✨ | Made training bit... spicier.

app.py

@@ -1,33 +1,49 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-
-import torch.nn.functional as F
-import torchaudio
-import tqdm
-
 import argparse
-import math
+
-import os
+import torch
+import torchaudio
+import torchcodec
+import tqdm
 
 import AudioUtils
 from generator import SISUGenerator
 
-
 # Init script argument parser
 parser = argparse.ArgumentParser(description="Training script")
-parser.add_argument("--device", type=str, default="cpu",  help="Select device")
+parser.add_argument("--device", type=str, default="cpu", help="Select device")
 parser.add_argument("--model", type=str, help="Model to use for upscaling")
-parser.add_argument("--clip_length", type=int, default=1024, help="Internal clip length, leave unspecified if unsure")
+parser.add_argument(
+    "--clip_length",
+    type=int,
+    default=16384,
+    help="Internal clip length, leave unspecified if unsure",
+)
+parser.add_argument(
+    "--sample_rate", type=int, default=44100, help="Output clip sample rate"
+)
+parser.add_argument(
+    "--bitrate",
+    type=int,
+    default=192000,
+    help="Output clip bitrate",
+)
 parser.add_argument("-i", "--input", type=str, help="Input audio file")
 parser.add_argument("-o", "--output", type=str, help="Output audio file")
 
 args = parser.parse_args()
 
+if args.sample_rate < 8000:
+    print(
+        "Sample rate cannot be lower than 8000! (44100 is recommended for base models)"
+    )
+    exit()
+
 device = torch.device(args.device if torch.cuda.is_available() else "cpu")
 print(f"Using device: {device}")
 
-generator = SISUGenerator()
+generator = SISUGenerator().to(device)
+
+generator = torch.compile(generator)
 
 models_dir = args.model
 clip_length = args.clip_length
@@ -35,17 +51,30 @@ input_audio = args.input
 output_audio = args.output
 
 if models_dir:
-    generator.load_state_dict(torch.load(f"{models_dir}", map_location=device, weights_only=True))
+    ckpt = torch.load(models_dir, map_location=device)
+    generator.load_state_dict(ckpt["G"])
 else:
-    print(f"Generator model (--model) isn't specified. Do you have the trained model? If not you need to train it OR acquire it from somewhere (DON'T ASK ME, YET!)")
+    print(
+        "Generator model (--model) isn't specified. Do you have the trained model? If not, you need to train it OR acquire it from somewhere (DON'T ASK ME, YET!)"
+    )
 
-generator = generator.to(device)
 
 def start():
     # To Mono!
-    audio, original_sample_rate = torchaudio.load(input_audio, normalize=True)
+    decoder = torchcodec.decoders.AudioDecoder(input_audio)
+
+    decoded_samples = decoder.get_all_samples()
+    audio = decoded_samples.data
+    original_sample_rate = decoded_samples.sample_rate
+
     audio = AudioUtils.stereo_tensor_to_mono(audio)
 
+    resample_transform = torchaudio.transforms.Resample(
+        original_sample_rate, args.sample_rate
+    )
+
+    audio = resample_transform(audio)
+
     splitted_audio = AudioUtils.split_audio(audio, clip_length)
     splitted_audio_on_device = [t.to(device) for t in splitted_audio]
     processed_audio = []
@@ -55,6 +84,13 @@ def start():
 
     reconstructed_audio = AudioUtils.reconstruct_audio(processed_audio)
     print(f"Saving {output_audio}!")
-    torchaudio.save(output_audio, reconstructed_audio.cpu().detach(), original_sample_rate)
+    torchaudio.save_with_torchcodec(
+        uri=output_audio,
+        src=reconstructed_audio,
+        sample_rate=args.sample_rate,
+        channels_first=True,
+        compression=args.bitrate,
+    )
+
 
 start()

data.py

@@ -1,41 +1,68 @@
-from torch.utils.data import Dataset
-import torch.nn.functional as F
-import torch
-import torchaudio
 import os
 import random
-import torchaudio.transforms as T
+
+import torchaudio
+import torchcodec.decoders as decoders
 import tqdm
+from torch.utils.data import Dataset
+
 import AudioUtils
 
+
 class AudioDataset(Dataset):
     audio_sample_rates = [11025]
 
-    def __init__(self, input_dir, device, clip_length = 1024):
+    def __init__(self, input_dir, clip_length=16384):
-        self.device = device
+        input_files = [
-        input_files = [os.path.join(root, f) for root, _, files in os.walk(input_dir) for f in files if f.endswith('.wav') or f.endswith('.mp3') or f.endswith('.flac')]
+            os.path.join(root, f)
+            for root, _, files in os.walk(input_dir)
+            for f in files
+            if f.endswith(".wav") or f.endswith(".mp3") or f.endswith(".flac")
+        ]
 
         data = []
-        for audio_clip in tqdm.tqdm(input_files, desc=f"Processing {len(input_files)} audio file(s)"):
+        for audio_clip in tqdm.tqdm(
-            audio, original_sample_rate = torchaudio.load(audio_clip, normalize=True)
+            input_files, desc=f"Processing {len(input_files)} audio file(s)"
+        ):
+            decoder = decoders.AudioDecoder(audio_clip)
+
+            decoded_samples = decoder.get_all_samples()
+            audio = decoded_samples.data
+            original_sample_rate = decoded_samples.sample_rate
+
             audio = AudioUtils.stereo_tensor_to_mono(audio)
 
