linter fixes

TTS/bin/find_unique_chars.py

@@ -7,16 +7,15 @@ from TTS.tts.datasets.preprocess import get_preprocessor_by_name
 
 
 def main():
+    # pylint: disable=bad-continuation
     parser = argparse.ArgumentParser(description='''Find all the unique characters or phonemes in a dataset.\n\n'''
 
     '''Target dataset must be defined in TTS.tts.datasets.preprocess\n\n'''\
-
     '''
     Example runs:
 
     python TTS/bin/find_unique_chars.py --dataset ljspeech --meta_file /path/to/LJSpeech/metadata.csv
-    ''',
-        formatter_class=RawTextHelpFormatter)
+    ''', formatter_class=RawTextHelpFormatter)
 
     parser.add_argument(
         '--dataset',

TTS/utils/arguments.py

@@ -6,15 +6,13 @@ import argparse
 import glob
 import os
 import re
-import json
 
 from TTS.tts.utils.generic_utils import check_config_tts
+from TTS.tts.utils.text.symbols import parse_symbols
 from TTS.utils.console_logger import ConsoleLogger
 from TTS.utils.generic_utils import create_experiment_folder, get_git_branch
-from TTS.utils.io import (copy_model_files, load_config,
-                          save_characters_to_config)
+from TTS.utils.io import copy_model_files, load_config
 from TTS.utils.tensorboard_logger import TensorboardLogger
-from TTS.tts.utils.text.symbols import parse_symbols
 
 
 def parse_arguments(argv):

TTS/utils/manage.py

@@ -122,7 +122,8 @@ class ModelManager(object):
         """Download files from GDrive using their file ids"""
         gdown.download(f"{self.url_prefix}{gdrive_idx}", output=output, quiet=False)
 
-    def _download_zip_file(self, file_url, output):
+    @staticmethod
+    def _download_zip_file(file_url, output):
         """Download the target zip file and extract the files
         to a folder with the same name as the zip file."""
         r = requests.get(file_url)

hubconf.py

@@ -1,11 +1,11 @@
-dependencies = ['torch', 'gdown', 'pysbd', 'phonemizer', 'unidecode']  # apt install espeak
+dependencies = ['torch', 'gdown', 'pysbd', 'phonemizer', 'unidecode']  # apt install espeak-ng
 import torch
 
 from TTS.utils.synthesizer import Synthesizer
 from TTS.utils.manage import ModelManager
 
 
-def tts(model_name='tts_models/en/ljspeech/tacotron2-DCA', vocoder_name='vocoder_models/en/ljspeech/mulitband-melgan', use_cuda=False):
+def tts(model_name='tts_models/en/ljspeech/tacotron2-DCA', vocoder_name=None, use_cuda=False):
     """TTS entry point for PyTorch Hub that provides a Synthesizer object to synthesize speech from a give text.
 
     Example:
@@ -15,7 +15,7 @@ def tts(model_name='tts_models/en/ljspeech/tacotron2-DCA', vocoder_name='vocoder
 
     Args:
         model_name (str, optional): One of the model names from .model.json. Defaults to 'tts_models/en/ljspeech/tacotron2-DCA'.
-        vocoder_name (str, optional): One of the model names from .model.json. Defaults to 'vocoder_models/en/ljspeech/mulitband-melgan'.
+        vocoder_name (str, optional): One of the model names from .model.json. Defaults to 'vocoder_models/en/ljspeech/multiband-melgan'.
         pretrained (bool, optional): [description]. Defaults to True.
 
     Returns:
@@ -23,8 +23,9 @@ def tts(model_name='tts_models/en/ljspeech/tacotron2-DCA', vocoder_name='vocoder
     """
     manager = ModelManager()
 
-    model_path, config_path = manager.download_model(model_name)
-    vocoder_path, vocoder_config_path = manager.download_model(vocoder_name)
+    model_path, config_path, model_item = manager.download_model(model_name)
+    vocoder_name = model_item['default_vocoder'] if vocoder_name is None else vocoder_name
+    vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)
 
     # create synthesizer
     synt = Synthesizer(model_path, config_path, vocoder_path, vocoder_config_path, use_cuda)

tests/test_demo_server.py

@@ -21,7 +21,7 @@ class DemoServerTest(unittest.TestCase):
         num_chars = len(phonemes) if config.use_phonemes else len(symbols)
         model = setup_model(num_chars, 0, config)
         output_path = os.path.join(get_tests_output_path())
-        save_checkpoint(model, None, 10, 10, 1, output_path)
+        save_checkpoint(model, None, 10, 10, 1, output_path, None)
 
     def test_in_out(self):
         self._create_random_model()