linter fixes

TTS/bin/train_tts.py

@@ -8,20 +8,20 @@ from TTS.utils.generic_utils import remove_experiment_folder
 
 
 def main():
-    # try:
-    args, config, OUT_PATH, AUDIO_PATH, c_logger, tb_logger = init_training(sys.argv)
-    trainer = TrainerTTS(args, config, c_logger, tb_logger, output_path=OUT_PATH)
-    trainer.fit()
-    # except KeyboardInterrupt:
-    #     remove_experiment_folder(OUT_PATH)
-    #     try:
-    #         sys.exit(0)
-    #     except SystemExit:
-    #         os._exit(0)  # pylint: disable=protected-access
-    # except Exception:  # pylint: disable=broad-except
-    #     remove_experiment_folder(OUT_PATH)
-    #     traceback.print_exc()
-    #     sys.exit(1)
+    try:
+        args, config, output_path, _, c_logger, tb_logger = init_training(sys.argv)
+        trainer = TrainerTTS(args, config, c_logger, tb_logger, output_path=output_path)
+        trainer.fit()
+    except KeyboardInterrupt:
+        remove_experiment_folder(output_path)
+        try:
+            sys.exit(0)
+        except SystemExit:
+            os._exit(0)  # pylint: disable=protected-access
+    except Exception:  # pylint: disable=broad-except
+        remove_experiment_folder(output_path)
+        traceback.print_exc()
+        sys.exit(1)
 
 
 if __name__ == "__main__":

TTS/trainer.py

@@ -184,7 +184,7 @@ class TrainerTTS:
 
     @staticmethod
     def get_speaker_manager(
-        config: Coqpit, restore_path: str = "", out_path: str = "", data_train: List = []
+        config: Coqpit, restore_path: str = "", out_path: str = "", data_train: List = None
     ) -> SpeakerManager:
         speaker_manager = SpeakerManager()
             if restore_path:
@@ -208,7 +208,9 @@ class TrainerTTS:
         return speaker_manager
 
     @staticmethod
-    def get_scheduler(config: Coqpit, optimizer: torch.optim.Optimizer) -> torch.optim.lr_scheduler._LRScheduler:
+    def get_scheduler(
+        config: Coqpit, optimizer: torch.optim.Optimizer
+    ) -> torch.optim.lr_scheduler._LRScheduler:  # pylint: disable=protected-access
         lr_scheduler = config.lr_scheduler
         lr_scheduler_params = config.lr_scheduler_params
         if lr_scheduler is None:

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)
+        x_lengths = torch.tensor(x.shape[1:2]).to(x.device)  # pylint: disable=not-callable
         # 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)
@@ -314,7 +314,7 @@ class AlignTTS(nn.Module):
         loss_dict["align_error"] = align_error
         return outputs, loss_dict
 
-    def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict):
+    def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict):  # pylint: disable=no-self-use
         model_outputs = outputs["model_outputs"]
         alignments = outputs["alignments"]
         mel_input = batch["mel_input"]

TTS/tts/models/glow_tts.py

@@ -143,7 +143,9 @@ class GlowTTS(nn.Module):
         o_attn_dur = torch.log(1 + torch.sum(attn, -1)) * x_mask
         return y_mean, y_log_scale, o_attn_dur
 
-    def forward(self, x, x_lengths, y, y_lengths=None, cond_input={"x_vectors": None}):
+    def forward(
+        self, x, x_lengths, y, y_lengths=None, cond_input={"x_vectors": None}
+    ):  # pylint: disable=dangerous-default-value
         """
         Shapes:
             x: [B, T]
@@ -344,7 +346,7 @@ class GlowTTS(nn.Module):
         loss_dict["align_error"] = align_error
         return outputs, loss_dict
 
-    def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict):
+    def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict):  # pylint: disable=no-self-use
         model_outputs = outputs["model_outputs"]
         alignments = outputs["alignments"]
         mel_input = batch["mel_input"]

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)
+        x_lengths = torch.tensor(x.shape[1:2]).to(x.device)  # pylint: disable=not-callable
         # input sequence should be greated than the max convolution size
         inference_padding = 5
         if x.shape[1] < 13:
@@ -226,7 +226,7 @@ class SpeedySpeech(nn.Module):
         loss_dict["align_error"] = align_error
         return outputs, loss_dict
 
-    def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict):
+    def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict):  # pylint: disable=no-self-use
         model_outputs = outputs["model_outputs"]
         alignments = outputs["alignments"]
         mel_input = batch["mel_input"]

TTS/tts/models/tacotron.py

@@ -79,7 +79,7 @@ class Tacotron(TacotronAbstract):
         use_gst=False,
         gst=None,
         memory_size=5,
-        gradual_training=[],
+        gradual_training=None,
     ):
         super().__init__(
             num_chars,

TTS/tts/models/tacotron2.py

@@ -1,5 +1,4 @@
 # coding: utf-8
-import numpy as np
 import torch
 from torch import nn
 
@@ -77,7 +76,7 @@ class Tacotron2(TacotronAbstract):
         speaker_embedding_dim=None,
         use_gst=False,
         gst=None,
-        gradual_training=[],
+        gradual_training=None,
     ):
         super().__init__(
             num_chars,

TTS/tts/models/tacotron_abstract.py

@@ -1,5 +1,4 @@
 import copy
-import logging
 from abc import ABC, abstractmethod
 
 import torch
@@ -37,7 +36,7 @@ class TacotronAbstract(ABC, nn.Module):
         speaker_embedding_dim=None,
         use_gst=False,
         gst=None,
-        gradual_training=[],
+        gradual_training=None,
     ):
         """Abstract Tacotron class"""
         super().__init__()
@@ -239,4 +238,4 @@ class TacotronAbstract(ABC, nn.Module):
                 trainer.model.decoder_backward.set_r(r)
             trainer.train_loader = trainer.setup_train_dataloader(self.ap, self.model.decoder.r, verbose=True)
             trainer.eval_loader = trainer.setup_eval_dataloder(self.ap, self.model.decoder.r)
-            logging.info(f"\n > Number of output frames: {self.decoder.r}")
+            print(f"\n > Number of output frames: {self.decoder.r}")