             # Generate low-quality audio with random downsampling
             mangled_sample_rate = random.choice(self.audio_sample_rates)
-            resample_transform_low = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
+            resample_transform_low = torchaudio.transforms.Resample(
-            resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
+                original_sample_rate, mangled_sample_rate
+            )
+            resample_transform_high = torchaudio.transforms.Resample(
+                mangled_sample_rate, original_sample_rate
+            )
 
             low_audio = resample_transform_low(audio)
             low_audio = resample_transform_high(low_audio)
 
             splitted_high_quality_audio = AudioUtils.split_audio(audio, clip_length)
-            splitted_high_quality_audio[-1] = AudioUtils.pad_tensor(splitted_high_quality_audio[-1], clip_length)
+            splitted_high_quality_audio[-1] = AudioUtils.pad_tensor(
+                splitted_high_quality_audio[-1], clip_length
+            )
 
             splitted_low_quality_audio = AudioUtils.split_audio(low_audio, clip_length)
-            splitted_low_quality_audio[-1] = AudioUtils.pad_tensor(splitted_low_quality_audio[-1], clip_length)
+            splitted_low_quality_audio[-1] = AudioUtils.pad_tensor(
+                splitted_low_quality_audio[-1], clip_length
+            )
 
-            for high_quality_sample, low_quality_sample in zip(splitted_high_quality_audio, splitted_low_quality_audio):
+            for high_quality_sample, low_quality_sample in zip(
-                data.append(((high_quality_sample, low_quality_sample), (original_sample_rate, mangled_sample_rate)))
+                splitted_high_quality_audio, splitted_low_quality_audio
+            ):
+                data.append(
+                    (
+                        (high_quality_sample, low_quality_sample),
+                        (original_sample_rate, mangled_sample_rate),
+                    )
+                )
 
         self.audio_data = data
 

discriminator.py

@@ -1,8 +1,16 @@
-import torch
 import torch.nn as nn
 import torch.nn.utils as utils
 
-def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, spectral_norm=True, use_instance_norm=True):
+
+def discriminator_block(
+    in_channels,
+    out_channels,
+    kernel_size=3,
+    stride=1,
+    dilation=1,
+    spectral_norm=True,
+    use_instance_norm=True,
+):
     padding = (kernel_size // 2) * dilation
     conv_layer = nn.Conv1d(
         in_channels,
@@ -10,7 +18,7 @@ def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dila
         kernel_size=kernel_size,
         stride=stride,
         dilation=dilation,
-        padding=padding
+        padding=padding,
     )
 
     if spectral_norm:
@@ -24,6 +32,7 @@ def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dila
 
     return nn.Sequential(*layers)
 
+
 class AttentionBlock(nn.Module):
     def __init__(self, channels):
         super(AttentionBlock, self).__init__()
@@ -31,27 +40,86 @@ class AttentionBlock(nn.Module):
             nn.Conv1d(channels, channels // 4, kernel_size=1),
             nn.ReLU(inplace=True),
             nn.Conv1d(channels // 4, channels, kernel_size=1),
-            nn.Sigmoid()
+            nn.Sigmoid(),
         )
 
     def forward(self, x):
         attention_weights = self.attention(x)
         return x * attention_weights
 
+
 class SISUDiscriminator(nn.Module):
     def __init__(self, base_channels=16):
         super(SISUDiscriminator, self).__init__()
         layers = base_channels
         self.model = nn.Sequential(
-            discriminator_block(1, layers, kernel_size=7, stride=1, spectral_norm=True, use_instance_norm=False),
+            discriminator_block(
-            discriminator_block(layers, layers * 2, kernel_size=5, stride=2, spectral_norm=True, use_instance_norm=True),
+                1,
-            discriminator_block(layers * 2, layers * 4, kernel_size=5, stride=1, dilation=2, spectral_norm=True, use_instance_norm=True),
+                layers,
+                kernel_size=7,
+                stride=1,
+                spectral_norm=True,
+                use_instance_norm=False,
+            ),
+            discriminator_block(
+                layers,
+                layers * 2,
+                kernel_size=5,
+                stride=2,
+                spectral_norm=True,
+                use_instance_norm=True,
+            ),
+            discriminator_block(
+                layers * 2,
+                layers * 4,
+                kernel_size=5,
+                stride=1,
+                dilation=2,
+                spectral_norm=True,
+                use_instance_norm=True,
+            ),
             AttentionBlock(layers * 4),
-            discriminator_block(layers * 4, layers * 8, kernel_size=5, stride=1, dilation=4, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(
-            discriminator_block(layers * 8, layers * 4, kernel_size=5, stride=2, spectral_norm=True, use_instance_norm=True),
+                layers * 4,
-            discriminator_block(layers * 4, layers * 2, kernel_size=3, stride=1, spectral_norm=True, use_instance_norm=True),
+                layers * 8,
-            discriminator_block(layers * 2, layers, kernel_size=3, stride=1, spectral_norm=True, use_instance_norm=True),
+                kernel_size=5,
-            discriminator_block(layers, 1, kernel_size=3, stride=1, spectral_norm=False, use_instance_norm=False)
+                stride=1,
+                dilation=4,
+                spectral_norm=True,
+                use_instance_norm=True,
+            ),
+            discriminator_block(
+                layers * 8,
+                layers * 4,
+                kernel_size=5,
+                stride=2,
+                spectral_norm=True,
+                use_instance_norm=True,
+            ),
+            discriminator_block(
+                layers * 4,
+                layers * 2,
+                kernel_size=3,
+                stride=1,
+                spectral_norm=True,
+                use_instance_norm=True,
+            ),
+            discriminator_block(
+                layers * 2,
+                layers,
+                kernel_size=3,
+                stride=1,
+                spectral_norm=True,
+                use_instance_norm=True,
+            ),
+            discriminator_block(
+                layers,
+                1,
+                kernel_size=3,
+                stride=1,
+                spectral_norm=False,
+                use_instance_norm=False,
+            ),
         )
 
