⚗️ | Experimenting...

AudioUtils.py

@@ -1,97 +1,18 @@
 import torch
 import torch.nn.functional as F
 
-
+def stereo_tensor_to_mono(waveform):
-def stereo_tensor_to_mono(waveform: torch.Tensor) -> torch.Tensor:
-    """
-    Convert stereo (C, N) to mono (1, N). Ensures a channel dimension.
-    """
-    if waveform.dim() == 1:
-        waveform = waveform.unsqueeze(0)  # (N,) -> (1, N)
-
     if waveform.shape[0] > 1:
-        mono_waveform = torch.mean(waveform, dim=0, keepdim=True)  # (1, N)
+        # Average across channels
+        mono_waveform = torch.mean(waveform, dim=0, keepdim=True)
     else:
+        # Already mono
         mono_waveform = waveform
     return mono_waveform
 
+def stretch_tensor(tensor, target_length):
+    scale_factor = target_length / tensor.size(1)
 
-def stretch_tensor(tensor: torch.Tensor, target_length: int) -> torch.Tensor:
+    tensor = F.interpolate(tensor, scale_factor=scale_factor, mode='linear', align_corners=False)
-    """
-    Stretch audio along time dimension to target_length.
-    Input assumed (1, N). Returns (1, target_length).
-    """
-    if tensor.dim() == 1:
-        tensor = tensor.unsqueeze(0)  # ensure (1, N)
 
-    tensor = tensor.unsqueeze(0)  # (1, 1, N) for interpolate
+    return tensor
-    stretched = F.interpolate(
-        tensor, size=target_length, mode="linear", align_corners=False
-    )
-    return stretched.squeeze(0)  # back to (1, target_length)
-
-
-def pad_tensor(audio_tensor: torch.Tensor, target_length: int = 128) -> torch.Tensor:
-    """
-    Pad to fixed length. Input assumed (1, N). Returns (1, target_length).
-    """
-    if audio_tensor.dim() == 1:
-        audio_tensor = audio_tensor.unsqueeze(0)
-
-    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[..., :target_length]  # crop if too long
-
-    return padded_audio_tensor
-
-
-def split_audio(
-    audio_tensor: torch.Tensor, chunk_size: int = 128
-) -> list[torch.Tensor]:
-    """
-    Split into chunks of (1, chunk_size).
-    """
-    if not isinstance(chunk_size, int) or chunk_size <= 0:
-        raise ValueError("chunk_size must be a positive integer.")
-
-    if audio_tensor.dim() == 1:
-        audio_tensor = audio_tensor.unsqueeze(0)
-
-    num_samples = audio_tensor.shape[-1]
-    if num_samples == 0:
-        return []
-
-    chunks = list(torch.split(audio_tensor, chunk_size, dim=-1))
-    return chunks
-
-
-def reconstruct_audio(chunks: list[torch.Tensor]) -> torch.Tensor:
-    """
-    Reconstruct audio from chunks. Returns (1, N).
-    """
-    if not chunks:
-        return torch.empty(1, 0)
-
-    chunks = [c if c.dim() == 2 else c.unsqueeze(0) for c in chunks]
-    try:
-        reconstructed_tensor = torch.cat(chunks, dim=-1)
-    except RuntimeError as e:
-        raise RuntimeError(
-            f"Failed to concatenate audio chunks. Ensure chunks have compatible shapes "
-            f"for concatenation along dim -1. Original error: {e}"
-        )
-
-    return reconstructed_tensor
-
-
-def normalize(audio_tensor: torch.Tensor, eps: float = 1e-8) -> torch.Tensor:
-    max_val = torch.max(torch.abs(audio_tensor))
-    if max_val < eps:
-        return audio_tensor  # silence, skip normalization
-    return audio_tensor / max_val

README.md

@@ -18,7 +18,6 @@ SISU (Super Ingenious Sound Upscaler) is a project that uses GANs (Generative Ad
 1. **Set Up**:
    - Make sure you have Python installed (version 3.8 or higher).
    - Install needed packages: `pip install -r requirements.txt`
-   - Install current version of PyTorch (CUDA/ROCm/What ever your device supports)
 