TTS/tts/utils/speakers.py

@@ -1,5 +1,4 @@
 import json
-import os
 import random
 from typing import Any, List, Union
 
@@ -11,79 +10,6 @@ from TTS.speaker_encoder.utils.generic_utils import setup_model
 from TTS.utils.audio import AudioProcessor
 
 
-def make_speakers_json_path(out_path):
-    """Returns conventional speakers.json location."""
-    return os.path.join(out_path, "speakers.json")
-
-
-def load_speaker_mapping(out_path):
-    """Loads speaker mapping if already present."""
-    if os.path.splitext(out_path)[1] == ".json":
-        json_file = out_path
-    else:
-        json_file = make_speakers_json_path(out_path)
-    with open(json_file) as f:
-        return json.load(f)
-
-
-def save_speaker_mapping(out_path, speaker_mapping):
-    """Saves speaker mapping if not yet present."""
-    if out_path is not None:
-        speakers_json_path = make_speakers_json_path(out_path)
-        with open(speakers_json_path, "w") as f:
-            json.dump(speaker_mapping, f, indent=4)
-
-
-def parse_speakers(c, args, meta_data_train, OUT_PATH):
-    """Returns number of speakers, speaker embedding shape and speaker mapping"""
-    if c.use_speaker_embedding:
-        speakers = get_speakers(meta_data_train)
-        if args.restore_path:
-            if c.use_external_speaker_embedding_file:  # if restore checkpoint and use External Embedding file
-                prev_out_path = os.path.dirname(args.restore_path)
-                speaker_mapping = load_speaker_mapping(prev_out_path)
-                if not speaker_mapping:
-                    print(
-                        "WARNING: speakers.json was not found in restore_path, trying to use CONFIG.external_speaker_embedding_file"
-                    )
-                    speaker_mapping = load_speaker_mapping(c.external_speaker_embedding_file)
-                    if not speaker_mapping:
-                        raise RuntimeError(
-                            "You must copy the file speakers.json to restore_path, or set a valid file in CONFIG.external_speaker_embedding_file"
-                        )
-                speaker_embedding_dim = len(speaker_mapping[list(speaker_mapping.keys())[0]]["embedding"])
-            elif (
-                not c.use_external_speaker_embedding_file
-            ):  # if restore checkpoint and don't use External Embedding file
-                prev_out_path = os.path.dirname(args.restore_path)
-                speaker_mapping = load_speaker_mapping(prev_out_path)
-                speaker_embedding_dim = None
-                assert all(speaker in speaker_mapping for speaker in speakers), (
-                    "As of now you, you cannot " "introduce new speakers to " "a previously trained model."
-                )
-        elif (
-            c.use_external_speaker_embedding_file and c.external_speaker_embedding_file
-        ):  # if start new train using External Embedding file
-            speaker_mapping = load_speaker_mapping(c.external_speaker_embedding_file)
-            speaker_embedding_dim = len(speaker_mapping[list(speaker_mapping.keys())[0]]["embedding"])
-        elif (
-            c.use_external_speaker_embedding_file and not c.external_speaker_embedding_file
-        ):  # if start new train using External Embedding file and don't pass external embedding file
-            raise "use_external_speaker_embedding_file is True, so you need pass a external speaker embedding file, run GE2E-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb or AngularPrototypical-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb notebook in notebooks/ folder"
-        else:  # if start new train and don't use External Embedding file
-            speaker_mapping = {name: i for i, name in enumerate(speakers)}
-            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)))
-    else:
-        num_speakers = 0
-        speaker_embedding_dim = None
-        speaker_mapping = None
-
-    return num_speakers, speaker_embedding_dim, speaker_mapping
-
-
 class SpeakerManager:
     """It manages the multi-speaker setup for 🐸TTS models. It loads the speaker files and parses the information
     in a way that you can query. There are 3 different scenarios considered.

TTS/tts/utils/synthesis.py

@@ -230,15 +230,16 @@ def synthesis(
         outputs = run_model_torch(model, text_inputs, speaker_id, style_mel, x_vector=x_vector)
         model_outputs = outputs["model_outputs"]
         model_outputs = model_outputs[0].data.cpu().numpy()
+        alignments = outputs["alignments"]
     elif backend == "tf":
         decoder_output, postnet_output, alignments, stop_tokens = run_model_tf(
             model, text_inputs, CONFIG, speaker_id, style_mel
         )
-        model_outputs, decoder_output, alignment, stop_tokens = parse_outputs_tf(
+        model_outputs, decoder_output, alignments, stop_tokens = parse_outputs_tf(
             postnet_output, decoder_output, alignments, stop_tokens
         )
     elif backend == "tflite":
-        decoder_output, postnet_output, alignment, stop_tokens = run_model_tflite(
+        decoder_output, postnet_output, alignments, stop_tokens = run_model_tflite(
             model, text_inputs, CONFIG, speaker_id, style_mel
         )
         model_outputs, decoder_output = parse_outputs_tflite(postnet_output, decoder_output)
@@ -252,7 +253,7 @@ def synthesis(
             wav = trim_silence(wav, ap)
     return_dict = {
         "wav": wav,
-        "alignments": outputs["alignments"],
+        "alignments": alignments,
         "model_outputs": model_outputs,
         "text_inputs": text_inputs,
     }