, padding = window_size//2, groups = channel) - mu1_sq
-    sigma2_sq = F.conv2d(img2*img2, window, padding = window_size//2, groups = channel) - mu2_sq
-    sigma12 = F.conv2d(img1*img2, window, padding = window_size//2, groups = channel) - mu1_mu2
+    sigma1_sq = F.conv2d(
+        img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
+    sigma2_sq = F.conv2d(
+        img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
+    sigma12 = F.conv2d(
+        img1 * img2, window, padding=window_size // 2,
+        groups=channel) - mu1_mu2
 
     C1 = 0.01**2
     C2 = 0.03**2
@@ -39,7 +44,7 @@ def _ssim(img1, img2, window, window_size, channel, size_average = True):
     return ssim_map.mean(1).mean(1).mean(1)
 
 class SSIM(torch.nn.Module):
-    def __init__(self, window_size = 11, size_average = True):
+    def __init__(self, window_size=11, size_average=True):
         super().__init__()
         self.window_size = window_size
         self.size_average = size_average
@@ -64,7 +69,8 @@ class SSIM(torch.nn.Module):
 
         return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
 
-def ssim(img1, img2, window_size = 11, size_average = True):
+
+def ssim(img1, img2, window_size=11, size_average=True):
     (_, channel, _, _) = img1.size()
     window = create_window(window_size, channel)
 

TTS/tts/utils/synthesis.py

@@ -20,9 +20,13 @@ def text_to_seqvec(text, CONFIG):
                                 add_blank=CONFIG['add_blank'] if 'add_blank' in CONFIG.keys() else False),
             dtype=np.int32)
     else:
-        seq = np.asarray(
-            text_to_sequence(text, text_cleaner, tp=CONFIG.characters if 'characters' in CONFIG.keys() else None,
-            add_blank=CONFIG['add_blank'] if 'add_blank' in CONFIG.keys() else False), dtype=np.int32)
+        seq = np.asarray(text_to_sequence(
+            text,
+            text_cleaner,
+            tp=CONFIG.characters if 'characters' in CONFIG.keys() else None,
+            add_blank=CONFIG['add_blank']
+            if 'add_blank' in CONFIG.keys() else False),
+                         dtype=np.int32)
     return seq
 
 
@@ -77,9 +81,9 @@ def run_model_torch(model, inputs, CONFIG, truncated, speaker_id=None, style_mel
         inputs_lengths = torch.tensor(inputs.shape[1:2]).to(inputs.device)  # pylint: disable=not-callable
         if hasattr(model, 'module'):
             # distributed model
-            postnet_output, alignments= model.module.inference(inputs, inputs_lengths, g=speaker_id if speaker_id is not None else speaker_embeddings)
+            postnet_output, alignments = model.module.inference(inputs, inputs_lengths, g=speaker_id if speaker_id is not None else speaker_embeddings)
         else:
-            postnet_output, alignments= model.inference(inputs, inputs_lengths, g=speaker_id if speaker_id is not None else speaker_embeddings)
+            postnet_output, alignments = model.inference(inputs, inputs_lengths, g=speaker_id if speaker_id is not None else speaker_embeddings)
         postnet_output = postnet_output.permute(0, 2, 1)
         # these only belong to tacotron models.
         decoder_output = None

TTS/tts/utils/text/abbreviations.py

@@ -2,60 +2,60 @@ import re
 
 # List of (regular expression, replacement) pairs for abbreviations in english:
 abbreviations_en = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1])
-                  for x in [
-                      ('mrs', 'misess'),
-                      ('mr', 'mister'),
-                      ('dr', 'doctor'),
-                      ('st', 'saint'),
-                      ('co', 'company'),
-                      ('jr', 'junior'),
-                      ('maj', 'major'),
-                      ('gen', 'general'),
-                      ('drs', 'doctors'),
-                      ('rev', 'reverend'),
-                      ('lt', 'lieutenant'),
-                      ('hon', 'honorable'),
-                      ('sgt', 'sergeant'),
-                      ('capt', 'captain'),
-                      ('esq', 'esquire'),
-                      ('ltd', 'limited'),
-                      ('col', 'colonel'),
-                      ('ft', 'fort'),
-                  ]]
+                    for x in [
+                        ('mrs', 'misess'),
+                        ('mr', 'mister'),
+                        ('dr', 'doctor'),
+                        ('st', 'saint'),
+                        ('co', 'company'),
+                        ('jr', 'junior'),
+                        ('maj', 'major'),
+                        ('gen', 'general'),
+                        ('drs', 'doctors'),
+                        ('rev', 'reverend'),
+                        ('lt', 'lieutenant'),
+                        ('hon', 'honorable'),
+                        ('sgt', 'sergeant'),
+                        ('capt', 'captain'),
+                        ('esq', 'esquire'),
+                        ('ltd', 'limited'),
+                        ('col', 'colonel'),
+                        ('ft', 'fort'),
+                    ]]
 