 2. **Prepare Audio Data**:
    - Put your audio files in the `dataset/good` folder.

app.py

@@ -1,97 +0,0 @@
-import argparse
-
-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("--model", type=str, help="Model to use for upscaling")
-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().to(device)
-
-generator = torch.compile(generator)
-
-models_dir = args.model
-clip_length = args.clip_length
-input_audio = args.input
-output_audio = args.output
-
-if models_dir:
-    ckpt = torch.load(models_dir, map_location=device)
-    generator.load_state_dict(ckpt["G"])
-else:
-    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!)"
-    )
-
-
-def start():
-    # To Mono!
-    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)
-    audio = AudioUtils.normalize(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 = []
-
-    for clip in tqdm.tqdm(splitted_audio_on_device, desc="Processing..."):
-        processed_audio.append(generator(clip))
-
-    reconstructed_audio = AudioUtils.reconstruct_audio(processed_audio)
-    print(f"Saving {output_audio}!")
-    torchaudio.save_with_torchcodec(
-        uri=output_audio,
-        src=reconstructed_audio,
-        sample_rate=args.sample_rate,
-        channels_first=True,
-        compression=args.bitrate,
-    )
-
-
-start()

data.py

@@ -1,79 +1,52 @@
+from torch.utils.data import Dataset
+import torch.nn.functional as F
+import torch
+import torchaudio
 import os
 import random
-
+from AudioUtils import stereo_tensor_to_mono, stretch_tensor
-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, clip_length: int = 8000, normalize: bool = True):
+    def __init__(self, input_dir):
-        self.clip_length = clip_length
+        self.input_files = [
-        self.normalize = normalize
+            os.path.join(root, f)
-
+            for root, _, files in os.walk(input_dir)
-        input_files = [
+            for f in files if f.endswith('.wav')
-            os.path.join(input_dir, f)
-            for f in os.listdir(input_dir)
-            if os.path.isfile(os.path.join(input_dir, f))
-            and f.lower().endswith((".wav", ".mp3", ".flac"))
         ]
 
-        data = []
-        for audio_clip in tqdm.tqdm(
-            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.float()  # ensure float32
-            original_sample_rate = decoded_samples.sample_rate
-
-            audio = AudioUtils.stereo_tensor_to_mono(audio)
-            if normalize:
-                audio = AudioUtils.normalize(audio)
-
-            mangled_sample_rate = random.choice(self.audio_sample_rates)
-            resample_transform_low = torchaudio.transforms.Resample(
-                original_sample_rate, mangled_sample_rate
-            )
-            resample_transform_high = torchaudio.transforms.Resample(
-                mangled_sample_rate, original_sample_rate
-            )
-
-            low_audio = resample_transform_high(resample_transform_low(audio))
-
-            splitted_high_quality_audio = AudioUtils.split_audio(audio, clip_length)
-            splitted_low_quality_audio = AudioUtils.split_audio(low_audio, clip_length)
-
-            if not splitted_high_quality_audio or not splitted_low_quality_audio:
-                continue  # skip empty or invalid clips
-
-            splitted_high_quality_audio[-1] = AudioUtils.pad_tensor(
-                splitted_high_quality_audio[-1], clip_length
-            )
-            splitted_low_quality_audio[-1] = AudioUtils.pad_tensor(
-                splitted_low_quality_audio[-1], clip_length
-            )
-
-            for high_quality_data, low_quality_data in zip(
-                splitted_high_quality_audio, splitted_low_quality_audio
-            ):
-                data.append(
-                    (
-                        (high_quality_data, low_quality_data),
-                        (original_sample_rate, mangled_sample_rate),
-                    )
-                )
-
-        self.audio_data = data
-
     def __len__(self):
-        return len(self.audio_data)
+        return len(self.input_files)
 
