formatting

TTS/speaker_encoder/utils/prepare_voxceleb.py

@@ -29,7 +29,6 @@ import pandas
 import soundfile as sf
 from absl import logging
 
-
 SUBSETS = {
     "vox1_dev_wav": [
         "http://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partaa",
@@ -191,7 +190,7 @@ def convert_audio_and_make_label(input_dir, subset, output_dir, output_file):
 
 
 def processor(directory, subset, force_process):
-    """ download and process """
+    """download and process"""
     urls = SUBSETS
     if subset not in urls:
         raise ValueError(subset, "is not in voxceleb")

TTS/tts/layers/generic/transformer.py

@@ -18,7 +18,7 @@ class FFTransformer(nn.Module):
         self.dropout = nn.Dropout(dropout_p)
 
     def forward(self, src, src_mask=None, src_key_padding_mask=None):
-        """😦 ugly looking with all the transposing """
+        """😦 ugly looking with all the transposing"""
         src = src.permute(2, 0, 1)
         src2, enc_align = self.self_attn(src, src, src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)
         src = self.norm1(src + src2)

TTS/tts/models/tacotron_abstract.py

@@ -39,7 +39,7 @@ class TacotronAbstract(ABC, nn.Module):
         gst_style_tokens=10,
         gst_use_speaker_embedding=False,
     ):
-        """ Abstract Tacotron class """
+        """Abstract Tacotron class"""
         super().__init__()
         self.num_chars = num_chars
         self.r = r
@@ -153,7 +153,7 @@ class TacotronAbstract(ABC, nn.Module):
         return input_mask, output_mask
 
     def _backward_pass(self, mel_specs, encoder_outputs, mask):
-        """ Run backwards decoder """
+        """Run backwards decoder"""
         decoder_outputs_b, alignments_b, _ = self.decoder_backward(
             encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask
         )
@@ -161,7 +161,7 @@ class TacotronAbstract(ABC, nn.Module):
         return decoder_outputs_b, alignments_b
 
     def _coarse_decoder_pass(self, mel_specs, encoder_outputs, alignments, input_mask):
-        """ Double Decoder Consistency """
+        """Double Decoder Consistency"""
         T = mel_specs.shape[1]
         if T % self.coarse_decoder.r > 0:
             padding_size = self.coarse_decoder.r - (T % self.coarse_decoder.r)
@@ -182,7 +182,7 @@ class TacotronAbstract(ABC, nn.Module):
     #############################
 
     def compute_speaker_embedding(self, speaker_ids):
-        """ Compute speaker embedding vectors """
+        """Compute speaker embedding vectors"""
         if hasattr(self, "speaker_embedding") and speaker_ids is None:
             raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
         if hasattr(self, "speaker_embedding") and speaker_ids is not None:
@@ -191,7 +191,7 @@ class TacotronAbstract(ABC, nn.Module):
             self.speaker_embeddings_projected = self.speaker_project_mel(self.speaker_embeddings).squeeze(1)
 
     def compute_gst(self, inputs, style_input, speaker_embedding=None):
-        """ Compute global style token """
+        """Compute global style token"""
         device = inputs.device
         if isinstance(style_input, dict):
             query = torch.zeros(1, 1, self.gst_embedding_dim // 2).to(device)

TTS/tts/tf/layers/tacotron/common_layers.py

@@ -140,7 +140,7 @@ class Attention(keras.layers.Layer):
         return tuple(states)
 
     def process_values(self, values):
-        """ cache values for decoder iterations """
+        """cache values for decoder iterations"""
         # pylint: disable=attribute-defined-outside-init
         self.processed_values = self.inputs_layer(values)
         self.values = values
@@ -158,14 +158,14 @@ class Attention(keras.layers.Layer):
         return score, processed_query
 
     def get_attn(self, query):
-        """ compute query layer and unnormalized attention weights """
+        """compute query layer and unnormalized attention weights"""
         processed_query = self.query_layer(tf.expand_dims(query, 1))
         score = self.v(tf.nn.tanh(self.processed_values + processed_query))
         score = tf.squeeze(score, axis=2)
         return score, processed_query
 
     def apply_score_masking(self, score, mask):  # pylint: disable=no-self-use
-        """ ignore sequence paddings """
+        """ignore sequence paddings"""
         padding_mask = tf.expand_dims(math_ops.logical_not(mask), 2)
         # Bias so padding positions do not contribute to attention distribution.
         score -= 1.0e9 * math_ops.cast(padding_mask, dtype=tf.float32)

