Make style

TTS/tts/layers/tortoise/dpm_solver.py

@@ -562,21 +562,15 @@ class DPM_Solver:
         if order == 3:
             K = steps // 3 + 1
             if steps % 3 == 0:
-                orders = [
+                orders = [3,] * (
-                    3,
-                ] * (
                     K - 2
                 ) + [2, 1]
             elif steps % 3 == 1:
-                orders = [
+                orders = [3,] * (
-                    3,
-                ] * (
                     K - 1
                 ) + [1]
             else:
-                orders = [
+                orders = [3,] * (
-                    3,
-                ] * (
                     K - 1
                 ) + [2]
         elif order == 2:
@@ -587,9 +581,7 @@ class DPM_Solver:
                 ] * K
             else:
                 K = steps // 2 + 1
-                orders = [
+                orders = [2,] * (
-                    2,
-                ] * (
                     K - 1
                 ) + [1]
         elif order == 1:
@@ -1448,10 +1440,7 @@ class DPM_Solver:
                         model_prev_list[-1] = self.model_fn(x, t)
             elif method in ["singlestep", "singlestep_fixed"]:
                 if method == "singlestep":
-                    (
+                    (timesteps_outer, orders,) = self.get_orders_and_timesteps_for_singlestep_solver(
-                        timesteps_outer,
-                        orders,
-                    ) = self.get_orders_and_timesteps_for_singlestep_solver(
                         steps=steps,
                         order=order,
                         skip_type=skip_type,

TTS/tts/layers/xtts/perceiver_encoder.py

@@ -1,16 +1,14 @@
 # Adapted from https://github.com/lucidrains/naturalspeech2-pytorch/blob/659bec7f7543e7747e809e950cc2f84242fbeec7/naturalspeech2_pytorch/naturalspeech2_pytorch.py#L532
 
-import torch
-from torch import nn, einsum
-import torch.nn.functional as F
-
 from collections import namedtuple
 from functools import wraps
-from packaging import version
-
 
+import torch
+import torch.nn.functional as F
 from einops import rearrange, repeat
 from einops.layers.torch import Rearrange
+from packaging import version
+from torch import einsum, nn
 
 
 def exists(val):

TTS/tts/layers/xtts/tokenizer.py

@@ -4,13 +4,12 @@ import re
 
 import pypinyin
 import torch
+from hangul_romanize import Transliter
+from hangul_romanize.rule import academic
 from num2words import num2words
 from tokenizers import Tokenizer
 
 from TTS.tts.layers.xtts.zh_num2words import TextNorm as zh_num2words
-from hangul_romanize import Transliter
-from hangul_romanize.rule import academic
-
 
 _whitespace_re = re.compile(r"\s+")
 

TTS/tts/models/base_tacotron.py

@@ -252,12 +252,7 @@ class BaseTacotron(BaseTTS):
 
     def compute_capacitron_VAE_embedding(self, inputs, reference_mel_info, text_info=None, speaker_embedding=None):
         """Capacitron Variational Autoencoder"""
-        (
+        (VAE_outputs, posterior_distribution, prior_distribution, capacitron_beta,) = self.capacitron_vae_layer(
-            VAE_outputs,
-            posterior_distribution,
-            prior_distribution,
-            capacitron_beta,
-        ) = self.capacitron_vae_layer(
             reference_mel_info,
             text_info,
             speaker_embedding,  # pylint: disable=not-callable

TTS/tts/models/tortoise.py

@@ -676,12 +676,7 @@ class Tortoise(BaseTTS):
         ), "Too much text provided. Break the text up into separate segments and re-try inference."
 
         if voice_samples is not None:
-            (
+            (auto_conditioning, diffusion_conditioning, _, _,) = self.get_conditioning_latents(
-                auto_conditioning,
-                diffusion_conditioning,
-                _,
-                _,
-            ) = self.get_conditioning_latents(
                 voice_samples,
                 return_mels=True,
                 latent_averaging_mode=latent_averaging_mode,

recipes/ljspeech/xtts_v2/train_gpt_xtts.py

@@ -7,7 +7,6 @@ from TTS.tts.datasets import load_tts_samples
 from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
 from TTS.utils.manage import ModelManager
 
-
 # Logging parameters
 RUN_NAME = "GPT_XTTS_v2.0_LJSpeech_FT"
 PROJECT_NAME = "XTTS_trainer"

tests/xtts_tests/test_xtts_v2-0_gpt_train.py

@@ -34,9 +34,7 @@ os.makedirs(OUT_PATH, exist_ok=True)
 # DVAE parameters: For the training we need the dvae to extract the dvae tokens, given that you must provide the paths for this model
 DVAE_CHECKPOINT = os.path.join(OUT_PATH, "dvae.pth")  # DVAE checkpoint
 # Mel spectrogram norms, required for dvae mel spectrogram extraction
-MEL_NORM_FILE = os.path.join(
+MEL_NORM_FILE = os.path.join(OUT_PATH, "mel_stats.pth")
-    OUT_PATH, "mel_stats.pth"
-)
 dvae = DiscreteVAE(
     channels=80,
     normalization=None,