     def __getitem__(self, idx):
-        return self.audio_data[idx]
+        # Load high-quality audio
+        high_quality_path = self.input_files[idx]
+        high_quality_audio, original_sample_rate = torchaudio.load(high_quality_path)
+        high_quality_audio = stereo_tensor_to_mono(high_quality_audio)
+
+        # Generate low-quality audio with random downsampling
+        mangled_sample_rate = random.choice(self.audio_sample_rates)
+        resample_low = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
+        low_quality_audio = resample_low(high_quality_audio)
+
+        resample_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
+        low_quality_audio = resample_high(low_quality_audio)
+
+        # Pad or truncate to match a fixed length
+        target_length = 44100  # Adjust this based on your data
+        high_quality_audio = self.pad_or_truncate(high_quality_audio, target_length)
+        low_quality_audio = self.pad_or_truncate(low_quality_audio, target_length)
+
+        return (high_quality_audio, original_sample_rate), (low_quality_audio, mangled_sample_rate)
+
+    def pad_or_truncate(self, tensor, target_length):
+        current_length = tensor.size(1)
+        if current_length < target_length:
+            # Pad with zeros
+            padding = target_length - current_length
+            tensor = F.pad(tensor, (0, padding))
+        else:
+            # Truncate to target length
+            tensor = tensor[:, :target_length]
+        return tensor

discriminator.py

@@ -1,75 +1,38 @@
+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):
-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(
+    return nn.Sequential(
-        in_channels,
+        utils.spectral_norm(
-        out_channels,
+            nn.Conv1d(in_channels, out_channels,
                 kernel_size=kernel_size,
                 stride=stride,
                 dilation=dilation,
-        padding=padding,
+                padding=padding
             )
-
+        ),
-    if spectral_norm:
+        nn.BatchNorm1d(out_channels),
-        conv_layer = utils.spectral_norm(conv_layer)
+        nn.LeakyReLU(0.2, inplace=True)
-
-    layers = [conv_layer]
-    layers.append(nn.LeakyReLU(0.2, inplace=True))
-
-    if use_instance_norm:
-        layers.append(nn.InstanceNorm1d(out_channels))
-
-    return nn.Sequential(*layers)
-
-
-class AttentionBlock(nn.Module):
-    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(),
     )
 
-    def forward(self, x):
-        attention_weights = self.attention(x)
-        return x * attention_weights
-
-
 class SISUDiscriminator(nn.Module):
-    def __init__(self, layers=32):
+    def __init__(self):
         super(SISUDiscriminator, self).__init__()
+        layers = 4
         self.model = nn.Sequential(
-            discriminator_block(1, layers, kernel_size=7, stride=1),
+            discriminator_block(1, layers, kernel_size=7, stride=2, dilation=1),
-            discriminator_block(layers, layers * 2, kernel_size=5, stride=2),
+            discriminator_block(layers, layers * 2, kernel_size=5, stride=2, dilation=1),
-            discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=2),
+            discriminator_block(layers * 2, layers * 4, kernel_size=3, dilation=4),
-            AttentionBlock(layers * 4),
+            discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=8),
-            discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=4),
+            discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=16),
-            discriminator_block(layers * 8, layers * 2, kernel_size=5, stride=2),
+            discriminator_block(layers * 2, layers, kernel_size=5, dilation=2),
-            discriminator_block(
+            discriminator_block(layers, 1, kernel_size=3, stride=1)
-                layers * 2,
-                1,
-                spectral_norm=False,
-                use_instance_norm=False,
-            ),
         )
-
         self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
 
     def forward(self, x):
         x = self.model(x)
         x = self.global_avg_pool(x)
-        x = x.view(x.size(0), -1)
+        return x.view(-1, 1)
-        return x

generator.py

@@ -1,81 +1,41 @@
-import torch
 import torch.nn as nn
 
-
+def conv_residual_block(in_channels, out_channels, kernel_size=3, dilation=1):
-def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
+    padding = (kernel_size // 2) * dilation
     return nn.Sequential(
-        nn.Conv1d(
+        nn.Conv1d(in_channels, out_channels, kernel_size, dilation=dilation, padding=padding),
-            in_channels,
+        nn.BatchNorm1d(out_channels),
-            out_channels,
-            kernel_size=kernel_size,
-            dilation=dilation,
-            padding=(kernel_size // 2) * dilation,
-        ),
-        nn.InstanceNorm1d(out_channels),
         nn.PReLU(),
+        nn.Conv1d(out_channels, out_channels, kernel_size, dilation=dilation, padding=padding),
+        nn.BatchNorm1d(out_channels)
     )
 
