Update pylint 2.10.2 and fix lint issues

TTS/__init__.py

@@ -1,6 +1,6 @@
 import os
 
-with open(os.path.join(os.path.dirname(__file__), "VERSION")) as f:
+with open(os.path.join(os.path.dirname(__file__), "VERSION"), 'r', encoding='utf-8') as f:
     version = f.read().strip()
 
 __version__ = version

TTS/bin/compute_attention_masks.py

@@ -158,7 +158,7 @@
         # ourput metafile
         metafile = os.path.join(args.data_path, "metadata_attn_mask.txt")
 
-        with open(metafile, "w") as f:
+        with open(metafile, "w", encoding="utf-8") as f:
             for p in file_paths:
                 f.write(f"{p[0]}|{p[1]}\n")
         print(f" >> Metafile created: {metafile}")

TTS/bin/extract_tts_spectrograms.py

@@ -215,7 +215,7 @@ def extract_spectrograms(
                 wav = ap.inv_melspectrogram(mel)
                 ap.save_wav(wav, wav_gl_path)
 
-    with open(os.path.join(output_path, metada_name), "w") as f:
+    with open(os.path.join(output_path, metada_name), "w", encoding="utf-8") as f:
         for data in export_metadata:
             f.write(f"{data[0]}|{data[1]+'.npy'}\n")
 

TTS/model.py

@@ -23,35 +23,31 @@ class BaseModel(nn.Module, ABC):
     """
 
     @abstractmethod
-    def forward(self, text: torch.Tensor, aux_input={}, **kwargs) -> Dict:
+    def forward(self, input: torch.Tensor, *args, aux_input={}, **kwargs) -> Dict:
         """Forward pass for the model mainly used in training.
 
-        You can be flexible here and use different number of arguments and argument names since it is mostly used by
-        `train_step()` in training whitout exposing it to the out of the class.
+        You can be flexible here and use different number of arguments and argument names since it is intended to be
+        used by `train_step()` without exposing it out of the model.
 
         Args:
-            text (torch.Tensor): Input text character sequence ids.
+            input (torch.Tensor): Input tensor.
             aux_input (Dict): Auxiliary model inputs like embeddings, durations or any other sorts of inputs.
-                for the model.
 
         Returns:
-            Dict: model outputs. This must include an item keyed `model_outputs` as the final artifact of the model.
+            Dict: Model outputs. Main model output must be named as "model_outputs".
         """
         outputs_dict = {"model_outputs": None}
         ...
         return outputs_dict
 
     @abstractmethod
-    def inference(self, text: torch.Tensor, aux_input={}) -> Dict:
+    def inference(self, input: torch.Tensor, aux_input={}) -> Dict:
         """Forward pass for inference.
 
-        After the model is trained this is the only function that connects the model the out world.
-
-        This function must only take a `text` input and a dictionary that has all the other model specific inputs.
         We don't use `*kwargs` since it is problematic with the TorchScript API.
 
         Args:
-            text (torch.Tensor): [description]
+            input (torch.Tensor): [description]
             aux_input (Dict): Auxiliary inputs like speaker embeddings, durations etc.
 
         Returns:

TTS/speaker_encoder/losses.py

@@ -1,6 +1,6 @@
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
+from torch import nn
 
 
 # adapted from https://github.com/cvqluu/GE2E-Loss

TTS/speaker_encoder/models/resnet.py

@@ -1,6 +1,6 @@
 import numpy as np
 import torch
-import torch.nn as nn
+from torch import nn
 
 from TTS.utils.io import load_fsspec
 

TTS/speaker_encoder/utils/prepare_voxceleb.py

@@ -94,7 +94,8 @@ def download_and_extract(directory, subset, urls):
         extract_path = zip_filepath.strip(".zip")
 
         # check zip file md5sum
-        md5 = hashlib.md5(open(zip_filepath, "rb").read()).hexdigest()
+        with open(zip_filepath, "rb") as f_zip:
+            md5 = hashlib.md5(f_zip.read()).hexdigest()
         if md5 != MD5SUM[subset]:
             raise ValueError("md5sum of %s mismatch" % zip_filepath)
 

TTS/trainer.py

@@ -631,13 +631,13 @@ def train_step(self, batch: Dict, batch_n_steps: int, step: int, loader_start_ti
             outputs = outputs_per_optimizer
 