         self.global_avg_pool = nn.AdaptiveAvgPool1d(1)

generator.py

@@ -1,6 +1,6 @@
-import torch
 import torch.nn as nn
 
+
 def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
     return nn.Sequential(
         nn.Conv1d(
@@ -8,29 +8,32 @@ def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
             out_channels,
             kernel_size=kernel_size,
             dilation=dilation,
-            padding=(kernel_size // 2) * dilation
+            padding=(kernel_size // 2) * dilation,
         ),
         nn.InstanceNorm1d(out_channels),
-        nn.PReLU()
+        nn.PReLU(),
     )
 
+
 class AttentionBlock(nn.Module):
     """
     Simple Channel Attention Block. Learns to weight channels based on their importance.
     """
+
     def __init__(self, channels):
         super(AttentionBlock, self).__init__()
         self.attention = nn.Sequential(
             nn.Conv1d(channels, channels // 4, kernel_size=1),
             nn.ReLU(inplace=True),
             nn.Conv1d(channels // 4, channels, kernel_size=1),
-            nn.Sigmoid()
+            nn.Sigmoid(),
         )
 
     def forward(self, x):
         attention_weights = self.attention(x)
         return x * attention_weights
 
+
 class ResidualInResidualBlock(nn.Module):
     def __init__(self, channels, num_convs=3):
         super(ResidualInResidualBlock, self).__init__()
@@ -47,6 +50,7 @@ class ResidualInResidualBlock(nn.Module):
         x = self.attention(x)
         return x + residual
 
+
 class SISUGenerator(nn.Module):
     def __init__(self, channels=16, num_rirb=4, alpha=1.0):
         super(SISUGenerator, self).__init__()

training.py

@@ -1,65 +1,74 @@
+import argparse
+import os
+
 import torch
 import torch.nn as nn
 import torch.optim as optim
-
+import torchaudio.transforms as T
-import torch.nn.functional as F
-import torchaudio
 import tqdm
-
+from torch.amp import GradScaler, autocast
-import argparse
-
-import math
-
-import os
-
-from torch.utils.data import random_split
 from torch.utils.data import DataLoader
 
-import AudioUtils
+import training_utils
 from data import AudioDataset
-from generator import SISUGenerator
 from discriminator import SISUDiscriminator
-
+from generator import SISUGenerator
 from training_utils import discriminator_train, generator_train
-import file_utils as Data
-
-import torchaudio.transforms as T
-
-# 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("--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("--debug", action="store_true",  help="Print debug logs")
-parser.add_argument("--continue_training", action="store_true", help="Continue training using temp_generator and temp_discriminator models")
 
+# ---------------------------
+# Argument parsing
+# ---------------------------
+parser = argparse.ArgumentParser(description="Training script (safer defaults)")
+parser.add_argument("--resume", action="store_true", help="Resume training")
+parser.add_argument(
+    "--device", type=str, default="cuda", help="Device (cuda, cpu, mps)"
+)
+parser.add_argument(
+    "--epochs", type=int, default=5000, help="Number of training epochs"
+)
+parser.add_argument("--batch_size", type=int, default=8, help="Batch size")
+parser.add_argument("--num_workers", type=int, default=2, help="DataLoader num_workers")
+parser.add_argument("--debug", action="store_true", help="Print debug logs")
+parser.add_argument(
+    "--no_pin_memory", action="store_true", help="Disable pin_memory even on CUDA"
+)
 args = parser.parse_args()
 
-device = torch.device(args.device if torch.cuda.is_available() else "cpu")
+# ---------------------------
+# Device setup
+# ---------------------------
+# Use requested device only if available
+device = torch.device(
+    args.device if (args.device != "cuda" or torch.cuda.is_available()) else "cpu"
+)
 print(f"Using device: {device}")
+# sensible performance flags
+if device.type == "cuda":
+    torch.backends.cudnn.benchmark = True
+    # optional: torch.set_float32_matmul_precision("high")
+debug = args.debug
 
-# Parameters
+# ---------------------------
+# Audio transforms
+# ---------------------------
 sample_rate = 44100
 n_fft = 1024
 win_length = n_fft
 hop_length = n_fft // 4
-n_mels = 40
+n_mels = 96
-n_mfcc = 13
+# n_mfcc = 13
 