TTS/tts/tf/utils/convert_torch_to_tf_utils.py

@@ -7,7 +7,7 @@ import tensorflow as tf
 
 
 def tf_create_dummy_inputs():
-    """ Create dummy inputs for TF Tacotron2 model """
+    """Create dummy inputs for TF Tacotron2 model"""
     batch_size = 4
     max_input_length = 32
     max_mel_length = 128
@@ -25,12 +25,12 @@ def tf_create_dummy_inputs():
 
 
 def compare_torch_tf(torch_tensor, tf_tensor):
-    """ Compute the average absolute difference b/w torch and tf tensors """
+    """Compute the average absolute difference b/w torch and tf tensors"""
     return abs(torch_tensor.detach().numpy() - tf_tensor.numpy()).mean()
 
 
 def convert_tf_name(tf_name):
-    """ Convert certain patterns in TF layer names to Torch patterns """
+    """Convert certain patterns in TF layer names to Torch patterns"""
     tf_name_tmp = tf_name
     tf_name_tmp = tf_name_tmp.replace(":0", "")
     tf_name_tmp = tf_name_tmp.replace("/forward_lstm/lstm_cell_1/recurrent_kernel", "/weight_hh_l0")
@@ -44,7 +44,7 @@ def convert_tf_name(tf_name):
 
 
 def transfer_weights_torch_to_tf(tf_vars, var_map_dict, state_dict):
-    """ Transfer weigths from torch state_dict to TF variables """
+    """Transfer weigths from torch state_dict to TF variables"""
     print(" > Passing weights from Torch to TF ...")
     for tf_var in tf_vars:
         torch_var_name = var_map_dict[tf_var.name]

TTS/tts/utils/data.py

@@ -33,7 +33,7 @@ def _pad_stop_target(x, length):
 
 
 def prepare_stop_target(inputs, out_steps):
-    """ Pad row vectors with 1. """
+    """Pad row vectors with 1."""
     max_len = max((x.shape[0] for x in inputs))
     remainder = max_len % out_steps
     pad_len = max_len + (out_steps - remainder) if remainder > 0 else max_len

TTS/tts/utils/speakers.py

@@ -40,7 +40,7 @@ def get_speakers(items):
 
 
 def parse_speakers(c, args, meta_data_train, OUT_PATH):
-    """ Returns number of speakers, speaker embedding shape and speaker mapping"""
+    """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:

TTS/utils/audio.py

@@ -366,7 +366,7 @@ class AudioProcessor(object):
         return len(wav)
 
     def trim_silence(self, wav):
-        """ Trim silent parts with a threshold and 0.01 sec margin """
+        """Trim silent parts with a threshold and 0.01 sec margin"""
         margin = int(self.sample_rate * 0.01)
         wav = wav[margin:-margin]
         return librosa.effects.trim(wav, top_db=self.trim_db, frame_length=self.win_length, hop_length=self.hop_length)[

TTS/utils/generic_utils.py

@@ -37,7 +37,7 @@ def get_commit_hash():
 
 
 def create_experiment_folder(root_path, model_name, debug):
-    """ Create a folder with the current date and time """
+    """Create a folder with the current date and time"""
     date_str = datetime.datetime.now().strftime("%B-%d-%Y_%I+%M%p")
     if debug:
         commit_hash = "debug"

TTS/vocoder/datasets/gan_dataset.py

@@ -105,7 +105,7 @@ class GANDataset(Dataset):
         random.shuffle(self.G_to_D_mappings)
 
     def load_item(self, idx):
-        """ load (audio, feat) couple """
+        """load (audio, feat) couple"""
         if self.compute_feat:
             # compute features from wav
             wavpath = self.item_list[idx]

TTS/vocoder/datasets/wavegrad_dataset.py

@@ -78,7 +78,7 @@ class WaveGradDataset(Dataset):
         return samples
 
     def load_item(self, idx):
-        """ load (audio, feat) couple """
+        """load (audio, feat) couple"""
         # compute features from wav
         wavpath = self.item_list[idx]
 

