data.py

@@ -12,8 +12,9 @@ class AudioDataset(Dataset):
     #audio_sample_rates = [8000, 11025, 16000, 22050]
     audio_sample_rates = [11025]
 
-    def __init__(self, input_dir):
+    def __init__(self, input_dir, device):
         self.input_files = [os.path.join(root, f) for root, _, files in os.walk(input_dir) for f in files if f.endswith('.wav')]
+        self.device = device
 
 
     def __len__(self):
@@ -32,4 +33,7 @@ class AudioDataset(Dataset):
         resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
         low_quality_audio = resample_transform_high(low_quality_audio)
 
-        return (AudioUtils.stereo_tensor_to_mono(high_quality_audio), original_sample_rate), (AudioUtils.stereo_tensor_to_mono(low_quality_audio), mangled_sample_rate)
+        high_quality_audio = AudioUtils.stereo_tensor_to_mono(high_quality_audio).to(self.device)
+        low_quality_audio = AudioUtils.stereo_tensor_to_mono(low_quality_audio).to(self.device)
+
+        return (high_quality_audio, original_sample_rate), (low_quality_audio, mangled_sample_rate)

requirements.txt

@@ -4,11 +4,11 @@ Jinja2==3.1.4
 MarkupSafe==2.1.5
 mpmath==1.3.0
 networkx==3.4.2
-numpy==2.2.1
+numpy==2.2.3
-pytorch-triton-rocm==3.2.0+git0d4682f0
+pytorch-triton-rocm==3.2.0+git4b3bb1f8
 setuptools==70.2.0
-sympy==1.13.1
+sympy==1.13.3
-torch==2.6.0.dev20241222+rocm6.2.4
+torch==2.7.0.dev20250226+rocm6.3
-torchaudio==2.6.0.dev20241222+rocm6.2.4
+torchaudio==2.6.0.dev20250226+rocm6.3
 tqdm==4.67.1
 typing_extensions==4.12.2

training.py

@@ -20,49 +20,35 @@ from data import AudioDataset
 from generator import SISUGenerator
 from discriminator import SISUDiscriminator
 
-import librosa
+import torchaudio.transforms as T
-import numpy as np
 
-def mfcc_loss(y_true, y_pred, sr):
+# Init script argument parser
-    """Calculates MFCC loss between two audio signals.
+parser = argparse.ArgumentParser(description="Training script")
+parser.add_argument("--generator", type=str, default=None,
+                    help="Path to the generator model file")
+parser.add_argument("--discriminator", type=str, default=None,
+                    help="Path to the discriminator model file")
+parser.add_argument("--device", type=str, default="cpu",  help="Select device")
+parser.add_argument("--epoch", type=int, default=0, help="Current epoch for model versioning")
+parser.add_argument("--verbose", action="store_true",  help="Increase output verbosity")
 
-    Args:
+args = parser.parse_args()
-        y_true: Target audio signal (PyTorch tensor).
-        y_pred: Predicted audio signal (PyTorch tensor).
-        sr: Sample rate (NumPy scalar).
 
-    Returns:
+device = torch.device(args.device if torch.cuda.is_available() else "cpu")
-        MFCC loss (PyTorch tensor).
+print(f"Using device: {device}")
-    """
 
-    # 1. Ensure sr is a NumPy scalar (not a Tensor)
+mfcc_transform = T.MFCC(
-    if isinstance(sr, torch.Tensor):
+    sample_rate=16000,  # Adjust to your sample rate
-        sr = sr.item()
+    n_mfcc=20,
-
+    melkwargs={'n_fft': 2048, 'hop_length': 512} # adjust n_fft and hop_length to your needs.
-    # 2. Convert y_true and y_pred to NumPy arrays (and detach from graph)
+).to(device)
-    y_true_np = y_true.cpu().detach().numpy()[0]  # .cpu() and .detach() are crucial!
-    y_pred_np = y_pred.cpu().detach().numpy()[0]
-
-    # 3. Dynamically calculate n_fft based on signal length
-    signal_length = min(y_true_np.shape[0], y_pred_np.shape[0]) # Use shortest signal length
-    n_fft = min(2048, 2**int(np.log2(signal_length)))  # Power of 2, up to 2048
-
-    # 4. Calculate MFCCs using adjusted n_fft
-    mfccs_true = librosa.feature.mfcc(y=y_true_np, sr=sr, n_fft=n_fft, n_mfcc=20)
-    mfccs_pred = librosa.feature.mfcc(y=y_pred_np, sr=sr, n_fft=n_fft, n_mfcc=20)
-
-    # 5. Truncate MFCCs to the same length (important!)
-    len_true = mfccs_true.shape[1]
-    len_pred = mfccs_pred.shape[1]
-    min_len = min(len_true, len_pred)
-
-    mfccs_true = mfccs_true[:, :min_len]
-    mfccs_pred = mfccs_pred[:, :min_len]
-
-    # 6. Convert MFCCs back to PyTorch tensors and ensure correct device
-    mfccs_true = torch.tensor(mfccs_true, device=y_true.device, dtype=torch.float32)
-    mfccs_pred = torch.tensor(mfccs_pred, device=y_pred.device, dtype=torch.float32)
 