-mfcc_transform = T.MFCC(
+# mfcc_transform = T.MFCC(
-    sample_rate=sample_rate,
+#     sample_rate=sample_rate,
-    n_mfcc=n_mfcc,
+#     n_mfcc=n_mfcc,
-    melkwargs={
+#     melkwargs=dict(
-        'n_fft': n_fft,
+#         n_fft=n_fft,
-        'hop_length': hop_length,
+#         hop_length=hop_length,
-        'win_length': win_length,
+#         win_length=win_length,
-        'n_mels': n_mels,
+#         n_mels=n_mels,
-        'power': 1.0,
+#         power=1.0,
-    }
+#     ),
-).to(device)
+# ).to(device)
 
 mel_transform = T.MelSpectrogram(
     sample_rate=sample_rate,
@@ -67,138 +76,198 @@ mel_transform = T.MelSpectrogram(
     hop_length=hop_length,
     win_length=win_length,
     n_mels=n_mels,
-    power=1.0  # Magnitude Mel
+    power=1.0,
 ).to(device)
 
 stft_transform = T.Spectrogram(
-    n_fft=n_fft,
+    n_fft=n_fft, win_length=win_length, hop_length=hop_length
-    win_length=win_length,
-    hop_length=hop_length
 ).to(device)
-debug = args.debug
 
-# Initialize dataset and dataloader
+# training_utils.init(mel_transform, stft_transform, mfcc_transform)
-dataset_dir = './dataset/good'
+training_utils.init(mel_transform, stft_transform)
-dataset = AudioDataset(dataset_dir, device)
-models_dir = "./models"
-os.makedirs(models_dir, exist_ok=True)
-audio_output_dir = "./output"
-os.makedirs(audio_output_dir, exist_ok=True)
 
-# ========= SINGLE =========
+# ---------------------------
+# Dataset / DataLoader
+# ---------------------------
+dataset_dir = "./dataset/good"
+dataset = AudioDataset(dataset_dir)
 
-train_data_loader = DataLoader(dataset, batch_size=2048, shuffle=True, num_workers=24)
+train_loader = DataLoader(
+    dataset,
+    batch_size=args.batch_size,
+    shuffle=True,
+    num_workers=args.num_workers,
+    pin_memory=True,
+    persistent_workers=True,
+)
 
+# ---------------------------
+# Models
+# ---------------------------
+generator = SISUGenerator().to(device)
+discriminator = SISUDiscriminator().to(device)
 
-# ========= MODELS =========
+generator = torch.compile(generator)
+discriminator = torch.compile(discriminator)
 
-generator = SISUGenerator()
+# ---------------------------
-discriminator = SISUDiscriminator()
+# Losses / Optimizers / Scalers
-
+# ---------------------------
-epoch: int = args.epoch
-
-if args.continue_training:
-    if args.generator is not None:
-        generator.load_state_dict(torch.load(args.generator, map_location=device, weights_only=True))
-    elif args.discriminator is not None:
-        discriminator.load_state_dict(torch.load(args.discriminator, map_location=device, weights_only=True))
-    else:
-        generator.load_state_dict(torch.load(f"{models_dir}/temp_generator.pt", map_location=device, weights_only=True))
-        discriminator.load_state_dict(torch.load(f"{models_dir}/temp_discriminator.pt", map_location=device, weights_only=True))
-
-        epoch_from_file = Data.read_data(f"{models_dir}/epoch_data.json")
-        epoch = epoch_from_file["epoch"] + 1
-
-generator = generator.to(device)
-discriminator = discriminator.to(device)
-
-# Loss
 criterion_g = nn.BCEWithLogitsLoss()
 criterion_d = nn.BCEWithLogitsLoss()
 
-# Optimizers
+optimizer_g = optim.AdamW(
-optimizer_g = optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
+    generator.parameters(), lr=0.0003, betas=(0.5, 0.999), weight_decay=0.0001
-optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0001, betas=(0.5, 0.999))
+)
+optimizer_d = optim.AdamW(
+    discriminator.parameters(), lr=0.0003, betas=(0.5, 0.999), weight_decay=0.0001
+)
 
-# Scheduler
+# Use modern GradScaler signature; choose device_type based on runtime device.
-scheduler_g = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_g, mode='min', factor=0.5, patience=5)
+scaler = GradScaler(device=device)
-scheduler_d = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_d, mode='min', factor=0.5, patience=5)
 
-def start_training():
+scheduler_g = torch.optim.lr_scheduler.ReduceLROnPlateau(
-    generator_epochs = 5000
+    optimizer_g, mode="min", factor=0.5, patience=5
-    for generator_epoch in range(generator_epochs):
+)
-        high_quality_audio = ([torch.empty((1))], 1)
+scheduler_d = torch.optim.lr_scheduler.ReduceLROnPlateau(
-        low_quality_audio = ([torch.empty((1))], 1)
+    optimizer_d, mode="min", factor=0.5, patience=5
-        ai_enhanced_audio = ([torch.empty((1))], 1)
+)
 
