⚗️ | More architectural changes

AudioUtils.py

@@ -3,95 +3,39 @@ import torch.nn.functional as F
 
 
 def stereo_tensor_to_mono(waveform: torch.Tensor) -> torch.Tensor:
-    """
+    mono_tensor = torch.mean(waveform, dim=0, keepdim=True)
-    Convert stereo (C, N) to mono (1, N). Ensures a channel dimension.
+    return mono_tensor
-    """
-    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)
-    else:
-        mono_waveform = waveform
-    return mono_waveform
 
 
-def stretch_tensor(tensor: torch.Tensor, target_length: int) -> torch.Tensor:
+def pad_tensor(audio_tensor: torch.Tensor, target_length: int = 512) -> torch.Tensor:
-    """
+    padding_amount = target_length - audio_tensor.size(-1)
-    Stretch audio along time dimension to target_length.
+    if padding_amount <= 0:
-    Input assumed (1, N). Returns (1, target_length).
+        return audio_tensor
-    """
-    if tensor.dim() == 1:
-        tensor = tensor.unsqueeze(0)  # ensure (1, N)
 
-    tensor = tensor.unsqueeze(0)  # (1, 1, N) for interpolate
+    padded_audio_tensor = F.pad(audio_tensor, (0, padding_amount))
-    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(
+def split_audio(audio_tensor: torch.Tensor, chunk_size: int = 512, pad_last_tensor: bool = False) -> list[torch.Tensor]:
-    audio_tensor: torch.Tensor, chunk_size: int = 128
+    chunks = list(torch.split(audio_tensor, chunk_size, dim=1))
-) -> 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:
+    if pad_last_tensor:
-        audio_tensor = audio_tensor.unsqueeze(0)
+        last_chunk = chunks[-1]
 
-    num_samples = audio_tensor.shape[-1]
+        if last_chunk.size(-1) < chunk_size:
-    if num_samples == 0:
+                    chunks[-1] = pad_tensor(last_chunk, chunk_size)
-        return []
 
-    chunks = list(torch.split(audio_tensor, chunk_size, dim=-1))
     return chunks
 
 
 def reconstruct_audio(chunks: list[torch.Tensor]) -> torch.Tensor:
-    """
+    reconstructed_tensor = torch.cat(chunks, dim=-1)
-    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
     return audio_tensor / max_val

app.py

@@ -4,6 +4,7 @@ import torch
 import torchaudio
 import torchcodec
 import tqdm
+from accelerate import Accelerator
 
 import AudioUtils
 from generator import SISUGenerator
@@ -15,7 +16,7 @@ parser.add_argument("--model", type=str, help="Model to use for upscaling")
 parser.add_argument(
     "--clip_length",
     type=int,
-    default=16384,
+    default=8000,
     help="Internal clip length, leave unspecified if unsure",
 )
 parser.add_argument(
@@ -38,21 +39,44 @@ if args.sample_rate < 8000:
     )
     exit()
 
-device = torch.device(args.device if torch.cuda.is_available() else "cpu")
+# ---------------------------
-print(f"Using device: {device}")
+# Init accelerator
+# ---------------------------
 
-generator = SISUGenerator().to(device)
+accelerator = Accelerator(mixed_precision="bf16")
+
+# ---------------------------
+# Models
+# ---------------------------
+generator = SISUGenerator()
+
+accelerator.print("🔨 | Compiling models...")
 
 generator = torch.compile(generator)
 
+accelerator.print("✅ | Compiling done!")
+
+# ---------------------------
+# Prepare accelerator
+# ---------------------------
+
+generator = accelerator.prepare(generator)
+
+# ---------------------------
+# Checkpoint helpers
+# ---------------------------
+
 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)
+    ckpt = torch.load(models_dir)
-    generator.load_state_dict(ckpt["G"])
+
+    accelerator.unwrap_model(generator).load_state_dict(ckpt["G"])
+
+    accelerator.print("💾 | Loaded model!")
 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!)"
@@ -67,7 +91,8 @@ def start():
     audio = decoded_samples.data
     original_sample_rate = decoded_samples.sample_rate
 