+def gpu_mfcc_loss(y_true, y_pred):
+    mfccs_true = mfcc_transform(y_true)
+    mfccs_pred = mfcc_transform(y_pred)
+    min_len = min(mfccs_true.shape[2], mfccs_pred.shape[2])
+    mfccs_true = mfccs_true[:, :, :min_len]
+    mfccs_pred = mfccs_pred[:, :, :min_len]
     return torch.mean((mfccs_true - mfccs_pred)**2)
 
 def discriminator_train(high_quality, low_quality, real_labels, fake_labels):
@@ -93,7 +79,7 @@ def generator_train(low_quality, high_quality, real_labels):
     # Forward pass for fake samples (from generator output)
     generator_output = generator(low_quality[0])
 
-    mfcc_l = mfcc_loss(high_quality[0], generator_output, high_quality[1])
+    mfcc_l = gpu_mfcc_loss(high_quality[0], generator_output)
 
     discriminator_decision = discriminator(generator_output)
     adversarial_loss = criterion_g(discriminator_decision, real_labels)
@@ -105,26 +91,11 @@ def generator_train(low_quality, high_quality, real_labels):
 
     return (generator_output, combined_loss, adversarial_loss, mfcc_l)
 
-# Init script argument parser
-parser = argparse.ArgumentParser(description="Training script")
-parser.add_argument("--generator", type=str, default=None,
-                    help="Path to the generator model file")
-parser.add_argument("--discriminator", type=str, default=None,
-                    help="Path to the discriminator model file")
-parser.add_argument("--epoch", type=int, default=0, help="Current epoch for model versioning")
-parser.add_argument("--verbose", action="store_true",  help="Increase output verbosity")
-
-args = parser.parse_args()
-
-# Check for CUDA availability
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
 debug = args.verbose
 
 # Initialize dataset and dataloader
 dataset_dir = './dataset/good'
-dataset = AudioDataset(dataset_dir)
+dataset = AudioDataset(dataset_dir, device)
 
 # ========= MULTIPLE =========
 
@@ -147,14 +118,14 @@ discriminator = SISUDiscriminator()
 
 epoch: int = args.epoch
 
-if args.generator is not None:
-    generator.load_state_dict(torch.load(args.generator, weights_only=True))
-if args.discriminator is not None:
-    discriminator.load_state_dict(torch.load(args.discriminator, weights_only=True))
-
 generator = generator.to(device)
 discriminator = discriminator.to(device)
 
+if args.generator is not None:
+    generator.load_state_dict(torch.load(args.generator, map_location=device, weights_only=True))
+if args.discriminator is not None:
+    discriminator.load_state_dict(torch.load(args.discriminator, map_location=device, weights_only=True))
+
 # Loss
 criterion_g = nn.MSELoss()
 criterion_d = nn.BCELoss()
@@ -182,8 +153,8 @@ def start_training():
         # ========= TRAINING =========
         for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Training epoch {generator_epoch+1}/{generator_epochs}, Current epoch {epoch+1}"):
         # for high_quality_clip, low_quality_clip in train_data_loader:
-            high_quality_sample = (high_quality_clip[0].to(device), high_quality_clip[1])
+            high_quality_sample = (high_quality_clip[0], high_quality_clip[1])
-            low_quality_sample = (low_quality_clip[0].to(device), low_quality_clip[1])
+            low_quality_sample = (low_quality_clip[0], low_quality_clip[1])
 
             # ========= LABELS =========
             batch_size = high_quality_clip[0].size(0)