Merge pull request 'Merge the most successful implementation of this crap' (#2) from rt-test into main

Reviewed-on: #2

AudioUtils.py

@@ -8,12 +8,13 @@ def stereo_tensor_to_mono(waveform: torch.Tensor) -> torch.Tensor:
 
 
 def pad_tensor(audio_tensor: torch.Tensor, target_length: int = 512) -> torch.Tensor:
-    current = audio_tensor.size(-1)
-    padding_amount = target_length - current
+    padding_amount = target_length - audio_tensor.size(-1)
     if padding_amount <= 0:
         return audio_tensor
 
-    return F.pad(audio_tensor, (0, padding_amount))
+    padded_audio_tensor = F.pad(audio_tensor, (0, padding_amount))
+
+    return padded_audio_tensor
 
 
 def split_audio(audio_tensor: torch.Tensor, chunk_size: int = 512, pad_last_tensor: bool = False) -> list[torch.Tensor]:

data.py

@@ -67,6 +67,5 @@ class AudioDataset(Dataset):
         if audio_clip[0].shape[1] < low_audio_clip.shape[1]:
             low_audio_clip = low_audio_clip[:, :audio_clip[0].shape[1]]
         elif audio_clip[0].shape[1] > low_audio_clip.shape[1]:
-            target_len = audio_clip[0].shape[1]
-            low_audio_clip = AudioUtils.pad_tensor(low_audio_clip, target_len)
+            low_audio_clip = AudioUtils.pad_tensor(low_audio_clip, self.clip_length)
         return ((audio_clip[0], low_audio_clip), (audio_clip[1], mangled_sample_rate))

training.py

@@ -34,11 +34,7 @@ args = parser.parse_args()
 # Init accelerator
 # ---------------------------
 
-try:
 accelerator = Accelerator(mixed_precision="bf16")
-except Exception:
-    accelerator = Accelerator(mixed_precision="fp16")
-    accelerator.print("⚠️ | bf16 unavailable — falling back to fp16")
 
 # ---------------------------
 # Models

utils/TrainingTools.py

@@ -44,8 +44,13 @@ def discriminator_loss(disc_real_outputs, disc_generated_outputs):
 
 
 def generator_adv_loss(disc_generated_outputs):
-    loss = 0.0
-    for dg in disc_generated_outputs:
+    """
+    Least Squares GAN Loss for the Generator.
+    Objective: Fake -> 1 (Fool the discriminator)
+    """
+    loss = 0
+    for dg in zip(disc_generated_outputs):
+        dg = dg[0] # Unpack tuple
         loss += torch.mean((dg - 1) ** 2)
     return loss