-
-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(),
-        )
-
-    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__()
-
-        self.conv_layers = nn.Sequential(
-            *[conv_block(channels, channels) for _ in range(num_convs)]
-        )
-
-        self.attention = AttentionBlock(channels)
-
-    def forward(self, x):
-        residual = x
-        x = self.conv_layers(x)
-        x = self.attention(x)
-        return x + residual
-
-
 class SISUGenerator(nn.Module):
-    def __init__(self, channels=16, num_rirb=4, alpha=1):
+    def __init__(self):
         super(SISUGenerator, self).__init__()
-        self.alpha = alpha
+        layers = 4
-
         self.conv1 = nn.Sequential(
-            nn.Conv1d(1, channels, kernel_size=7, padding=3),
+            nn.Conv1d(1, layers, kernel_size=7, padding=3),
-            nn.InstanceNorm1d(channels),
+            nn.BatchNorm1d(layers),
-            nn.PReLU(),
+            nn.PReLU()
         )
 
-        self.rir_blocks = nn.Sequential(
+        self.conv_blocks = nn.Sequential(
-            *[ResidualInResidualBlock(channels) for _ in range(num_rirb)]
+            conv_residual_block(layers, layers, kernel_size=3, dilation=1),
+            conv_residual_block(layers, layers * 2, kernel_size=5, dilation=2),
+            conv_residual_block(layers * 2, layers * 4, kernel_size=3, dilation=16),
+            conv_residual_block(layers * 4, layers * 2, kernel_size=5, dilation=8),
+            conv_residual_block(layers * 2, layers, kernel_size=5, dilation=2),
+            conv_residual_block(layers, layers, kernel_size=3, dilation=1)
         )
 
         self.final_layer = nn.Sequential(
-            nn.Conv1d(channels, 1, kernel_size=3, padding=1), nn.Tanh()
+            nn.Conv1d(layers, 1, kernel_size=3, padding=1)
         )
 
     def forward(self, x):
-        residual_input = x
+        residual = x
         x = self.conv1(x)
-        x_rirb_out = self.rir_blocks(x)
+        x = self.conv_blocks(x) + x  # Adding residual connection after blocks
-        learned_residual = self.final_layer(x_rirb_out)
+        x = self.final_layer(x)
-        output = residual_input + self.alpha * learned_residual
+        return x + residual
-
-        return torch.tanh(output)

requirements.txt

@@ -0,0 +1,14 @@
+filelock==3.16.1
+fsspec==2024.10.0
+Jinja2==3.1.4
+MarkupSafe==2.1.5
+mpmath==1.3.0
+networkx==3.4.2
+numpy==2.2.1
+pytorch-triton-rocm==3.2.0+git0d4682f0
+setuptools==70.2.0
+sympy==1.13.1
+torch==2.6.0.dev20241222+rocm6.2.4
+torchaudio==2.6.0.dev20241222+rocm6.2.4
+tqdm==4.67.1
+typing_extensions==4.12.2

training.py

@@ -1,245 +1,164 @@
-import argparse
-import os
-
 import torch
 import torch.nn as nn
 import torch.optim as optim
+
+import torch.nn.functional as F
+import torchaudio
 import tqdm
-from accelerate import Accelerator
-from torch.utils.data import DataLoader, DistributedSampler
 
