:albemic: | Real-time testing...

AudioUtils.py

@@ -16,3 +16,37 @@ def stretch_tensor(tensor, target_length):
     tensor = F.interpolate(tensor, scale_factor=scale_factor, mode='linear', align_corners=False)
 
     return tensor
+
+def pad_tensor(audio_tensor: torch.Tensor, target_length: int = 128):
+    current_length = audio_tensor.shape[-1]
+
+    if current_length < target_length:
+        padding_needed = target_length - current_length
+
+        padding_tuple = (0, padding_needed)
+        padded_audio_tensor = F.pad(audio_tensor, padding_tuple, mode='constant', value=0)
+    else:
+        padded_audio_tensor = audio_tensor
+
+    return padded_audio_tensor
+
+def split_audio(audio_tensor: torch.Tensor, chunk_size: int = 128) -> list[torch.Tensor]:
+    if not isinstance(chunk_size, int) or chunk_size <= 0:
+        raise ValueError("chunk_size must be a positive integer.")
+
+    # Handle scalar tensor edge case if necessary
+    if audio_tensor.dim() == 0:
+        return [audio_tensor] if audio_tensor.numel() > 0 else []
+
+    # Identify the dimension to split (usually the last one, representing time/samples)
+    split_dim = -1
+    num_samples = audio_tensor.shape[split_dim]
+
+    if num_samples == 0:
+        return [] # Return empty list if the dimension to split is empty
+
+    # Use torch.split to divide the tensor into chunks
+    # It handles the last chunk being potentially smaller automatically.
+    chunks = list(torch.split(audio_tensor, chunk_size, dim=split_dim))
+
+    return chunks