-    audio = AudioUtils.stereo_tensor_to_mono(audio)
+    # Support for multichannel audio
+    # audio = AudioUtils.stereo_tensor_to_mono(audio)
     audio = AudioUtils.normalize(audio)
 
     resample_transform = torchaudio.transforms.Resample(
@@ -77,14 +102,20 @@ def start():
     audio = resample_transform(audio)
 
     splitted_audio = AudioUtils.split_audio(audio, clip_length)
-    splitted_audio_on_device = [t.to(device) for t in splitted_audio]
+    splitted_audio_on_device = [t.view(1, t.shape[0], t.shape[-1]).to(accelerator.device) for t in splitted_audio]
     processed_audio = []
-
+    with torch.no_grad():
-    for clip in tqdm.tqdm(splitted_audio_on_device, desc="Processing..."):
+        for clip in tqdm.tqdm(splitted_audio_on_device, desc="Processing..."):
-        processed_audio.append(generator(clip))
+            channels = []
+            for audio_channel in torch.split(clip, 1, dim=1):
+                output_piece = generator(audio_channel)
+                channels.append(output_piece.detach().cpu())
+            output_clip = torch.cat(channels, dim=1)
+            processed_audio.append(output_clip)
 
     reconstructed_audio = AudioUtils.reconstruct_audio(processed_audio)
-    print(f"Saving {output_audio}!")
+    reconstructed_audio = reconstructed_audio.squeeze(0)
+    print(f"🔊 | Saving {output_audio}!")
     torchaudio.save_with_torchcodec(
         uri=output_audio,
         src=reconstructed_audio,

data.py

@@ -1,6 +1,7 @@
 import os
 import random
 
+import torch
 import torchaudio
 import torchcodec.decoders as decoders
 import tqdm
@@ -10,9 +11,9 @@ import AudioUtils
 
 
 class AudioDataset(Dataset):
-    audio_sample_rates = [11025]
+    audio_sample_rates = [8000, 11025, 12000, 16000, 22050, 24000, 32000, 44100]
 
-    def __init__(self, input_dir, clip_length: int = 8000, normalize: bool = True):
+    def __init__(self, input_dir, clip_length: int = 512, normalize: bool = True):
         self.clip_length = clip_length
         self.normalize = normalize
 
@@ -30,45 +31,20 @@ class AudioDataset(Dataset):
             decoder = decoders.AudioDecoder(audio_clip)
             decoded_samples = decoder.get_all_samples()
 
-            audio = decoded_samples.data.float()  # ensure float32
+            audio = decoded_samples.data.float()
             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)
+            splitted_high_quality_audio = AudioUtils.split_audio(audio, clip_length, True)
-            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))
+            if not splitted_high_quality_audio:
+                continue
 
-            splitted_high_quality_audio = AudioUtils.split_audio(audio, clip_length)
+            for splitted_audio_clip in splitted_high_quality_audio:
-            splitted_low_quality_audio = AudioUtils.split_audio(low_audio, clip_length)
+                for audio_clip in torch.split(splitted_audio_clip, 1):
-
+                    data.append((audio_clip, original_sample_rate))
-            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
 
@@ -76,4 +52,20 @@ class AudioDataset(Dataset):
         return len(self.audio_data)
 
     def __getitem__(self, idx):
-        return self.audio_data[idx]
+        audio_clip = self.audio_data[idx]
+        mangled_sample_rate = random.choice(self.audio_sample_rates)
+
+        resample_transform_low = torchaudio.transforms.Resample(
+            audio_clip[1], mangled_sample_rate
+        )
+
+        resample_transform_high = torchaudio.transforms.Resample(
+            mangled_sample_rate, audio_clip[1]
+        )
+
+        low_audio_clip = resample_transform_high(resample_transform_low(audio_clip[0]))
+        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]:
+            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))