+import argparse
+
+import math
+
+from torch.utils.data import random_split
+from torch.utils.data import DataLoader
+
+import AudioUtils
 from data import AudioDataset
-from discriminator import SISUDiscriminator
 from generator import SISUGenerator
-from utils.TrainingTools import discriminator_train, generator_train
+from discriminator import SISUDiscriminator
+
+def perceptual_loss(y_true, y_pred):
+    return torch.mean((y_true - y_pred) ** 2)
+
+def discriminator_train(high_quality, low_quality, real_labels, fake_labels):
+    optimizer_d.zero_grad()
+
+    # Forward pass for real samples
+    discriminator_decision_from_real = discriminator(high_quality[0])
+    d_loss_real = criterion_d(discriminator_decision_from_real, real_labels)
+
+    # Forward pass for fake samples (from generator output)
+    generator_output = generator(low_quality[0])
+    discriminator_decision_from_fake = discriminator(generator_output.detach())
+    d_loss_fake = criterion_d(discriminator_decision_from_fake, fake_labels)
+
+    # Combine real and fake losses
+    d_loss = (d_loss_real + d_loss_fake) / 2.0
+
+    # Backward pass and optimization
+    d_loss.backward()
+    nn.utils.clip_grad_norm_(discriminator.parameters(), max_norm=1.0)  # Gradient Clipping
+    optimizer_d.step()
+
+    return d_loss
+
+def generator_train(low_quality, real_labels):
+    optimizer_g.zero_grad()
+
+    # Forward pass for fake samples (from generator output)
+    generator_output = generator(low_quality[0])
+    discriminator_decision = discriminator(generator_output)
+    g_loss = criterion_g(discriminator_decision, real_labels)
+
+    g_loss.backward()
+    optimizer_g.step()
+    return generator_output
+
+def first(objects):
+    if len(objects) >= 1:
+        return objects[0]
+    return objects
+
+# 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")
 
-# ---------------------------
-# Argument parsing
-# ---------------------------
-parser = argparse.ArgumentParser(description="Training script (safer defaults)")
-parser.add_argument("--resume", action="store_true", help="Resume training")
-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()
 
-# ---------------------------
+# Check for CUDA availability
-# Init accelerator
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-# ---------------------------
+print(f"Using device: {device}")
 
-accelerator = Accelerator(mixed_precision="bf16")
+# Initialize dataset and dataloader
+dataset_dir = './dataset/good'
+dataset = AudioDataset(dataset_dir)
 
-# ---------------------------
+# ========= SINGLE =========
-# Models
+
-# ---------------------------
+train_data_loader = DataLoader(dataset, batch_size=16, shuffle=True)
+
+# Initialize models and move them to device
 generator = SISUGenerator()
 discriminator = SISUDiscriminator()
 
-accelerator.print("🔨 | Compiling models...")
+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 = torch.compile(generator)
+generator = generator.to(device)
-discriminator = torch.compile(discriminator)
+discriminator = discriminator.to(device)
 
-accelerator.print("✅ | Compiling done!")
+# Loss
+criterion_g = nn.MSELoss()
+criterion_d = nn.BCELoss()
 
-# ---------------------------
+# Optimizers
-# Dataset / DataLoader
+optimizer_g = optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
-# ---------------------------
+optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0001, betas=(0.5, 0.999))
-accelerator.print("📊 | Fetching dataset...")
-dataset = AudioDataset("./dataset")
 
-sampler = DistributedSampler(dataset) if accelerator.num_processes > 1 else None
+# Scheduler
-pin_memory = torch.cuda.is_available() and not args.no_pin_memory
+scheduler_g = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_g, mode='min', factor=0.5, patience=5)
+scheduler_d = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_d, mode='min', factor=0.5, patience=5)
 
-train_loader = DataLoader(
+def start_training():
-    dataset,
+    generator_epochs = 5000
-    sampler=sampler,
+    for generator_epoch in range(generator_epochs):
-    batch_size=args.batch_size,
+        low_quality_audio = (torch.empty((1)), 1)
-    shuffle=(sampler is None),
+        high_quality_audio = (torch.empty((1)), 1)
-    num_workers=args.num_workers,
+        ai_enhanced_audio = (torch.empty((1)), 1)
-    pin_memory=pin_memory,
-    persistent_workers=pin_memory,
-)
 
-if not train_loader or not train_loader.batch_size or train_loader.batch_size == 0:
+        times_correct = 0
-    accelerator.print("🪹 | There is no data to train with! Exiting...")
-    exit()
 