-        times_correct = 0
+# ---------------------------
-
+# Checkpoint helpers
-        # ========= TRAINING =========
+# ---------------------------
-        for training_data in tqdm.tqdm(train_data_loader, desc=f"Training epoch {generator_epoch+1}/{generator_epochs}, Current epoch {epoch+1}"):
+models_dir = "./models"
-            ## Data structure:
+os.makedirs(models_dir, exist_ok=True)
-            # [[[float..., float..., float...], [float..., float..., float...]], [original_sample_rate, mangled_sample_rate]]
-
-            # ========= LABELS =========
-            good_quality_data = training_data[0][0].to(device)
-            bad_quality_data = training_data[0][1].to(device)
-            original_sample_rate = training_data[1][0]
-            mangled_sample_rate = training_data[1][1]
-
-            batch_size = good_quality_data.size(0)
-            real_labels = torch.ones(batch_size, 1).to(device)
-            fake_labels = torch.zeros(batch_size, 1).to(device)
-
-            high_quality_audio = (good_quality_data, original_sample_rate)
-            low_quality_audio = (bad_quality_data, mangled_sample_rate)
-
-            # ========= DISCRIMINATOR =========
-            discriminator.train()
-            d_loss = discriminator_train(
-                good_quality_data,
-                bad_quality_data,
-                real_labels,
-                fake_labels,
-                discriminator,
-                generator,
-                criterion_d,
-                optimizer_d
-            )
-
-            # ========= GENERATOR =========
-            generator.train()
-            generator_output, combined_loss, adversarial_loss, mel_l1_tensor, log_stft_l1_tensor, mfcc_l_tensor = generator_train(
-                bad_quality_data,
-                good_quality_data,
-                real_labels,
-                generator,
-                discriminator,
-                criterion_d,
-                optimizer_g,
-                device,
-                mel_transform,
-                stft_transform,
-                mfcc_transform
-            )
-
-            if debug:
-                print(f"D_LOSS: {d_loss.item():.4f}, COMBINED_LOSS: {combined_loss.item():.4f}, ADVERSARIAL_LOSS: {adversarial_loss.item():.4f}, MEL_L1_LOSS: {mel_l1_tensor.item():.4f}, LOG_STFT_L1_LOSS: {log_stft_l1_tensor.item():.4f}, MFCC_LOSS: {mfcc_l_tensor.item():.4f}")
-            scheduler_d.step(d_loss.detach())
-            scheduler_g.step(adversarial_loss.detach())
-
-            # ========= SAVE LATEST AUDIO =========
-            high_quality_audio = (good_quality_data, original_sample_rate)
-            low_quality_audio = (bad_quality_data, original_sample_rate)
-            ai_enhanced_audio = (generator_output, original_sample_rate)
-
-        torch.save(discriminator.state_dict(), f"{models_dir}/temp_discriminator.pt")
-        torch.save(generator.state_dict(), f"{models_dir}/temp_generator.pt")
-
-        new_epoch = generator_epoch+epoch
-        Data.write_data(f"{models_dir}/epoch_data.json", {"epoch": new_epoch})
 
 
-    torch.save(discriminator, "models/epoch-5000-discriminator.pt")
+def save_ckpt(path, epoch):
-    torch.save(generator, "models/epoch-5000-generator.pt")
+    torch.save(
-    print("Training complete!")
+        {
+            "epoch": epoch,
+            "G": generator.state_dict(),
+            "D": discriminator.state_dict(),
+            "optG": optimizer_g.state_dict(),
+            "optD": optimizer_d.state_dict(),
+            "scaler": scaler.state_dict(),
+            "schedG": scheduler_g.state_dict(),
+            "schedD": scheduler_d.state_dict(),
+        },
+        path,
+    )
 
-start_training()
+
+start_epoch = 0
+if args.resume:
+    ckpt = torch.load(os.path.join(models_dir, "last.pt"), map_location=device)
+    generator.load_state_dict(ckpt["G"])
+    discriminator.load_state_dict(ckpt["D"])
+    optimizer_g.load_state_dict(ckpt["optG"])
+    optimizer_d.load_state_dict(ckpt["optD"])
+    scaler.load_state_dict(ckpt["scaler"])
+    scheduler_g.load_state_dict(ckpt["schedG"])
+    scheduler_d.load_state_dict(ckpt["schedD"])
+    start_epoch = ckpt.get("epoch", 1)
+
+# ---------------------------
+# Training loop (safer)
+# ---------------------------
+
+if not train_loader or not train_loader.batch_size:
+    print("There is no data to train with! Exiting...")
+    exit()
+
+max_batch = max(1, train_loader.batch_size)
+real_buf = torch.full((max_batch, 1), 0.9, device=device)  # label smoothing
+fake_buf = torch.zeros(max_batch, 1, device=device)
+
+try:
+    for epoch in range(start_epoch, args.epochs):
+        generator.train()
+        discriminator.train()
+
+        running_d, running_g, steps = 0.0, 0.0, 0
+
+        for i, (
+            (high_quality, low_quality),
+            (high_sample_rate, low_sample_rate),
+        ) in enumerate(tqdm.tqdm(train_loader, desc=f"Epoch {epoch}")):
+            batch_size = high_quality.size(0)
+
+            high_quality = high_quality.to(device, non_blocking=True)
+            low_quality = low_quality.to(device, non_blocking=True)
+
+            real_labels = real_buf[:batch_size]
+            fake_labels = fake_buf[:batch_size]
+
+            # --- Discriminator ---
+            optimizer_d.zero_grad(set_to_none=True)
+            with autocast(device_type=device.type):
+                d_loss = discriminator_train(
+                    high_quality,
+                    low_quality,
+                    real_labels,
+                    fake_labels,
+                    discriminator,
+                    generator,
+                    criterion_d,
+                )
+
+            scaler.scale(d_loss).backward()
+            scaler.unscale_(optimizer_d)
+            torch.nn.utils.clip_grad_norm_(discriminator.parameters(), 1.0)
+            scaler.step(optimizer_d)
+
+            # --- Generator ---
+            optimizer_g.zero_grad(set_to_none=True)
+            with autocast(device_type=device.type):
+                g_out, g_total, g_adv = generator_train(
+                    low_quality,
+                    high_quality,
+                    real_labels,
+                    generator,
+                    discriminator,
+                    criterion_d,
+                )
+
+            scaler.scale(g_total).backward()
+            scaler.unscale_(optimizer_g)
+            torch.nn.utils.clip_grad_norm_(generator.parameters(), 1.0)
+            scaler.step(optimizer_g)
+
+            scaler.update()
+
+            running_d += float(d_loss.detach().cpu().item())
+            running_g += float(g_total.detach().cpu().item())
+            steps += 1
+
+        # epoch averages & schedulers
+        if steps == 0:
+            print("No steps in epoch (empty dataloader?). Exiting.")
+            break
+
+        mean_d = running_d / steps
+        mean_g = running_g / steps
+
+        scheduler_d.step(mean_d)
+        scheduler_g.step(mean_g)
+
+        save_ckpt(os.path.join(models_dir, "last.pt"), epoch)
+        print(f"Epoch {epoch} done | D {mean_d:.4f} | G {mean_g:.4f}")
+
+except Exception:
+    try:
+        save_ckpt(os.path.join(models_dir, "crash_last.pt"), epoch)
+        print(f"Saved crash checkpoint for epoch {epoch}")
+    except Exception as e:
+        print("Failed saving crash checkpoint:", e)
+    raise
+
+try:
+    torch.save(generator.state_dict(), os.path.join(models_dir, "final_generator.pt"))
+    torch.save(
+        discriminator.state_dict(), os.path.join(models_dir, "final_discriminator.pt")
+    )
+except Exception as e:
+    print("Failed to save final states:", e)
+
+print("Training finished.")