 # List of (regular expression, replacement) pairs for abbreviations in french:
 abbreviations_fr = [(re.compile('\\b%s\\.?' % x[0], re.IGNORECASE), x[1])
-                  for x in [
-                      ('M', 'monsieur'),
-                      ('Mlle', 'mademoiselle'),
-                      ('Mlles', 'mesdemoiselles'),
-                      ('Mme', 'Madame'),
-                      ('Mmes', 'Mesdames'),
-                      ('N.B', 'nota bene'),
-                      ('M', 'monsieur'),
-                      ('p.c.q', 'parce que'),
-                      ('Pr', 'professeur'),
-                      ('qqch', 'quelque chose'),
-                      ('rdv', 'rendez-vous'),
-                      ('max', 'maximum'),
-                      ('min', 'minimum'),
-                      ('no', 'numéro'),
-                      ('adr', 'adresse'),
-                      ('dr', 'docteur'),
-                      ('st', 'saint'),
-                      ('co', 'companie'),
-                      ('jr', 'junior'),
-                      ('sgt', 'sergent'),
-                      ('capt', 'capitain'),
-                      ('col', 'colonel'),
-                      ('av', 'avenue'),
-                      ('av. J.-C', 'avant Jésus-Christ'),
-                      ('apr. J.-C', 'après Jésus-Christ'),
-                      ('art', 'article'),
-                      ('boul', 'boulevard'),
-                      ('c.-à-d', 'c’est-à-dire'),
-                      ('etc', 'et cetera'),
-                      ('ex', 'exemple'),
-                      ('excl', 'exclusivement'),
-                      ('boul', 'boulevard'),
-                  ]]
+                    for x in [
+                        ('M', 'monsieur'),
+                        ('Mlle', 'mademoiselle'),
+                        ('Mlles', 'mesdemoiselles'),
+                        ('Mme', 'Madame'),
+                        ('Mmes', 'Mesdames'),
+                        ('N.B', 'nota bene'),
+                        ('M', 'monsieur'),
+                        ('p.c.q', 'parce que'),
+                        ('Pr', 'professeur'),
+                        ('qqch', 'quelque chose'),
+                        ('rdv', 'rendez-vous'),
+                        ('max', 'maximum'),
+                        ('min', 'minimum'),
+                        ('no', 'numéro'),
+                        ('adr', 'adresse'),
+                        ('dr', 'docteur'),
+                        ('st', 'saint'),
+                        ('co', 'companie'),
+                        ('jr', 'junior'),
+                        ('sgt', 'sergent'),
+                        ('capt', 'capitain'),
+                        ('col', 'colonel'),
+                        ('av', 'avenue'),
+                        ('av. J.-C', 'avant Jésus-Christ'),
+                        ('apr. J.-C', 'après Jésus-Christ'),
+                        ('art', 'article'),
+                        ('boul', 'boulevard'),
+                        ('c.-à-d', 'c’est-à-dire'),
+                        ('etc', 'et cetera'),
+                        ('ex', 'exemple'),
+                        ('excl', 'exclusivement'),
+                        ('boul', 'boulevard'),
+                    ]]

TTS/utils/io.py

@@ -22,7 +22,7 @@ class AttrDict(dict):
 
 def read_json_with_comments(json_path):
     # fallback to json
-    with open(json_path, "r", encoding = "utf-8") as f:
+    with open(json_path, "r", encoding="utf-8") as f:
         input_str = f.read()
     # handle comments
     input_str = re.sub(r'\\\n', '', input_str)
@@ -40,7 +40,7 @@ def load_config(config_path: str) -> AttrDict:
 
     ext = os.path.splitext(config_path)[1]
     if ext in (".yml", ".yaml"):
-        with open(config_path, "r", encoding = "utf-8") as f:
+        with open(config_path, "r", encoding="utf-8") as f:
             data = yaml.safe_load(f)
     else:
         data = read_json_with_comments(config_path)
@@ -61,7 +61,7 @@ def copy_model_files(c, config_file, out_path, new_fields):
     """
     # copy config.json
     copy_config_path = os.path.join(out_path, 'config.json')
-    config_lines = open(config_file, "r", encoding = "utf-8").readlines()
+    config_lines = open(config_file, "r", encoding="utf-8").readlines()
     # add extra information fields
     for key, value in new_fields.items():
         if isinstance(value, str):

TTS/utils/manage.py

@@ -144,8 +144,3 @@ class ModelManager(object):
             if isinstance(key, str) and len(my_dict[key]) > 0:
                 return True
         return False
-
-
-
-
-