TTS/vocoder/layers/losses.py

@@ -131,7 +131,7 @@ class MultiScaleSTFTLoss(torch.nn.Module):
 
 
 class L1SpecLoss(nn.Module):
-    """ L1 Loss over Spectrograms as described in HiFiGAN paper https://arxiv.org/pdf/2010.05646.pdf"""
+    """L1 Loss over Spectrograms as described in HiFiGAN paper https://arxiv.org/pdf/2010.05646.pdf"""
 
     def __init__(
         self, sample_rate, n_fft, hop_length, win_length, mel_fmin=None, mel_fmax=None, n_mels=None, use_mel=True
@@ -169,7 +169,7 @@ class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
 
 
 class MSEGLoss(nn.Module):
-    """ Mean Squared Generator Loss """
+    """Mean Squared Generator Loss"""
 
     # pylint: disable=no-self-use
     def forward(self, score_real):
@@ -178,7 +178,7 @@ class MSEGLoss(nn.Module):
 
 
 class HingeGLoss(nn.Module):
-    """ Hinge Discriminator Loss """
+    """Hinge Discriminator Loss"""
 
     # pylint: disable=no-self-use
     def forward(self, score_real):
@@ -193,7 +193,7 @@ class HingeGLoss(nn.Module):
 
 
 class MSEDLoss(nn.Module):
-    """ Mean Squared Discriminator Loss """
+    """Mean Squared Discriminator Loss"""
 
     def __init__(
         self,
@@ -210,7 +210,7 @@ class MSEDLoss(nn.Module):
 
 
 class HingeDLoss(nn.Module):
-    """ Hinge Discriminator Loss """
+    """Hinge Discriminator Loss"""
 
     # pylint: disable=no-self-use
     def forward(self, score_fake, score_real):
@@ -260,7 +260,7 @@ def _apply_G_adv_loss(scores_fake, loss_func):
 
 
 def _apply_D_loss(scores_fake, scores_real, loss_func):
-    """ Compute D loss func and normalize loss values """
+    """Compute D loss func and normalize loss values"""
     loss = 0
     real_loss = 0
     fake_loss = 0

TTS/vocoder/models/wavegrad.py

@@ -84,7 +84,7 @@ class Wavegrad(nn.Module):
 
     @torch.no_grad()
     def inference(self, x, y_n=None):
-        """ x: B x D X T """
+        """x: B x D X T"""
         if y_n is None:
             y_n = torch.randn(x.shape[0], 1, self.hop_len * x.shape[-1], dtype=torch.float32).to(x)
         else:

TTS/vocoder/tf/utils/convert_torch_to_tf_utils.py

@@ -3,12 +3,12 @@ import tensorflow as tf
 
 
 def compare_torch_tf(torch_tensor, tf_tensor):
-    """ Compute the average absolute difference b/w torch and tf tensors """
+    """Compute the average absolute difference b/w torch and tf tensors"""
     return abs(torch_tensor.detach().numpy() - tf_tensor.numpy()).mean()
 
 
 def convert_tf_name(tf_name):
-    """ Convert certain patterns in TF layer names to Torch patterns """
+    """Convert certain patterns in TF layer names to Torch patterns"""
     tf_name_tmp = tf_name
     tf_name_tmp = tf_name_tmp.replace(":0", "")
     tf_name_tmp = tf_name_tmp.replace("/forward_lstm/lstm_cell_1/recurrent_kernel", "/weight_hh_l0")
@@ -22,7 +22,7 @@ def convert_tf_name(tf_name):
 
 
 def transfer_weights_torch_to_tf(tf_vars, var_map_dict, state_dict):
-    """ Transfer weigths from torch state_dict to TF variables """
+    """Transfer weigths from torch state_dict to TF variables"""
     print(" > Passing weights from Torch to TF ...")
     for tf_var in tf_vars:
         torch_var_name = var_map_dict[tf_var.name]

TTS/vocoder/tf/utils/io.py

@@ -5,7 +5,7 @@ import tensorflow as tf
 
 
 def save_checkpoint(model, current_step, epoch, output_path, **kwargs):
-    """ Save TF Vocoder model """
+    """Save TF Vocoder model"""
     state = {
         "model": model.weights,
         "step": current_step,
@@ -17,7 +17,7 @@ def save_checkpoint(model, current_step, epoch, output_path, **kwargs):
 
 
 def load_checkpoint(model, checkpoint_path):
-    """ Load TF Vocoder model """
+    """Load TF Vocoder model"""
     checkpoint = pickle.load(open(checkpoint_path, "rb"))
     chkp_var_dict = {var.name: var.numpy() for var in checkpoint["model"]}
     tf_vars = model.weights