training_utils.py

@@ -1,89 +1,88 @@
 import torch
-import torch.nn as nn
-import torch.optim as optim
-
-import torchaudio
 import torchaudio.transforms as T
 
-def gpu_mfcc_loss(mfcc_transform, y_true, y_pred):
+from utils.MultiResolutionSTFTLoss import MultiResolutionSTFTLoss
-    mfccs_true = mfcc_transform(y_true)
-    mfccs_pred = mfcc_transform(y_pred)
 
-    min_len = min(mfccs_true.shape[2], mfccs_pred.shape[2])
+mel_transform: T.MelSpectrogram
-    mfccs_true = mfccs_true[:, :, :min_len]
+stft_transform: T.Spectrogram
-    mfccs_pred = mfccs_pred[:, :, :min_len]
+# mfcc_transform: T.MFCC
 
-    loss = torch.mean((mfccs_true - mfccs_pred)**2)
-    return loss
 
-def mel_spectrogram_l1_loss(mel_transform: T.MelSpectrogram, y_true: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:
+# def init(mel_trans: T.MelSpectrogram, stft_trans: T.Spectrogram, mfcc_trans: T.MFCC):
-    mel_spec_true = mel_transform(y_true)
+#     """Initializes the global transform variables for the module."""
-    mel_spec_pred = mel_transform(y_pred)
+#     global mel_transform, stft_transform, mfcc_transform
+#     mel_transform = mel_trans
+#     stft_transform = stft_trans
+#     mfcc_transform = mfcc_trans
 
-    min_len = min(mel_spec_true.shape[-1], mel_spec_pred.shape[-1])
-    mel_spec_true = mel_spec_true[..., :min_len]
-    mel_spec_pred = mel_spec_pred[..., :min_len]
 
-    loss = torch.mean(torch.abs(mel_spec_true - mel_spec_pred))
+def init(mel_trans: T.MelSpectrogram, stft_trans: T.Spectrogram):
-    return loss
+    """Initializes the global transform variables for the module."""
+    global mel_transform, stft_transform
+    mel_transform = mel_trans
+    stft_transform = stft_trans
 
-def mel_spectrogram_l2_loss(mel_transform: T.MelSpectrogram, y_true: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:
-    mel_spec_true = mel_transform(y_true)
-    mel_spec_pred = mel_transform(y_pred)
 
-    min_len = min(mel_spec_true.shape[-1], mel_spec_pred.shape[-1])
+# def mfcc_loss(y_true: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:
-    mel_spec_true = mel_spec_true[..., :min_len]
+#     """Computes the Mean Squared Error (MSE) loss on MFCCs."""
-    mel_spec_pred = mel_spec_pred[..., :min_len]
+#     mfccs_true = mfcc_transform(y_true)
+#     mfccs_pred = mfcc_transform(y_pred)
+#     return F.mse_loss(mfccs_pred, mfccs_true)
 
-    loss = torch.mean((mel_spec_true - mel_spec_pred)**2)
-    return loss
 
-def log_stft_magnitude_loss(stft_transform: T.Spectrogram, y_true: torch.Tensor, y_pred: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+# def mel_spectrogram_loss(
-    stft_mag_true = stft_transform(y_true)
+#     y_true: torch.Tensor, y_pred: torch.Tensor, loss_type: str = "l1"
-    stft_mag_pred = stft_transform(y_pred)
+# ) -> torch.Tensor:
+#     """Calculates L1 or L2 loss on the Mel Spectrogram."""
+#     mel_spec_true = mel_transform(y_true)
+#     mel_spec_pred = mel_transform(y_pred)
+#     if loss_type == "l1":
+#         return F.l1_loss(mel_spec_pred, mel_spec_true)
+#     elif loss_type == "l2":
+#         return F.mse_loss(mel_spec_pred, mel_spec_true)
+#     else:
+#         raise ValueError("loss_type must be 'l1' or 'l2'")
 