TTS/vocoder/layers/losses.py

@@ -4,7 +4,7 @@ from torch import nn
 from torch.nn import functional as F
 
 
-class TorchSTFT(nn.Module):
+class TorchSTFT(nn.Module):  # pylint: disable=abstract-method
     def __init__(self, n_fft, hop_length, win_length, window='hann_window'):
         """ Torch based STFT operation """
         super(TorchSTFT, self).__init__()

TTS/vocoder/layers/wavegrad.py

@@ -22,8 +22,10 @@ class PositionalEncoding(nn.Module):
 
     def forward(self, x, noise_level):
         if x.shape[2] > self.pe.shape[1]:
-            self.init_pe_matrix(x.shape[1] ,x.shape[2], x)
-        return x + noise_level[..., None, None] + self.pe[:, :x.size(2)].repeat(x.shape[0], 1, 1) / self.C
+            self.init_pe_matrix(x.shape[1], x.shape[2], x)
+        return x + noise_level[..., None,
+                               None] + self.pe[:, :x.size(2)].repeat(
+                                   x.shape[0], 1, 1) / self.C
 
     def init_pe_matrix(self, n_channels, max_len, x):
         pe = torch.zeros(max_len, n_channels)
@@ -171,5 +173,4 @@ class DBlock(nn.Module):
         self.res_block = weight_norm(self.res_block)
         for idx, layer in enumerate(self.main_block):
             if len(layer.state_dict()) != 0:
-               self.main_block[idx] = weight_norm(layer)
-
+                self.main_block[idx] = weight_norm(layer)

TTS/vocoder/models/wavegrad.py

@@ -79,7 +79,7 @@ class Wavegrad(nn.Module):
         return x
 
     def load_noise_schedule(self, path):
-        beta = np.load(path, allow_pickle=True).item()['beta']
+        beta = np.load(path, allow_pickle=True).item()['beta']  # pylint: disable=unexpected-keyword-arg
         self.compute_noise_level(beta)
 
     @torch.no_grad()
@@ -91,8 +91,8 @@ class Wavegrad(nn.Module):
             y_n = torch.FloatTensor(y_n).unsqueeze(0).unsqueeze(0).to(x)
         sqrt_alpha_hat = self.noise_level.to(x)
         for n in range(len(self.alpha) - 1, -1, -1):
-            y_n = self.c1[n] * (y_n -
-                        self.c2[n] * self.forward(y_n, x, sqrt_alpha_hat[n].repeat(x.shape[0])))
+            y_n = self.c1[n] * (y_n - self.c2[n] * self.forward(
+                y_n, x, sqrt_alpha_hat[n].repeat(x.shape[0])))
             if n > 0:
                 z = torch.randn_like(y_n)
                 y_n += self.sigma[n - 1] * z

TTS/vocoder/models/wavernn.py

@@ -73,15 +73,15 @@ class Stretch2d(nn.Module):
 
 class UpsampleNetwork(nn.Module):
     def __init__(
-        self,
-        feat_dims,
-        upsample_scales,
-        compute_dims,
-        num_res_blocks,
-        res_out_dims,
-        pad,
-        use_aux_net,
-    ):
+            self,
+            feat_dims,
+            upsample_scales,
+            compute_dims,
+            num_res_blocks,
+            res_out_dims,
+            pad,
+            use_aux_net,
+        ):
         super().__init__()
         self.total_scale = np.cumproduct(upsample_scales)[-1]
         self.indent = pad * self.total_scale
@@ -118,9 +118,8 @@ class UpsampleNetwork(nn.Module):
 
 
 class Upsample(nn.Module):
-    def __init__(
-        self, scale, pad, num_res_blocks, feat_dims, compute_dims, res_out_dims, use_aux_net
-    ):
+    def __init__(self, scale, pad, num_res_blocks, feat_dims, compute_dims,
+                 res_out_dims, use_aux_net):
         super().__init__()
         self.scale = scale
         self.pad = pad

TTS/vocoder/utils/distribution.py

@@ -44,9 +44,11 @@ def log_sum_exp(x):
 
 
 # It is adapted from https://github.com/r9y9/wavenet_vocoder/blob/master/wavenet_vocoder/mixture.py
-def discretized_mix_logistic_loss(
-    y_hat, y, num_classes=65536, log_scale_min=None, reduce=True
-):
+def discretized_mix_logistic_loss(y_hat,
+                                  y,
+                                  num_classes=65536,
+                                  log_scale_min=None,
+                                  reduce=True):
     if log_scale_min is None:
         log_scale_min = float(np.log(1e-14))
     y_hat = y_hat.permute(0, 2, 1)

TTS/vocoder/utils/io.py

@@ -7,7 +7,7 @@ import pickle as pickle_tts
 from TTS.utils.io import RenamingUnpickler
 