-loader_batch_size = train_loader.batch_size
+        # ========= TRAINING =========
+        for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Epoch {generator_epoch+1}/{generator_epochs}"):
+        # for high_quality_clip, low_quality_clip in train_data_loader:
+            high_quality_sample = (high_quality_clip[0].to(device), high_quality_clip[1])
+            low_quality_sample = (low_quality_clip[0].to(device), low_quality_clip[1])
 
-accelerator.print("✅ | Dataset fetched!")
+            # ========= LABELS =========
+            batch_size = high_quality_clip[0].size(0)
+            real_labels = torch.ones(batch_size, 1).to(device)
+            fake_labels = torch.zeros(batch_size, 1).to(device)
 
-# ---------------------------
+            # ========= DISCRIMINATOR =========
-# Losses / Optimizers / Scalers
-# ---------------------------
-
-optimizer_g = optim.AdamW(
-    generator.parameters(), lr=0.0003, betas=(0.5, 0.999), weight_decay=0.0001
-)
-optimizer_d = optim.AdamW(
-    discriminator.parameters(), lr=0.0003, betas=(0.5, 0.999), weight_decay=0.0001
-)
-
-scheduler_g = torch.optim.lr_scheduler.ReduceLROnPlateau(
-    optimizer_g, mode="min", factor=0.5, patience=5
-)
-scheduler_d = torch.optim.lr_scheduler.ReduceLROnPlateau(
-    optimizer_d, mode="min", factor=0.5, patience=5
-)
-
-criterion_g = nn.BCEWithLogitsLoss()
-criterion_d = nn.MSELoss()
-
-# ---------------------------
-# Prepare accelerator
-# ---------------------------
-
-generator, discriminator, optimizer_g, optimizer_d, train_loader = accelerator.prepare(
-    generator, discriminator, optimizer_g, optimizer_d, train_loader
-)
-
-# ---------------------------
-# Checkpoint helpers
-# ---------------------------
-models_dir = "./models"
-os.makedirs(models_dir, exist_ok=True)
-
-
-def save_ckpt(path, epoch):
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        accelerator.save(
-            {
-                "epoch": epoch,
-                "G": accelerator.unwrap_model(generator).state_dict(),
-                "D": accelerator.unwrap_model(discriminator).state_dict(),
-                "optG": optimizer_g.state_dict(),
-                "optD": optimizer_d.state_dict(),
-                "schedG": scheduler_g.state_dict(),
-                "schedD": scheduler_d.state_dict(),
-            },
-            path,
-        )
-
-
-start_epoch = 0
-if args.resume:
-    ckpt_path = os.path.join(models_dir, "last.pt")
-    ckpt = torch.load(ckpt_path)
-
-    accelerator.unwrap_model(generator).load_state_dict(ckpt["G"])
-    accelerator.unwrap_model(discriminator).load_state_dict(ckpt["D"])
-    optimizer_g.load_state_dict(ckpt["optG"])
-    optimizer_d.load_state_dict(ckpt["optD"])
-    scheduler_g.load_state_dict(ckpt["schedG"])
-    scheduler_d.load_state_dict(ckpt["schedD"])
-
-    start_epoch = ckpt.get("epoch", 1)
-    accelerator.print(f"🔁 | Resumed from epoch {start_epoch}!")
-
-real_buf = torch.full(
-    (loader_batch_size, 1), 1, device=accelerator.device, dtype=torch.float32
-)
-fake_buf = torch.zeros(
-    (loader_batch_size, 1), device=accelerator.device, dtype=torch.float32
-)
-
-accelerator.print("🏋️ | Started training...")
-
-try:
-    for epoch in range(start_epoch, args.epochs):
-        generator.train()
             discriminator.train()
+            discriminator_train(high_quality_sample, low_quality_sample, real_labels, fake_labels)
 
-        running_d, running_g, steps = 0.0, 0.0, 0
+            # ========= GENERATOR =========
+            generator.train()
+            generator_output = generator_train(low_quality_sample, real_labels)
 