-    min_len = min(stft_mag_true.shape[-1], stft_mag_pred.shape[-1])
-    stft_mag_true = stft_mag_true[..., :min_len]
-    stft_mag_pred = stft_mag_pred[..., :min_len]
 
-    loss = torch.mean(torch.abs(torch.log(stft_mag_true + eps) - torch.log(stft_mag_pred + eps)))
+# def log_stft_magnitude_loss(
-    return loss
+#     y_true: torch.Tensor, y_pred: torch.Tensor, eps: float = 1e-7
+# ) -> torch.Tensor:
+#     """Calculates L1 loss on the log STFT magnitude."""
+#     stft_mag_true = stft_transform(y_true)
+#     stft_mag_pred = stft_transform(y_pred)
+#     return F.l1_loss(torch.log(stft_mag_pred + eps), torch.log(stft_mag_true + eps))
 
-def spectral_convergence_loss(stft_transform: T.Spectrogram, y_true: torch.Tensor, y_pred: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
-    stft_mag_true = stft_transform(y_true)
-    stft_mag_pred = stft_transform(y_pred)
 
-    min_len = min(stft_mag_true.shape[-1], stft_mag_pred.shape[-1])
+stft_loss_fn = MultiResolutionSTFTLoss(
-    stft_mag_true = stft_mag_true[..., :min_len]
+    fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240]
-    stft_mag_pred = stft_mag_pred[..., :min_len]
+)
 
-    norm_true = torch.linalg.norm(stft_mag_true, ord='fro', dim=(-2, -1))
-    norm_diff = torch.linalg.norm(stft_mag_true - stft_mag_pred, ord='fro', dim=(-2, -1))
 
-    loss = torch.mean(norm_diff / (norm_true + eps))
+def discriminator_train(
-    return loss
+    high_quality,
-
+    low_quality,
-def discriminator_train(high_quality, low_quality, real_labels, fake_labels, discriminator, generator, criterion, optimizer):
+    real_labels,
-    optimizer.zero_grad()
+    fake_labels,
-
+    discriminator,
-    # Forward pass for real samples
+    generator,
+    criterion,
+):
     discriminator_decision_from_real = discriminator(high_quality)
     d_loss_real = criterion(discriminator_decision_from_real, real_labels)
 
     with torch.no_grad():
         generator_output = generator(low_quality)
     discriminator_decision_from_fake = discriminator(generator_output)
-    d_loss_fake = criterion(discriminator_decision_from_fake, fake_labels.expand_as(discriminator_decision_from_fake))
+    d_loss_fake = criterion(
+        discriminator_decision_from_fake,
+        fake_labels.expand_as(discriminator_decision_from_fake),
+    )
 
     d_loss = (d_loss_real + d_loss_fake) / 2.0
 
-    d_loss.backward()
-    # Optional: Gradient Clipping (can be helpful)
-    # nn.utils.clip_grad_norm_(discriminator.parameters(), max_norm=1.0)  # Gradient Clipping
-    optimizer.step()
-
     return d_loss
 
+
 def generator_train(
     low_quality,
     high_quality,
@@ -91,52 +90,65 @@ def generator_train(
     generator,
     discriminator,
     adv_criterion,
-    g_optimizer,
-    device,
-    mel_transform: T.MelSpectrogram,
-    stft_transform: T.Spectrogram,
-    mfcc_transform: T.MFCC,
     lambda_adv: float = 1.0,
-    lambda_mel_l1: float = 10.0,
+    lambda_feat: float = 10.0,
-    lambda_log_stft: float = 1.0,
+    lambda_stft: float = 2.5,
-    lambda_mfcc: float = 1.0
 ):
-    g_optimizer.zero_grad()
-
     generator_output = generator(low_quality)
 
     discriminator_decision = discriminator(generator_output)
-    adversarial_loss = adv_criterion(discriminator_decision, real_labels.expand_as(discriminator_decision))
+    # adversarial_loss = adv_criterion(
+    #     discriminator_decision, real_labels.expand_as(discriminator_decision)
+    # )
+    adversarial_loss = adv_criterion(discriminator_decision, real_labels)
 
-    mel_l1 = 0.0
+    combined_loss = lambda_adv * adversarial_loss
-    log_stft_l1 = 0.0
-    mfcc_l = 0.0
 
-    # Calculate Mel L1 Loss if weight is positive
+    stft_losses = stft_loss_fn(high_quality, generator_output)
-    if lambda_mel_l1 > 0:
+    stft_loss = stft_losses["total"]
-        mel_l1 = mel_spectrogram_l1_loss(mel_transform, high_quality, generator_output)
 
-    # Calculate Log STFT L1 Loss if weight is positive
+    combined_loss = (lambda_adv * adversarial_loss) + (lambda_stft * stft_loss)
-    if lambda_log_stft > 0:
-        log_stft_l1 = log_stft_magnitude_loss(stft_transform, high_quality, generator_output)
 