         # update avg runtime stats
-        keep_avg_update = dict()
+        keep_avg_update = {}
         keep_avg_update["avg_loader_time"] = loader_time
         keep_avg_update["avg_step_time"] = step_time
         self.keep_avg_train.update_values(keep_avg_update)
 
         # update avg loss stats
-        update_eval_values = dict()
+        update_eval_values = {}
         for key, value in loss_dict.items():
             update_eval_values["avg_" + key] = value
         self.keep_avg_train.update_values(update_eval_values)
@@ -797,7 +797,7 @@ def eval_step(self, batch: Dict, step: int) -> Tuple[Dict, Dict]:
                     loss_dict = self._detach_loss_dict(loss_dict)
 
             # update avg stats
-            update_eval_values = dict()
+            update_eval_values = {}
             for key, value in loss_dict.items():
                 update_eval_values["avg_" + key] = value
             self.keep_avg_eval.update_values(update_eval_values)
@@ -977,12 +977,13 @@ class Logger(object):
             def __init__(self, print_to_terminal=True):
                 self.print_to_terminal = print_to_terminal
                 self.terminal = sys.stdout
-                self.log = open(log_file, "a")
+                self.log_file = log_file
 
             def write(self, message):
                 if self.print_to_terminal:
                     self.terminal.write(message)
-                self.log.write(message)
+                with open(self.log_file, "a", encoding="utf-8") as f:
+                    f.write(message)
 
             def flush(self):
                 # this flush method is needed for python 3 compatibility.

TTS/tts/datasets/__init__.py

@@ -66,7 +66,7 @@ def load_meta_data(datasets, eval_split=True):
 
 def load_attention_mask_meta_data(metafile_path):
     """Load meta data file created by compute_attention_masks.py"""
-    with open(metafile_path, "r") as f:
+    with open(metafile_path, "r", encoding="utf-8") as f:
         lines = f.readlines()
 
     meta_data = []

TTS/tts/datasets/formatters.py

@@ -19,7 +19,7 @@ def tweb(root_path, meta_file):
     txt_file = os.path.join(root_path, meta_file)
     items = []
     speaker_name = "tweb"
-    with open(txt_file, "r") as ttf:
+    with open(txt_file, "r", encoding="utf-8") as ttf:
         for line in ttf:
             cols = line.split("\t")
             wav_file = os.path.join(root_path, cols[0] + ".wav")
@@ -33,7 +33,7 @@ def mozilla(root_path, meta_file):
     txt_file = os.path.join(root_path, meta_file)
     items = []
     speaker_name = "mozilla"
-    with open(txt_file, "r") as ttf:
+    with open(txt_file, "r", encoding="utf-8") as ttf:
         for line in ttf:
             cols = line.split("|")
             wav_file = cols[1].strip()
@@ -77,7 +77,7 @@ def mailabs(root_path, meta_files=None):
             continue
         speaker_name = speaker_name_match.group("speaker_name")
         print(" | > {}".format(csv_file))
-        with open(txt_file, "r") as ttf:
+        with open(txt_file, "r", encoding="utf-8") as ttf:
             for line in ttf:
                 cols = line.split("|")
                 if meta_files is None:
@@ -102,7 +102,7 @@ def ljspeech(root_path, meta_file):
         for line in ttf:
             cols = line.split("|")
             wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
-            text = cols[1]
+            text = cols[2]
             items.append([text, wav_file, speaker_name])
     return items
 
@@ -116,7 +116,7 @@ def ljspeech_test(root_path, meta_file):
         for idx, line in enumerate(ttf):
             cols = line.split("|")
             wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
-            text = cols[1]
+            text = cols[2]
             items.append([text, wav_file, f"ljspeech-{idx}"])
     return items
 