-        for i, (
+            # ========= SAVE LATEST AUDIO =========
-            (high_quality, low_quality),
+            high_quality_audio = (first(high_quality_clip[0]), high_quality_clip[1][0])
-            (high_sample_rate, low_sample_rate),
+            low_quality_audio = (first(low_quality_clip[0]), low_quality_clip[1][0])
-        ) in enumerate(tqdm.tqdm(train_loader, desc=f"Epoch {epoch}")):
+            ai_enhanced_audio = (first(generator_output[0]), high_quality_clip[1][0])
-            batch_size = high_quality.size(0)
+            print(high_quality_audio)
 
-            real_labels = real_buf[:batch_size].to(accelerator.device)
+            print(f"Saved epoch {generator_epoch}!")
-            fake_labels = fake_buf[:batch_size].to(accelerator.device)
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-crap.wav", low_quality_audio[0][0].cpu(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-ai.wav", ai_enhanced_audio[0][0].cpu(), ai_enhanced_audio[1])
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-orig.wav", high_quality_audio[0][0].cpu(), high_quality_audio[1])
 
-            # --- Discriminator ---
+        #metric = snr(high_quality_audio[0].to(device), ai_enhanced_audio[0])
-            optimizer_d.zero_grad(set_to_none=True)
+        #print(f"Generator metric {metric}!")
-            with accelerator.autocast():
+        #scheduler_g.step(metric)
-                d_loss = discriminator_train(
-                    high_quality,
-                    low_quality,
-                    real_labels,
-                    fake_labels,
-                    discriminator,
-                    generator,
-                    criterion_d,
-                )
 
-            accelerator.backward(d_loss)
+        if generator_epoch % 10 == 0:
-            torch.nn.utils.clip_grad_norm_(discriminator.parameters(), 1)
+            print(f"Saved epoch {generator_epoch}!")
-            optimizer_d.step()
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-crap.wav", low_quality_audio[0][0].cpu(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-ai.wav", ai_enhanced_audio[0][0].cpu(), ai_enhanced_audio[1])
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-orig.wav", high_quality_audio[0][0].cpu(), high_quality_audio[1])
 
-            # --- Generator ---
+        torch.save(discriminator.state_dict(), f"models/current-epoch-discriminator.pt")
-            optimizer_g.zero_grad(set_to_none=True)
+        torch.save(generator.state_dict(), f"models/current-epoch-generator.pt")
-            with accelerator.autocast():
-                g_total, g_adv = generator_train(
-                    low_quality,
-                    high_quality,
-                    real_labels,
-                    generator,
-                    discriminator,
-                    criterion_d,
-                )
 
-            accelerator.backward(g_total)
+    torch.save(discriminator.state_dict(), "models/epoch-5000-discriminator.pt")
-            torch.nn.utils.clip_grad_norm_(generator.parameters(), 1)
+    torch.save(generator.state_dict(), "models/epoch-5000-generator.pt")
-            optimizer_g.step()
+    print("Training complete!")
 
-            d_val = accelerator.gather(d_loss.detach()).mean()
+start_training()
-            g_val = accelerator.gather(g_total.detach()).mean()
-
-            if torch.isfinite(d_val):
-                running_d += d_val.item()
-            else:
-                accelerator.print(
-                    f"🫥 | NaN in discriminator loss at step {i}, skipping update."
-                )
-
-            if torch.isfinite(g_val):
-                running_g += g_val.item()
-            else:
-                accelerator.print(
-                    f"🫥 | NaN in generator loss at step {i}, skipping update."
-                )
-
-            steps += 1
-
-        # epoch averages & schedulers
-        if steps == 0:
-            accelerator.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)
-        accelerator.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)
-        accelerator.print(f"💾 | Saved crash checkpoint for epoch {epoch}")
-    except Exception as e:
-        accelerator.print("😬 | Failed saving crash checkpoint:", e)
-    raise
-
-accelerator.print("🏁 | Training finished.")

