diff --git a/rvc/train/train.py b/rvc/train/train.py
index cf1c3485..5b6a9b4a 100644
--- a/rvc/train/train.py
+++ b/rvc/train/train.py
@@ -125,7 +125,9 @@ def verify_checkpoint_shapes(checkpoint_path, model):
         else:
             model_state_dict = model.load_state_dict(checkpoint_state_dict)
     except RuntimeError:
-        print("The parameters of the pretrain model such as the sample rate or architecture do not match the selected model.")
+        print(
+            "The parameters of the pretrain model such as the sample rate or architecture do not match the selected model."
+        )
         sys.exit(1)
     else:
         del checkpoint
@@ -324,8 +326,8 @@ def run(
         DistributedBucketSampler,
         TextAudioCollateMultiNSFsid,
         TextAudioLoaderMultiNSFsid,
-    )    
-    
+    )
+
     train_dataset = TextAudioLoaderMultiNSFsid(config.data)
     collate_fn = TextAudioCollateMultiNSFsid()
     train_sampler = DistributedBucketSampler(
@@ -351,8 +353,8 @@ def run(
     # Initialize models and optimizers
     from rvc.lib.algorithm.discriminators import MultiPeriodDiscriminator
     from rvc.lib.algorithm.discriminators import MultiPeriodDiscriminatorV2
-    from rvc.lib.algorithm.synthesizers import Synthesizer    
-    
+    from rvc.lib.algorithm.synthesizers import Synthesizer
+
     net_g = Synthesizer(
         config.data.filter_length // 2 + 1,
         config.train.segment_size // config.data.hop_length,
@@ -468,6 +470,7 @@ def run(
         scheduler_g.step()
         scheduler_d.step()
 
+
 def train_and_evaluate(
     rank,
     epoch,
@@ -537,17 +540,31 @@ def train_and_evaluate(
                 info = [tensor.cuda(rank, non_blocking=True) for tensor in info]
             elif device.type != "cuda":
                 info = [tensor.to(device) for tensor in info]
-            # else iterator is going thru a cached list with a device already assigned 
-
-            phone, phone_lengths, pitch, pitchf, spec, spec_lengths, wave, wave_lengths, sid = info
+            # else iterator is going thru a cached list with a device already assigned
+
+            (
+                phone,
+                phone_lengths,
+                pitch,
+                pitchf,
+                spec,
+                spec_lengths,
+                wave,
+                wave_lengths,
+                sid,
+            ) = info
             pitch = pitch if pitch_guidance else None
             pitchf = pitchf if pitch_guidance else None
 
             # Forward pass
             use_amp = config.train.fp16_run and device.type == "cuda"
             with autocast(enabled=use_amp):
-                model_output = net_g(phone, phone_lengths, pitch, pitchf, spec, spec_lengths, sid)
-                y_hat, ids_slice, x_mask, z_mask, (z, z_p, m_p, logs_p, m_q, logs_q) = model_output 
+                model_output = net_g(
+                    phone, phone_lengths, pitch, pitchf, spec, spec_lengths, sid
+                )
+                y_hat, ids_slice, x_mask, z_mask, (z, z_p, m_p, logs_p, m_q, logs_q) = (
+                    model_output
+                )
                 # used for tensorboard chart - all/mel
                 mel = spec_to_mel_torch(
                     spec,