diff --git a/TTS/trainer.py b/TTS/trainer.py
index e515ad04..afe51e04 100644
--- a/TTS/trainer.py
+++ b/TTS/trainer.py
@@ -626,17 +626,13 @@ class Trainer:
                 # https://nvidia.github.io/apex/advanced.html?highlight=accumulate#backward-passes-with-multiple-optimizers
                 with amp.scale_loss(loss_dict["loss"], optimizer) as scaled_loss:
                     scaled_loss.backward()
-                grad_norm = torch.nn.utils.clip_grad_norm_(
-                    amp.master_params(optimizer), grad_clip, error_if_nonfinite=False
-                )
+                grad_norm = torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), grad_clip)
             else:
                 # model optimizer step in mixed precision mode
                 scaler.scale(loss_dict["loss"]).backward()
                 if grad_clip > 0:
                     scaler.unscale_(optimizer)
-                    grad_norm = torch.nn.utils.clip_grad_norm_(
-                        self.master_params(optimizer), grad_clip, error_if_nonfinite=False
-                    )
+                    grad_norm = torch.nn.utils.clip_grad_norm_(self.master_params(optimizer), grad_clip)
                     # pytorch skips the step when the norm is 0. So ignore the norm value when it is NaN
                     if torch.isnan(grad_norm) or torch.isinf(grad_norm):
                         grad_norm = 0
@@ -648,7 +644,7 @@ class Trainer:
             # main model optimizer step
             loss_dict["loss"].backward()
             if grad_clip > 0:
-                grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip, error_if_nonfinite=False)
+                grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
             optimizer.step()
 
         step_time = time.time() - step_start_time
diff --git a/TTS/vocoder/datasets/preprocess.py b/TTS/vocoder/datasets/preprocess.py
index 91dad459..d8cc350a 100644
--- a/TTS/vocoder/datasets/preprocess.py
+++ b/TTS/vocoder/datasets/preprocess.py
@@ -29,9 +29,7 @@ def preprocess_wav_files(out_path: str, config: Coqpit, ap: AudioProcessor):
         mel = ap.melspectrogram(y)
         np.save(mel_path, mel)
         if isinstance(config.mode, int):
-            quant = (
-                ap.mulaw_encode(y, qc=config.mode) if config.model_args.mulaw else ap.quantize(y, bits=config.mode)
-            )
+            quant = ap.mulaw_encode(y, qc=config.mode) if config.model_args.mulaw else ap.quantize(y, bits=config.mode)
             np.save(quant_path, quant)