Run 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 @@ def tts_to_file(
     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 @@ def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type
         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 @@ def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type
                 ] * K
             else:
                 K = steps // 2 + 1
-                orders = [2,] * (
+                orders = [
+                    2,
+                ] * (
                     K - 1
                 ) + [1]
         elif order == 1:
@@ -1440,7 +1448,10 @@ def sample(
                         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

@@ -559,12 +559,15 @@ def __init__(self, vocab_file=None):
     @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"}:

TTS/tts/models/xtts.py

@@ -606,7 +606,7 @@ def handle_chunks(self, wav_gen, wav_gen_prev, wav_overlap, overlap_len):
             if overlap_len > len(wav_chunk):
                 # wav_chunk is smaller than overlap_len, pass on last wav_gen
                 if wav_gen_prev is not None:
-                    wav_chunk = wav_gen[(wav_gen_prev.shape[0] - overlap_len):]
+                    wav_chunk = wav_gen[(wav_gen_prev.shape[0] - overlap_len) :]
                 else:
                     # not expecting will hit here as problem happens on last chunk
                     wav_chunk = wav_gen[-overlap_len:]
@@ -616,7 +616,7 @@ def handle_chunks(self, wav_gen, wav_gen_prev, wav_overlap, overlap_len):
                 crossfade_wav = crossfade_wav * torch.linspace(0.0, 1.0, overlap_len).to(crossfade_wav.device)
                 wav_chunk[:overlap_len] = wav_overlap * torch.linspace(1.0, 0.0, overlap_len).to(wav_overlap.device)
                 wav_chunk[:overlap_len] += crossfade_wav
-                
+
         wav_overlap = wav_gen[-overlap_len:]
         wav_gen_prev = wav_gen
         return wav_chunk, wav_gen_prev, wav_overlap

tests/xtts_tests/test_xtts_gpt_train.py

@@ -60,7 +60,9 @@
 
 
 # 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 @@
 
 
 # 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