Make style

TTS/tts/datasets/dataset.py

@@ -11,7 +11,6 @@ from torch.utils.data import Dataset
 from TTS.tts.utils.data import prepare_data, prepare_stop_target, prepare_tensor
 from TTS.utils.audio.numpy_transforms import load_wav, wav_to_mel, wav_to_spec
 
-
 # to prevent too many open files error as suggested here
 # https://github.com/pytorch/pytorch/issues/11201#issuecomment-421146936
 torch.multiprocessing.set_sharing_strategy("file_system")

TTS/tts/models/forward_tts_e2e.py

@@ -2,10 +2,9 @@ import os
 from dataclasses import dataclass, field
 from itertools import chain
 from typing import Dict, List, Tuple, Union
+
 import numpy as np
 import pyworld as pw
-
-
 import torch
 import torch.distributed as dist
 from coqpit import Coqpit
@@ -20,15 +19,16 @@ from TTS.tts.layers.vits.discriminator import VitsDiscriminator
 from TTS.tts.models.base_tts import BaseTTSE2E
 from TTS.tts.models.forward_tts import ForwardTTS, ForwardTTSArgs
 from TTS.tts.models.vits import load_audio, wav_to_mel
-from TTS.utils.audio.numpy_transforms import build_mel_basis, compute_f0, mel_to_wav as mel_to_wav_numpy
 from TTS.tts.utils.helpers import rand_segments, segment, sequence_mask
 from TTS.tts.utils.speakers import SpeakerManager
 from TTS.tts.utils.synthesis import synthesis
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.tts.utils.visual import plot_alignment, plot_avg_pitch
+from TTS.tts.utils.visual import plot_alignment, plot_avg_pitch, plot_spectrogram
+from TTS.utils.audio.numpy_transforms import build_mel_basis, compute_f0
+from TTS.utils.audio.numpy_transforms import db_to_amp as db_to_amp_numpy
+from TTS.utils.audio.numpy_transforms import mel_to_wav as mel_to_wav_numpy
 from TTS.vocoder.models.hifigan_generator import HifiganGenerator
 from TTS.vocoder.utils.generic_utils import plot_results
-from TTS.tts.utils.visual import plot_alignment, plot_avg_pitch, plot_spectrogram
 
 
 def id_to_torch(aux_id, cuda=False):
@@ -89,7 +89,9 @@ class ForwardTTSE2eF0Dataset(F0Dataset):
     @staticmethod
     def _compute_and_save_pitch(config, wav_file, pitch_file=None):
         wav, _ = load_audio(wav_file)
-        f0 = compute_f0(x=wav.numpy()[0], sample_rate=config.sample_rate, hop_length=config.hop_length, pitch_fmax=config.pitch_fmax)
+        f0 = compute_f0(
+            x=wav.numpy()[0], sample_rate=config.sample_rate, hop_length=config.hop_length, pitch_fmax=config.pitch_fmax
+        )
         # skip the last F0 value to align with the spectrogram
         if wav.shape[1] % config.hop_length != 0:
             f0 = f0[:-1]
@@ -632,7 +634,9 @@ class ForwardTTSE2e(BaseTTSE2E):
             figures["alignment_hat"] = plot_alignment(alignments_hat.T, output_fig=False)
 
         # Sample audio
-        encoder_audio = mel_to_wav_numpy(mel=pred_spec.T, mel_basis=self.__mel_basis, **self.config.audio)
+        encoder_audio = mel_to_wav_numpy(
+            mel=db_to_amp_numpy(x=pred_spec.T, gain=1, base=None), mel_basis=self.__mel_basis, **self.config.audio
+        )
         audios[f"{name_prefix}/encoder_audio"] = encoder_audio
 
         # vocoder outputs
@@ -780,7 +784,9 @@ class ForwardTTSE2e(BaseTTSE2E):
         outputs = self.inference_spec_decoder(text_inputs, aux_input={"d_vectors": d_vector, "speaker_ids": speaker_id})
 