-    # Calculate MFCC Loss if weight is positive
+    return generator_output, combined_loss, adversarial_loss
-    if lambda_mfcc > 0:
-        mfcc_l = gpu_mfcc_loss(mfcc_transform, high_quality, generator_output)
 
-    mel_l1_tensor = torch.tensor(mel_l1, device=device) if isinstance(mel_l1, float) else mel_l1
-    log_stft_l1_tensor = torch.tensor(log_stft_l1, device=device) if isinstance(log_stft_l1, float) else log_stft_l1
-    mfcc_l_tensor = torch.tensor(mfcc_l, device=device) if isinstance(mfcc_l, float) else mfcc_l
 
-    combined_loss = (lambda_adv * adversarial_loss) + \
+# def generator_train(
-                    (lambda_mel_l1 * mel_l1_tensor) + \
+#     low_quality,
-                    (lambda_log_stft * log_stft_l1_tensor) + \
+#     high_quality,
-                    (lambda_mfcc * mfcc_l_tensor)
+#     real_labels,
+#     generator,
+#     discriminator,
+#     adv_criterion,
+#     lambda_adv: float = 1.0,
+#     lambda_mel_l1: float = 10.0,
+#     lambda_log_stft: float = 1.0,
 
-    combined_loss.backward()
+# ):
-    # Optional: Gradient Clipping
+#     generator_output = generator(low_quality)
-    # nn.utils.clip_grad_norm_(generator.parameters(), max_norm=1.0)
-    g_optimizer.step()
 
-    # 6. Return values for logging
+#     discriminator_decision = discriminator(generator_output)
-    return generator_output, combined_loss, adversarial_loss, mel_l1_tensor, log_stft_l1_tensor, mfcc_l_tensor
+#     adversarial_loss = adv_criterion(
+#         discriminator_decision, real_labels.expand_as(discriminator_decision)
+#     )
+
+#     combined_loss = lambda_adv * adversarial_loss
+
+#     if lambda_mel_l1 > 0:
+#         mel_l1_loss = mel_spectrogram_loss(high_quality, generator_output, "l1")
+#         combined_loss += lambda_mel_l1 * mel_l1_loss
+#     else:
+#         mel_l1_loss = torch.tensor(0.0, device=low_quality.device)  # For logging
+
+#     if lambda_log_stft > 0:
+#         log_stft_loss = log_stft_magnitude_loss(high_quality, generator_output)
+#         combined_loss += lambda_log_stft * log_stft_loss
+#     else:
+#         log_stft_loss = torch.tensor(0.0, device=low_quality.device)
+
+#     if lambda_mfcc > 0:
+#         mfcc_loss_val = mfcc_loss(high_quality, generator_output)
+#         combined_loss += lambda_mfcc * mfcc_loss_val
+#     else:
+#         mfcc_loss_val = torch.tensor(0.0, device=low_quality.device)
+
+#     return generator_output, combined_loss, adversarial_loss

utils/MultiResolutionSTFTLoss.py

@@ -0,0 +1,62 @@
+from typing import Dict, List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchaudio.transforms as T
+
+
+class MultiResolutionSTFTLoss(nn.Module):
+    """
+    Computes a loss based on multiple STFT resolutions, including both
+    spectral convergence and log STFT magnitude components.
+    """
+
+    def __init__(
+        self,
+        fft_sizes: List[int] = [1024, 2048, 512],
+        hop_sizes: List[int] = [120, 240, 50],
+        win_lengths: List[int] = [600, 1200, 240],
+        eps: float = 1e-7,
+    ):
+        super().__init__()
+        self.stft_transforms = nn.ModuleList(
+            [
+                T.Spectrogram(
+                    n_fft=n_fft, win_length=win_len, hop_length=hop_len, power=None
+                )
+                for n_fft, hop_len, win_len in zip(fft_sizes, hop_sizes, win_lengths)
+            ]
+        )
+        self.eps = eps
+
+    def forward(
+        self, y_true: torch.Tensor, y_pred: torch.Tensor
+    ) -> Dict[str, torch.Tensor]:
+        sc_loss = 0.0  # Spectral Convergence Loss
+        mag_loss = 0.0  # Log STFT Magnitude Loss
+
+        for stft in self.stft_transforms:
+            stft.to(y_pred.device)  # Ensure transform is on the correct device
+
+            # Get complex STFTs
+            stft_true = stft(y_true)
+            stft_pred = stft(y_pred)
+
+            # Get magnitudes
+            stft_mag_true = torch.abs(stft_true)
+            stft_mag_pred = torch.abs(stft_pred)
+
+            # --- Spectral Convergence Loss ---
+            # || |S_true| - |S_pred| ||_F  /  || |S_true| ||_F
+            norm_true = torch.linalg.norm(stft_mag_true, dim=(-2, -1))
+            norm_diff = torch.linalg.norm(stft_mag_true - stft_mag_pred, dim=(-2, -1))
+            sc_loss += torch.mean(norm_diff / (norm_true + self.eps))
+
+            # --- Log STFT Magnitude Loss ---
+            mag_loss += F.l1_loss(
+                torch.log(stft_mag_pred + self.eps), torch.log(stft_mag_true + self.eps)
+            )
+
+        total_loss = sc_loss + mag_loss
+        return {"total": total_loss, "sc": sc_loss, "mag": mag_loss}