Code linting

TTS/tts/models/xtts.py

@@ -381,33 +381,26 @@ class Xtts(BaseTTS):
             audio_22k = torchaudio.functional.resample(audio, sr, 22050)
         audio_22k = audio_22k[:, : 22050 * length]
         if self.args.gpt_use_perceiver_resampler:
-            mel = wav_to_mel_cloning(
-                audio_22k,
-                mel_norms=self.mel_stats.cpu(),
-                n_fft=2048,
-                hop_length=256,
-                win_length=1024,
-                power=2,
-                normalized=False,
-                sample_rate=22050,
-                f_min=0,
-                f_max=8000,
-                n_mels=80,
-            )
+            n_fft = 2048
+            hop_length = 256
+            win_length = 1024
         else:
-            mel = wav_to_mel_cloning(
-                audio_22k,
-                mel_norms=self.mel_stats.cpu(),
-                n_fft=4096,
-                hop_length=1024,
-                win_length=4096,
-                power=2,
-                normalized=False,
-                sample_rate=22050,
-                f_min=0,
-                f_max=8000,
-                n_mels=80,
-            )
+            n_fft = 4096
+            hop_length = 1024
+            win_length = 4096
+        mel = wav_to_mel_cloning(
+            audio_22k,
+            mel_norms=self.mel_stats.cpu(),
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            power=2,
+            normalized=False,
+            sample_rate=22050,
+            f_min=0,
+            f_max=8000,
+            n_mels=80,
+        )
         cond_latent = self.gpt.get_style_emb(mel.to(self.device))
         return cond_latent.transpose(1, 2)
 

tests/xtts_tests/test_xtts_v2-0_gpt_train.py

@@ -27,16 +27,16 @@ PROJECT_NAME = "XTTS_trainer"
 DASHBOARD_LOGGER = "tensorboard"
 LOGGER_URI = None
 
-# Set here the path that the checkpoints will be saved. Default: ./run/training/
 OUT_PATH = os.path.join(get_tests_output_path(), "train_outputs", "xtts_tests")
 os.makedirs(OUT_PATH, exist_ok=True)
 
 # Create DVAE checkpoint and mel_norms on test time
 # 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(
     OUT_PATH, "mel_stats.pth"
-)  # Mel spectrogram norms, required for dvae mel spectrogram extraction
+)
 dvae = DiscreteVAE(
     channels=80,
     normalization=None,
@@ -99,9 +99,7 @@ config = GPTTrainerConfig(
     model_args=model_args,
     run_name=RUN_NAME,
     project_name=PROJECT_NAME,
-    run_description="""
-        GPT XTTS training
-        """,
+    run_description="GPT XTTS training",
     dashboard_logger=DASHBOARD_LOGGER,
     logger_uri=LOGGER_URI,
     audio=audio_config,