         # collect outputs
-        wav = mel_to_wav_numpy(mel=outputs["model_outputs"].cpu().numpy()[0].T, mel_basis=self.__mel_basis, **self.config.audio)
+        wav = mel_to_wav_numpy(
+            mel=outputs["model_outputs"].cpu().numpy()[0].T, mel_basis=self.__mel_basis, **self.config.audio
+        )
         alignments = outputs["alignments"]
         return_dict = {
             "wav": wav[None, :],

TTS/utils/audio/numpy_transforms.py

@@ -1,10 +1,10 @@
 from typing import Callable, Tuple
+
 import librosa
 import numpy as np
-
-import soundfile as sf
 import pyworld as pw
 import scipy
+import soundfile as sf
 
 # from TTS.tts.utils.helpers import StandardScaler
 
@@ -148,21 +148,15 @@ def wav_to_mel(*, y: np.ndarray = None, **kwargs) -> np.ndarray:
     return S.astype(np.float32)
 
 
-def spec_to_wav(*, spec: np.ndarray, power: float = 1.5, denorm_func: Callable = None, **kwargs) -> np.ndarray:
+def spec_to_wav(*, spec: np.ndarray, power: float = 1.5, **kwargs) -> np.ndarray:
     """Convert a spectrogram to a waveform using Griffi-Lim vocoder."""
     S = spec.copy()
-    if denorm_func is not None:
-        S = denorm_func(spec=S, **kwargs)
-    S = db_to_amp(S)
     return griffin_lim(spec=S**power, **kwargs)
 
 
-def mel_to_wav(*, mel: np.ndarray = None, power: float = 1.5, denorm_func: Callable = None, **kwargs) -> np.ndarray:
+def mel_to_wav(*, mel: np.ndarray = None, power: float = 1.5, **kwargs) -> np.ndarray:
     """Convert a melspectrogram to a waveform using Griffi-Lim vocoder."""
     S = mel.copy()
-    if denorm_func is not None:
-        S = denorm_func(spec=S, **kwargs)
-    S = db_to_amp(S)
     S = mel_to_spec(mel=S, mel_basis=kwargs["mel_basis"])  # Convert back to linear
     return griffin_lim(spec=S**power, **kwargs)
 

TTS/utils/audio/torch_transforms.py

@@ -1,6 +1,6 @@
+import librosa
 import torch
 from torch import nn
-import librosa
 
 
 class TorchSTFT(nn.Module):  # pylint: disable=abstract-method

TTS/vocoder/utils/generic_utils.py

@@ -3,9 +3,9 @@ from typing import Dict
 import numpy as np
 import torch
 from matplotlib import pyplot as plt
-from TTS.utils.audio.numpy_transforms import amp_to_db, build_mel_basis, wav_to_mel
 
 from TTS.tts.utils.visual import plot_spectrogram
+from TTS.utils.audio.numpy_transforms import amp_to_db, build_mel_basis, wav_to_mel
 from TTS.utils.audio.processor import AudioProcessor
 
 
@@ -30,7 +30,13 @@ def interpolate_vocoder_input(scale_factor, spec):
     return spec
 
 
-def plot_results(y_hat: torch.tensor, y: torch.tensor, ap: AudioProcessor=None, audio_config: "Coqpit"= None,  name_prefix: str = None) -> Dict:
+def plot_results(
+    y_hat: torch.tensor,
+    y: torch.tensor,
+    ap: AudioProcessor = None,
+    audio_config: "Coqpit" = None,
+    name_prefix: str = None,
+) -> Dict:
     """Plot the predicted and the real waveform and their spectrograms.
 
     Args:

recipes/ljspeech/fast_pitch_e2e/train_fast_pitch_e2e.py

@@ -8,7 +8,6 @@ from TTS.tts.datasets import load_tts_samples
 from TTS.tts.models.forward_tts_e2e import ForwardTTSE2e, ForwardTTSE2eArgs, ForwardTTSE2eAudio
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 
-
 output_path = os.path.dirname(os.path.abspath(__file__))
 
 # init configs