TTS/vocoder/utils/distribution.py

@@ -31,7 +31,7 @@ def sample_from_gaussian(y_hat, log_std_min=-7.0, scale_factor=1.0):
 
 
 def log_sum_exp(x):
-    """ numerically stable log_sum_exp implementation that prevents overflow """
+    """numerically stable log_sum_exp implementation that prevents overflow"""
     # TF ordering
     axis = len(x.size()) - 1
     m, _ = torch.max(x, dim=axis)

TTS/vocoder/utils/generic_utils.py

@@ -30,7 +30,7 @@ def interpolate_vocoder_input(scale_factor, spec):
 
 
 def plot_results(y_hat, y, ap, global_step, name_prefix):
-    """ Plot vocoder model results """
+    """Plot vocoder model results"""
 
     # select an instance from batch
     y_hat = y_hat[0].squeeze(0).detach().cpu().numpy()

tests/model_manager.py

@@ -1,7 +1,8 @@
 #!/usr/bin/env python3`
+import glob
 import os
 import shutil
-import glob
+
 from tests import get_tests_output_path
 from TTS.utils.manage import ModelManager
 
@@ -15,6 +16,6 @@ def test_if_all_models_available():
         manager.download_model(model_name)
         print(f" | > OK: {model_name}")
 
-    folders = glob.glob(os.path.join(manager.output_prefix, '*'))
+    folders = glob.glob(os.path.join(manager.output_prefix, "*"))
     assert len(folders) == len(model_names)
     shutil.rmtree(manager.output_prefix)

tests/test_audio.py

@@ -55,7 +55,7 @@ class TestAudio(unittest.TestCase):
         _test(4.0, True, True, True)
 
     def test_normalize(self):
-        """Check normalization and denormalization for range values and consistency """
+        """Check normalization and denormalization for range values and consistency"""
         print(" > Testing normalization and denormalization.")
         wav = self.ap.load_wav(WAV_FILE)
         wav = self.ap.sound_norm(wav)  # normalize audio to get abetter normalization range below.

tests/test_tacotron2_tf_model.py

@@ -40,7 +40,7 @@ class TacotronTFTrainTest(unittest.TestCase):
         return chars_seq, chars_seq_lengths, mel_spec, mel_postnet_spec, mel_lengths, stop_targets, speaker_ids
 
     def test_train_step(self):
-        """ test forward pass """
+        """test forward pass"""
         (
             chars_seq,
             chars_seq_lengths,

tests/test_vocoder_gan_datasets.py

@@ -22,7 +22,7 @@ ok_ljspeech = os.path.exists(test_data_path)
 def gan_dataset_case(
     batch_size, seq_len, hop_len, conv_pad, return_pairs, return_segments, use_noise_augment, use_cache, num_workers
 ):
-    """Run dataloader with given parameters and check conditions """
+    """Run dataloader with given parameters and check conditions"""
     ap = AudioProcessor(**C.audio)
     _, train_items = load_wav_data(test_data_path, 10)
     dataset = GANDataset(
@@ -90,7 +90,7 @@ def gan_dataset_case(
 
 
 def test_parametrized_gan_dataset():
-    """ test dataloader with different parameters """
+    """test dataloader with different parameters"""
     params = [
         [32, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, True, False, True, 0],
         [32, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, True, False, True, 4],

tests/test_vocoder_wavernn_datasets.py

@@ -23,7 +23,7 @@ ok_ljspeech = os.path.exists(test_data_path)
 
 
 def wavernn_dataset_case(batch_size, seq_len, hop_len, pad, mode, mulaw, num_workers):
-    """ run dataloader with given parameters and check conditions """
+    """run dataloader with given parameters and check conditions"""
     ap = AudioProcessor(**C.audio)
 
     C.batch_size = batch_size
@@ -69,7 +69,7 @@ def wavernn_dataset_case(batch_size, seq_len, hop_len, pad, mode, mulaw, num_wor
 
 
 def test_parametrized_wavernn_dataset():
-    """ test dataloader with different parameters """
+    """test dataloader with different parameters"""
     params = [
         [16, C.audio["hop_length"] * 10, C.audio["hop_length"], 2, 10, True, 0],
         [16, C.audio["hop_length"] * 10, C.audio["hop_length"], 2, "mold", False, 4],