@@ -158,7 +158,7 @@ def css10(root_path, meta_file):
     txt_file = os.path.join(root_path, meta_file)
     items = []
     speaker_name = "ljspeech"
-    with open(txt_file, "r") as ttf:
+    with open(txt_file, "r", encoding="utf-8") as ttf:
         for line in ttf:
             cols = line.split("|")
             wav_file = os.path.join(root_path, cols[0])
@@ -172,7 +172,7 @@ def nancy(root_path, meta_file):
     txt_file = os.path.join(root_path, meta_file)
     items = []
     speaker_name = "nancy"
-    with open(txt_file, "r") as ttf:
+    with open(txt_file, "r", encoding="utf-8") as ttf:
         for line in ttf:
             utt_id = line.split()[1]
             text = line[line.find('"') + 1 : line.rfind('"') - 1]
@@ -185,7 +185,7 @@ def common_voice(root_path, meta_file):
     """Normalize the common voice meta data file to TTS format."""
     txt_file = os.path.join(root_path, meta_file)
     items = []
-    with open(txt_file, "r") as ttf:
+    with open(txt_file, "r", encoding="utf-8") as ttf:
         for line in ttf:
             if line.startswith("client_id"):
                 continue
@@ -208,7 +208,7 @@ def libri_tts(root_path, meta_files=None):
 
     for meta_file in meta_files:
         _meta_file = os.path.basename(meta_file).split(".")[0]
-        with open(meta_file, "r") as ttf:
+        with open(meta_file, "r", encoding="utf-8") as ttf:
             for line in ttf:
                 cols = line.split("\t")
                 file_name = cols[0]
@@ -245,7 +245,7 @@ def brspeech(root_path, meta_file):
     """BRSpeech 3.0 beta"""
     txt_file = os.path.join(root_path, meta_file)
     items = []
-    with open(txt_file, "r") as ttf:
+    with open(txt_file, "r", encoding="utf-8") as ttf:
         for line in ttf:
             if line.startswith("wav_filename"):
                 continue
@@ -268,7 +268,7 @@ def vctk(root_path, meta_files=None, wavs_path="wav48"):
         if isinstance(test_speakers, list):  # if is list ignore this speakers ids
             if speaker_id in test_speakers:
                 continue
-        with open(meta_file) as file_text:
+        with open(meta_file, "r", encoding="utf-8") as file_text:
             text = file_text.readlines()[0]
         wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id + ".wav")
         items.append([text, wav_file, "VCTK_" + speaker_id])
@@ -295,7 +295,7 @@ def vctk_slim(root_path, meta_files=None, wavs_path="wav48"):
 def mls(root_path, meta_files=None):
     """http://www.openslr.org/94/"""
     items = []
-    with open(os.path.join(root_path, meta_files), "r") as meta:
+    with open(os.path.join(root_path, meta_files), "r", encoding="utf-8") as meta:
         for line in meta:
             file, text = line.split("\t")
             text = text[:-1]
@@ -329,7 +329,7 @@ def _voxcel_x(root_path, meta_file, voxcel_idx):
 
     # if not exists meta file, crawl recursively for 'wav' files
     if meta_file is not None:
-        with open(str(meta_file), "r") as f:
+        with open(str(meta_file), "r", encoding="utf-8") as f:
             return [x.strip().split("|") for x in f.readlines()]
 
     elif not cache_to.exists():
@@ -346,12 +346,12 @@ def _voxcel_x(root_path, meta_file, voxcel_idx):
             text = None  # VoxCel does not provide transciptions, and they are not needed for training the SE
             meta_data.append(f"{text}|{path}|voxcel{voxcel_idx}_{speaker_id}\n")
             cnt += 1
-        with open(str(cache_to), "w") as f:
+        with open(str(cache_to), "w", encoding="utf-8") as f:
             f.write("".join(meta_data))
         if cnt < expected_count:
             raise ValueError(f"Found too few instances for Voxceleb. Should be around {expected_count}, is: {cnt}")
 
-    with open(str(cache_to), "r") as f:
+    with open(str(cache_to), "r", encoding="utf-8") as f:
         return [x.strip().split("|") for x in f.readlines()]
 
 
@@ -367,7 +367,7 @@ def baker(root_path: str, meta_file: str) -> List[List[str]]:
     txt_file = os.path.join(root_path, meta_file)
     items = []
     speaker_name = "baker"
-    with open(txt_file, "r") as ttf:
+    with open(txt_file, "r", encoding="utf-8") as ttf:
         for line in ttf:
             wav_name, text = line.rstrip("\n").split("|")
             wav_path = os.path.join(root_path, "clips_22", wav_name)
@@ -380,7 +380,7 @@ def kokoro(root_path, meta_file):
     txt_file = os.path.join(root_path, meta_file)
     items = []
     speaker_name = "kokoro"
-    with open(txt_file, "r") as ttf:
+    with open(txt_file, "r", encoding="utf-8") as ttf:
         for line in ttf:
             cols = line.split("|")
             wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")