discriminator.py

@@ -5,32 +5,25 @@ import torch.nn.utils as utils
 def discriminator_block(
     in_channels,
     out_channels,
-    kernel_size=3,
+    kernel_size=15,
     stride=1,
-    dilation=1,
+    dilation=1
-    spectral_norm=True,
-    use_instance_norm=True,
 ):
-    padding = (kernel_size // 2) * dilation
+    padding = dilation * (kernel_size - 1) // 2
+
     conv_layer = nn.Conv1d(
         in_channels,
         out_channels,
         kernel_size=kernel_size,
         stride=stride,
         dilation=dilation,
-        padding=padding,
+        padding=padding
     )
 
-    if spectral_norm:
+    conv_layer = utils.spectral_norm(conv_layer)
-        conv_layer = utils.spectral_norm(conv_layer)
+    leaky_relu = nn.LeakyReLU(0.2)
 
-    layers = [conv_layer]
+    return nn.Sequential(conv_layer, leaky_relu)
-    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):
@@ -38,38 +31,40 @@ class AttentionBlock(nn.Module):
         super(AttentionBlock, self).__init__()
         self.attention = nn.Sequential(
             nn.Conv1d(channels, channels // 4, kernel_size=1),
-            nn.ReLU(inplace=True),
+            nn.ReLU(),
             nn.Conv1d(channels // 4, channels, kernel_size=1),
             nn.Sigmoid(),
         )
 
     def forward(self, x):
         attention_weights = self.attention(x)
-        return x * attention_weights
+        return x + (x * attention_weights)
 
 
 class SISUDiscriminator(nn.Module):
-    def __init__(self, layers=32):
+    def __init__(self, layers=8):
         super(SISUDiscriminator, self).__init__()
-        self.model = nn.Sequential(
+        self.discriminator_blocks = nn.Sequential(
-            discriminator_block(1, layers, kernel_size=7, stride=1),
+            # 1 -> 32
-            discriminator_block(layers, layers * 2, kernel_size=5, stride=2),
+            discriminator_block(2, layers),
-            discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=2),
+            AttentionBlock(layers),
+            # 32 -> 64
+            discriminator_block(layers, layers * 2, dilation=2),
+            # 64 -> 128
+            discriminator_block(layers * 2, layers * 4, dilation=4),
             AttentionBlock(layers * 4),
-            discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=4),
+            # 128 -> 256
-            discriminator_block(layers * 8, layers * 2, kernel_size=5, stride=2),
+            discriminator_block(layers * 4, layers * 8, stride=4),
-            discriminator_block(
+            # 256 -> 512
-                layers * 2,
+            # discriminator_block(layers * 8, layers * 16, stride=4)
-                1,
-                spectral_norm=False,
-                use_instance_norm=False,
-            ),
         )
 
-        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
+        self.final_conv = nn.Conv1d(layers * 8, 1, kernel_size=3, padding=1)
+
+        self.avg_pool = nn.AdaptiveAvgPool1d(1)
 
     def forward(self, x):
-        x = self.model(x)
+        x = self.discriminator_blocks(x)
-        x = self.global_avg_pool(x)
+        x = self.final_conv(x)
-        x = x.view(x.size(0), -1)
+        x = self.avg_pool(x)
-        return x
+        return x.squeeze(2)

generator.py

@@ -2,25 +2,23 @@ import torch
 import torch.nn as nn
 
 
-def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
+def GeneratorBlock(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
+    padding = (kernel_size - 1) // 2 * dilation
     return nn.Sequential(
         nn.Conv1d(
             in_channels,
             out_channels,
             kernel_size=kernel_size,
+            stride=stride,
             dilation=dilation,
-            padding=(kernel_size // 2) * dilation,
+            padding=padding
         ),
         nn.InstanceNorm1d(out_channels),
-        nn.PReLU(),
+        nn.PReLU(num_parameters=1, init=0.1),
     )
 