utils/MultiResolutionSTFTLoss.py

@@ -1,87 +0,0 @@
-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):
-    """
-    Multi-resolution STFT loss.
-    Combines spectral convergence loss and log-magnitude loss
-    across multiple STFT resolutions.
-    """
-
-    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.eps = eps
-        self.n_resolutions = len(fft_sizes)
-
-        self.stft_transforms = nn.ModuleList()
-        for n_fft, hop_len, win_len in zip(fft_sizes, hop_sizes, win_lengths):
-            window = torch.hann_window(win_len)
-            stft = T.Spectrogram(
-                n_fft=n_fft,
-                hop_length=hop_len,
-                win_length=win_len,
-                window_fn=lambda _: window,
-                power=None,  # Keep complex output
-                center=True,
-                pad_mode="reflect",
-                normalized=False,
-            )
-            self.stft_transforms.append(stft)
-
-    def forward(
-        self, y_true: torch.Tensor, y_pred: torch.Tensor
-    ) -> Dict[str, torch.Tensor]:
-        """
-        Args:
-            y_true: (B, T) or (B, 1, T) waveform
-            y_pred: (B, T) or (B, 1, T) waveform
-        """
-        # Ensure correct shape (B, T)
-        if y_true.dim() == 3 and y_true.size(1) == 1:
-            y_true = y_true.squeeze(1)
-        if y_pred.dim() == 3 and y_pred.size(1) == 1:
-            y_pred = y_pred.squeeze(1)
-
-        sc_loss = 0.0
-        mag_loss = 0.0
-
-        for stft in self.stft_transforms:
-            stft = stft.to(y_pred.device)
-
-            # Complex STFTs: (B, F, T, 2)
-            stft_true = stft(y_true)
-            stft_pred = stft(y_pred)
-
-            # Magnitudes
-            stft_mag_true = torch.abs(stft_true)
-            stft_mag_pred = torch.abs(stft_pred)
-
-            # --- Spectral Convergence Loss ---
-            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),
-            )
-
-        # Average across resolutions
-        sc_loss /= self.n_resolutions
-        mag_loss /= self.n_resolutions
-        total_loss = sc_loss + mag_loss
-
-        return {"total": total_loss, "sc": sc_loss, "mag": mag_loss}

utils/TrainingTools.py

@@ -1,60 +0,0 @@
-import torch
-
-# In case if needed again...
-# from utils.MultiResolutionSTFTLoss import MultiResolutionSTFTLoss
-#
-# stft_loss_fn = MultiResolutionSTFTLoss(
-#     fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240]
-# )
-
-
-def signal_mae(input_one: torch.Tensor, input_two: torch.Tensor) -> torch.Tensor:
-    absolute_difference = torch.abs(input_one - input_two)
-    return torch.mean(absolute_difference)
-
-
-def discriminator_train(
-    high_quality,
-    low_quality,
-    high_labels,
-    low_labels,
-    discriminator,
-    generator,
-    criterion,
-):
-    decision_high = discriminator(high_quality)
-    d_loss_high = criterion(decision_high, high_labels)
-    # print(f"Is this real?: {discriminator_decision_from_real} | {d_loss_real}")
-
-    decision_low = discriminator(low_quality)
-    d_loss_low = criterion(decision_low, low_labels)
-    # print(f"Is this real?: {discriminator_decision_from_fake} | {d_loss_fake}")
-
-    with torch.no_grad():
-        generator_quality = generator(low_quality)
-    decision_gen = discriminator(generator_quality)
-    d_loss_gen = criterion(decision_gen, low_labels)
-
-    noise = torch.rand_like(high_quality) * 0.08
-    decision_noise = discriminator(high_quality + noise)
-    d_loss_noise = criterion(decision_noise, low_labels)
-
-    d_loss = (d_loss_high + d_loss_low + d_loss_gen + d_loss_noise) / 4.0
-
-    return d_loss
-
-
-def generator_train(
-    low_quality, high_quality, real_labels, generator, discriminator, adv_criterion
-):
-    generator_output = generator(low_quality)
-
-    discriminator_decision = discriminator(generator_output)
-    adversarial_loss = adv_criterion(discriminator_decision, real_labels)
-
-    # Signal similarity
-    similarity_loss = signal_mae(generator_output, high_quality)
-
-    combined_loss = adversarial_loss + (similarity_loss * 100)
-
-    return combined_loss, adversarial_loss