 
-def load_checkpoint(model, checkpoint_path, use_cuda=False, eval=False):
+def load_checkpoint(model, checkpoint_path, use_cuda=False, eval=False):  # pylint: disable=redefined-builtin
     try:
         state = torch.load(checkpoint_path, map_location=torch.device('cpu'))
     except ModuleNotFoundError:

tests/test_layers.py

@@ -166,7 +166,7 @@ class SSIMLossTests(unittest.TestCase):
         dummy_target = T.zeros(4, 8, 128).float()
         dummy_length = (T.ones(4) * 8).long()
         output = layer(dummy_input, dummy_target, dummy_length)
-        assert abs(output.item() - 1.0) < 1e-4 , "1.0 vs {}".format(output.item())
+        assert abs(output.item() - 1.0) < 1e-4, "1.0 vs {}".format(output.item())
 
         # test if padded values of input makes any difference
         dummy_input = T.ones(4, 8, 128).float()
@@ -217,4 +217,3 @@ class SSIMLossTests(unittest.TestCase):
             (sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
         output = layer(dummy_input + mask, dummy_target, dummy_length)
         assert output.item() == 0, "0 vs {}".format(output.item())
-

tests/test_speedy_speech_layers.py

@@ -161,7 +161,7 @@ def test_speedy_speech():
                   x_lengths,
                   y_lengths,
                   durations,
-                  g=torch.rand((B,256)).to(device))
+                  g=torch.rand((B, 256)).to(device))
 
     assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
     assert list(attn.shape) == [B, T_de, T_en]

tests/test_tacotron_model.py

@@ -356,4 +356,3 @@ class SCGSTMultiSpeakeTacotronTrainTest(unittest.TestCase):
             ), "param {} with shape {} not updated!! \n{}\n{}".format(
                 count, param.shape, param, param_ref)
             count += 1
-

tests/test_text_cleaners.py

@@ -17,5 +17,5 @@ def test_currency() -> None:
 
 
 def test_expand_numbers() -> None:
-    assert "minus one" == phoneme_cleaners("-1")
-    assert "one" == phoneme_cleaners("1")
+    assert phoneme_cleaners("-1") == 'minus one'
+    assert phoneme_cleaners("1") == 'one'

tests/test_text_processing.py

@@ -17,7 +17,7 @@ def test_phoneme_to_sequence():
     lang = "en-us"
     sequence = phoneme_to_sequence(text, text_cleaner, lang)
     text_hat = sequence_to_phoneme(sequence)
-    sequence_with_params = phoneme_to_sequence(text, text_cleaner, lang, tp=conf.characters)
+    _ = phoneme_to_sequence(text, text_cleaner, lang, tp=conf.characters)
     text_hat_with_params = sequence_to_phoneme(sequence, tp=conf.characters)
     gt = 'ɹiːsənt ɹᵻsɜːtʃ æt hɑːɹvɚd hɐz ʃoʊn mɛdᵻteɪɾɪŋ fɔːɹ æz lɪɾəl æz eɪt wiːks kæn æktʃuːəli ɪŋkɹiːs, ðə ɡɹeɪ mæɾɚɹ ɪnðə pɑːɹts ʌvðə bɹeɪn ɹᵻspɑːnsᵻbəl fɔːɹ ɪmoʊʃənəl ɹɛɡjʊleɪʃən ænd lɜːnɪŋ!'
     assert text_hat == text_hat_with_params == gt

tests/test_wavegrad_train.py

@@ -20,18 +20,18 @@ class WavegradTrainTest(unittest.TestCase):
 
         criterion = torch.nn.L1Loss().to(device)
         model = Wavegrad(in_channels=80,
-                     out_channels=1,
-                     upsample_factors=[5, 5, 3, 2, 2],
-                     upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2],
-                                         [1, 2, 4, 8], [1, 2, 4, 8],
-                                         [1, 2, 4, 8]])
+                         out_channels=1,
+                         upsample_factors=[5, 5, 3, 2, 2],
+                         upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2],
+                                             [1, 2, 4, 8], [1, 2, 4, 8],
+                                             [1, 2, 4, 8]])
 
         model_ref = Wavegrad(in_channels=80,
-                     out_channels=1,
-                     upsample_factors=[5, 5, 3, 2, 2],
-                     upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2],
-                                         [1, 2, 4, 8], [1, 2, 4, 8],
-                                         [1, 2, 4, 8]])
+                             out_channels=1,
+                             upsample_factors=[5, 5, 3, 2, 2],
+                             upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2],
+                                                 [1, 2, 4, 8], [1, 2, 4, 8],
+                                                 [1, 2, 4, 8]])
         model.train()
         model.to(device)
         betas = np.linspace(1e-6, 1e-2, 1000)