Make style

TTS/tts/layers/tortoise/diffusion.py

@@ -17,7 +17,6 @@
 
 from TTS.tts.layers.tortoise.dpm_solver import DPM_Solver, NoiseScheduleVP, model_wrapper
 
-
 try:
     from k_diffusion.sampling import sample_dpmpp_2m, sample_euler_ancestral
 

TTS/tts/layers/xtts/gpt.py

@@ -441,7 +441,9 @@ def forward(
         audio_codes = F.pad(audio_codes[:, :max_mel_len], (0, 1), value=self.stop_audio_token)
 
         # Pad mel codes with stop_audio_token
-        audio_codes = self.set_mel_padding(audio_codes, code_lengths - 3) # -3 to get the real code lengths without consider start and stop tokens that was not added yet
+        audio_codes = self.set_mel_padding(
+            audio_codes, code_lengths - 3
+        )  # -3 to get the real code lengths without consider start and stop tokens that was not added yet
 
         # Build input and target tensors
         # Prepend start token to inputs and append stop token to targets

TTS/tts/layers/xtts/tokenizer.py

@@ -1,23 +1,22 @@
 import os
 import re
-import torch
-import pypinyin
 import textwrap
-
 from functools import cached_property
+
+import pypinyin
+import torch
 from hangul_romanize import Transliter
 from hangul_romanize.rule import academic
 from num2words import num2words
+from spacy.lang.ar import Arabic
+from spacy.lang.en import English
+from spacy.lang.es import Spanish
+from spacy.lang.ja import Japanese
+from spacy.lang.zh import Chinese
 from tokenizers import Tokenizer
 
 from TTS.tts.layers.xtts.zh_num2words import TextNorm as zh_num2words
 
-from spacy.lang.en import English
-from spacy.lang.zh import Chinese
-from spacy.lang.ja import Japanese
-from spacy.lang.ar import Arabic
-from spacy.lang.es import Spanish
-
 
 def get_spacy_lang(lang):
     if lang == "zh":
@@ -32,6 +31,7 @@ def get_spacy_lang(lang):
         # For most languages, Enlish does the job
         return English()
 
+
 def split_sentence(text, lang, text_split_length=250):
     """Preprocess the input text"""
     text_splits = []
@@ -67,6 +67,7 @@ def split_sentence(text, lang, text_split_length=250):
 
     return text_splits
 
+
 _whitespace_re = re.compile(r"\s+")
 
 # List of (regular expression, replacement) pairs for abbreviations:
@@ -619,7 +620,7 @@ def katsu(self):
         return cutlet.Cutlet()
 
     def check_input_length(self, txt, lang):
-        lang = lang.split("-")[0] # remove the region
+        lang = lang.split("-")[0]  # remove the region
         limit = self.char_limits.get(lang, 250)
         if len(txt) > limit:
             print(
@@ -640,7 +641,7 @@ def preprocess_text(self, txt, lang):
         return txt
 
     def encode(self, txt, lang):
-        lang = lang.split("-")[0] # remove the region
+        lang = lang.split("-")[0]  # remove the region
         self.check_input_length(txt, lang)
         txt = self.preprocess_text(txt, lang)
         lang = "zh-cn" if lang == "zh" else lang

TTS/tts/models/xtts.py

@@ -513,13 +513,13 @@ def inference(
         enable_text_splitting=False,
         **hf_generate_kwargs,
     ):
-        language = language.split("-")[0] # remove the country code
+        language = language.split("-")[0]  # remove the country code
         length_scale = 1.0 / max(speed, 0.05)
         if enable_text_splitting:
             text = split_sentence(text, language, self.tokenizer.char_limits[language])
         else:
             text = [text]
-        
+
         wavs = []
         gpt_latents_list = []
         for sent in text:
@@ -563,9 +563,7 @@ def inference(
 
                 if length_scale != 1.0:
                     gpt_latents = F.interpolate(
-                        gpt_latents.transpose(1, 2),
-                        scale_factor=length_scale,
-                        mode="linear"
+                        gpt_latents.transpose(1, 2), scale_factor=length_scale, mode="linear"
                     ).transpose(1, 2)
 
                 gpt_latents_list.append(gpt_latents.cpu())
@@ -623,7 +621,7 @@ def inference_stream(
         enable_text_splitting=False,
         **hf_generate_kwargs,
     ):
-        language = language.split("-")[0] # remove the country code
+        language = language.split("-")[0]  # remove the country code
         length_scale = 1.0 / max(speed, 0.05)
         if enable_text_splitting:
             text = split_sentence(text, language, self.tokenizer.char_limits[language])
@@ -675,9 +673,7 @@ def inference_stream(
                     gpt_latents = torch.cat(all_latents, dim=0)[None, :]
                     if length_scale != 1.0:
                         gpt_latents = F.interpolate(
-                            gpt_latents.transpose(1, 2),
-                            scale_factor=length_scale,
-                            mode="linear"
+                            gpt_latents.transpose(1, 2), scale_factor=length_scale, mode="linear"
                         ).transpose(1, 2)
                     wav_gen = self.hifigan_decoder(gpt_latents, g=speaker_embedding.to(self.device))
                     wav_chunk, wav_gen_prev, wav_overlap = self.handle_chunks(

tests/zoo_tests/test_models.py

@@ -186,7 +186,7 @@ def test_xtts_v2_streaming():
         "en",
         gpt_cond_latent,
         speaker_embedding,
-        speed=1.5
+        speed=1.5,
     )
     wav_chuncks = []
     for i, chunk in enumerate(chunks):
@@ -198,7 +198,7 @@ def test_xtts_v2_streaming():
         "en",
         gpt_cond_latent,
         speaker_embedding,
-        speed=0.66
+        speed=0.66,
     )
     wav_chuncks = []
     for i, chunk in enumerate(chunks):