Make style

TTS/cs_api.py

@@ -82,7 +82,6 @@ class CS_API:
         },
     }
 
-
     SUPPORTED_LANGUAGES = ["en", "es", "de", "fr", "it", "pt", "pl", "tr", "ru", "nl", "cs", "ar", "zh-cn", "ja"]
 
     def __init__(self, api_token=None, model="XTTS"):
@@ -308,7 +307,11 @@ if __name__ == "__main__":
     print(api.list_speakers_as_tts_models())
 
     ts = time.time()
-    wav, sr = api.tts("It took me quite a long time to develop a voice.", language="en", speaker_name=api.speakers[0].name)
+    wav, sr = api.tts(
+        "It took me quite a long time to develop a voice.", language="en", speaker_name=api.speakers[0].name
+    )
     print(f" [i] XTTS took {time.time() - ts:.2f}s")
 
-    filepath = api.tts_to_file(text="Hello world!", speaker_name=api.speakers[0].name, language="en", file_path="output.wav")
+    filepath = api.tts_to_file(
+        text="Hello world!", speaker_name=api.speakers[0].name, language="en", file_path="output.wav"
+    )

TTS/tts/layers/tortoise/dpm_solver.py

@@ -562,15 +562,21 @@ class DPM_Solver:
         if order == 3:
             K = steps // 3 + 1
             if steps % 3 == 0:
-                orders = [3,] * (
+                orders = [
+                    3,
+                ] * (
                     K - 2
                 ) + [2, 1]
             elif steps % 3 == 1:
-                orders = [3,] * (
+                orders = [
+                    3,
+                ] * (
                     K - 1
                 ) + [1]
             else:
-                orders = [3,] * (
+                orders = [
+                    3,
+                ] * (
                     K - 1
                 ) + [2]
         elif order == 2:
@@ -581,7 +587,9 @@ class DPM_Solver:
                 ] * K
             else:
                 K = steps // 2 + 1
-                orders = [2,] * (
+                orders = [
+                    2,
+                ] * (
                     K - 1
                 ) + [1]
         elif order == 1:
@@ -1440,7 +1448,10 @@ 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,
@@ -1548,4 +1559,4 @@ def expand_dims(v, dims):
     Returns:
         a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
     """
-    return v[(...,) + (None,) * (dims - 1)]
+    return v[(...,) + (None,) * (dims - 1)]

TTS/tts/layers/xtts/tokenizer.py

@@ -1,6 +1,7 @@
 import json
 import os
 import re
+from functools import cached_property
 
 import pypinyin
 import torch
@@ -8,7 +9,6 @@ from hangul_romanize import Transliter
 from hangul_romanize.rule import academic
 from num2words import num2words
 from tokenizers import Tokenizer
-from functools import cached_property
 
 from TTS.tts.layers.xtts.zh_num2words import TextNorm as zh_num2words
 
@@ -560,19 +560,22 @@ class VoiceBpeTokenizer:
     @cached_property
     def katsu(self):
         import cutlet
+
         return cutlet.Cutlet()
-    
+
     def check_input_length(self, txt, lang):
         limit = self.char_limits.get(lang, 250)
         if len(txt) > limit:
-            print(f"[!] Warning: The text length exceeds the character limit of {limit} for language '{lang}', this might cause truncated audio.")
+            print(
+                f"[!] Warning: The text length exceeds the character limit of {limit} for language '{lang}', this might cause truncated audio."
+            )
 
     def preprocess_text(self, txt, lang):
         if lang in {"ar", "cs", "de", "en", "es", "fr", "hu", "it", "nl", "pl", "pt", "ru", "tr", "zh", "zh-cn"}:
             txt = multilingual_cleaners(txt, lang)
             if lang in {"zh", "zh-cn"}:
                 txt = chinese_transliterate(txt)
-        elif lang == "ja":                
+        elif lang == "ja":
             txt = japanese_cleaners(txt, self.katsu)
         elif lang == "ko":
             txt = korean_cleaners(txt)

TTS/tts/layers/xtts/trainer/dataset.py

@@ -5,6 +5,7 @@ import sys
 import torch
 import torch.nn.functional as F
 import torch.utils.data
+
 from TTS.tts.models.xtts import load_audio
 
 torch.set_num_threads(1)

tests/xtts_tests/test_xtts_gpt_train.py

@@ -60,7 +60,9 @@ XTTS_CHECKPOINT = None  # "/raid/edresson/dev/Checkpoints/XTTS_evaluation/xtts_s
 
 
 # Training sentences generations
-SPEAKER_REFERENCE = ["tests/data/ljspeech/wavs/LJ001-0002.wav"]  # speaker reference to be used in training test sentences
+SPEAKER_REFERENCE = [
+    "tests/data/ljspeech/wavs/LJ001-0002.wav"
+]  # speaker reference to be used in training test sentences
 LANGUAGE = config_dataset.language
 
 

tests/xtts_tests/test_xtts_v2-0_gpt_train.py

@@ -58,7 +58,9 @@ XTTS_CHECKPOINT = None  # "/raid/edresson/dev/Checkpoints/XTTS_evaluation/xtts_s
 
 
 # Training sentences generations
-SPEAKER_REFERENCE = ["tests/data/ljspeech/wavs/LJ001-0002.wav"]  # speaker reference to be used in training test sentences
+SPEAKER_REFERENCE = [
+    "tests/data/ljspeech/wavs/LJ001-0002.wav"
+]  # speaker reference to be used in training test sentences
 LANGUAGE = config_dataset.language