TTS/tts/layers/generic/transformer.py

@@ -1,6 +1,6 @@
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
+from torch import nn
 
 
 class FFTransformer(nn.Module):

TTS/tts/layers/tacotron/gst_layers.py

@@ -1,6 +1,6 @@
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
+from torch import nn
 
 
 class GST(nn.Module):

TTS/tts/layers/tacotron/tacotron.py

@@ -388,8 +388,8 @@ def decode(self, inputs, mask=None):
         decoder_input = self.project_to_decoder_in(torch.cat((self.attention_rnn_hidden, self.context_vec), -1))
 
         # Pass through the decoder RNNs
-        for idx in range(len(self.decoder_rnns)):
-            self.decoder_rnn_hiddens[idx] = self.decoder_rnns[idx](decoder_input, self.decoder_rnn_hiddens[idx])
+        for idx, decoder_rnn in enumerate(self.decoder_rnns):
+            self.decoder_rnn_hiddens[idx] = decoder_rnn(decoder_input, self.decoder_rnn_hiddens[idx])
             # Residual connection
             decoder_input = self.decoder_rnn_hiddens[idx] + decoder_input
         decoder_output = decoder_input

TTS/tts/models/align_tts.py

@@ -2,8 +2,8 @@
 from typing import Dict, Tuple
 
 import torch
-import torch.nn as nn
 from coqpit import Coqpit
+from torch import nn
 
 from TTS.tts.layers.align_tts.mdn import MDNBlock
 from TTS.tts.layers.feed_forward.decoder import Decoder

TTS/tts/models/vits.py

@@ -435,7 +435,7 @@ def forward(
                 attn_durations,
                 g=g.detach() if self.args.detach_dp_input and g is not None else g,
             )
-            loss_duration = loss_duration/ torch.sum(x_mask)
+            loss_duration = loss_duration / torch.sum(x_mask)
         else:
             attn_log_durations = torch.log(attn_durations + 1e-6) * x_mask
             log_durations = self.duration_predictor(
@@ -579,7 +579,7 @@ def train_step(self, batch: dict, criterion: nn.Module, optimizer_idx: int) -> T
                     scores_disc_fake=outputs["scores_disc_fake"],
                     feats_disc_fake=outputs["feats_disc_fake"],
                     feats_disc_real=outputs["feats_disc_real"],
-                    loss_duration=outputs["loss_duration"]
+                    loss_duration=outputs["loss_duration"],
                 )
 
         elif optimizer_idx == 1:

TTS/utils/distribute.py

@@ -18,46 +18,3 @@ def init_distributed(rank, num_gpus, group_name, dist_backend, dist_url):
 
     # Initialize distributed communication
     dist.init_process_group(dist_backend, init_method=dist_url, world_size=num_gpus, rank=rank, group_name=group_name)
-
-
-def apply_gradient_allreduce(module):
-
-    # sync model parameters
-    for p in module.state_dict().values():
-        if not torch.is_tensor(p):
-            continue
-        dist.broadcast(p, 0)
-
-    def allreduce_params():
-        if module.needs_reduction:
-            module.needs_reduction = False
-            # bucketing params based on value types
-            buckets = {}
-            for param in module.parameters():
-                if param.requires_grad and param.grad is not None:
-                    tp = type(param.data)
-                    if tp not in buckets:
-                        buckets[tp] = []
-                    buckets[tp].append(param)
-            for tp in buckets:
-                bucket = buckets[tp]
-                grads = [param.grad.data for param in bucket]
-                coalesced = _flatten_dense_tensors(grads)
-                dist.all_reduce(coalesced, op=dist.reduce_op.SUM)
-                coalesced /= dist.get_world_size()
-                for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
-                    buf.copy_(synced)
-
-    for param in list(module.parameters()):
-
-        def allreduce_hook(*_):
-            Variable._execution_engine.queue_callback(allreduce_params)  # pylint: disable=protected-access
-
-        if param.requires_grad:
-            param.register_hook(allreduce_hook)
-
-    def set_needs_reduction(self, *_):
-        self.needs_reduction = True
-
-    module.register_forward_hook(set_needs_reduction)
-    return module