 
 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(
@@ -32,7 +30,7 @@ class AttentionBlock(nn.Module):
 
     def forward(self, x):
         attention_weights = self.attention(x)
-        return x * attention_weights
+        return x + (x * attention_weights)
 
 
 class ResidualInResidualBlock(nn.Module):
@@ -40,7 +38,7 @@ class ResidualInResidualBlock(nn.Module):
         super(ResidualInResidualBlock, self).__init__()
 
         self.conv_layers = nn.Sequential(
-            *[conv_block(channels, channels) for _ in range(num_convs)]
+            *[GeneratorBlock(channels, channels) for _ in range(num_convs)]
         )
 
         self.attention = AttentionBlock(channels)
@@ -51,31 +49,74 @@ class ResidualInResidualBlock(nn.Module):
         x = self.attention(x)
         return x + residual
 
+def UpsampleBlock(in_channels, out_channels):
+    return nn.Sequential(
+        nn.ConvTranspose1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=4,
+            stride=2,
+            padding=1
+        ),
+        nn.InstanceNorm1d(out_channels),
+        nn.PReLU(num_parameters=1, init=0.1)
+    )
 
 class SISUGenerator(nn.Module):
-    def __init__(self, channels=16, num_rirb=4, alpha=1):
+    def __init__(self, channels=32, num_rirb=1):
         super(SISUGenerator, self).__init__()
-        self.alpha = alpha
 
-        self.conv1 = nn.Sequential(
+        self.first_conv = GeneratorBlock(1, channels)
-            nn.Conv1d(1, channels, kernel_size=7, padding=3),
+
-            nn.InstanceNorm1d(channels),
+        self.downsample = GeneratorBlock(channels, channels * 2, stride=2)
-            nn.PReLU(),
+        self.downsample_attn = AttentionBlock(channels * 2)
+        self.downsample_2 = GeneratorBlock(channels * 2, channels * 4, stride=2)
+        self.downsample_2_attn = AttentionBlock(channels * 4)
+
+        self.rirb = ResidualInResidualBlock(channels * 4)
+        # self.rirb = nn.Sequential(
+        #     *[ResidualInResidualBlock(channels * 4) for _ in range(num_rirb)]
+        # )
+
+        self.upsample = UpsampleBlock(channels * 4, channels * 2)
+        self.upsample_attn = AttentionBlock(channels * 2)
+        self.compress_1 = GeneratorBlock(channels * 4, channels * 2)
+
+        self.upsample_2 = UpsampleBlock(channels * 2, channels)
+        self.upsample_2_attn = AttentionBlock(channels)
+        self.compress_2 = GeneratorBlock(channels * 2, channels)
+
+        self.final_conv = nn.Sequential(
+            nn.Conv1d(channels, 1, kernel_size=7, padding=3),
+            nn.Tanh()
         )
 
-        self.rir_blocks = nn.Sequential(
-            *[ResidualInResidualBlock(channels) for _ in range(num_rirb)]
-        )
-
-        self.final_layer = nn.Sequential(
-            nn.Conv1d(channels, 1, kernel_size=3, padding=1), nn.Tanh()
-        )
 
     def forward(self, x):
         residual_input = x
-        x = self.conv1(x)
+        x1 = self.first_conv(x)
-        x_rirb_out = self.rir_blocks(x)
-        learned_residual = self.final_layer(x_rirb_out)
-        output = residual_input + self.alpha * learned_residual
 
-        return torch.tanh(output)
+        x2 = self.downsample(x1)
+        x2 = self.downsample_attn(x2)
+
+        x3 = self.downsample_2(x2)
+        x3 = self.downsample_2_attn(x3)
+
+        x_rirb = self.rirb(x3)
+
+        up1 = self.upsample(x_rirb)
+        up1 = self.upsample_attn(up1)
+
+        cat1 = torch.cat((up1, x2), dim=1)
+        comp1 = self.compress_1(cat1)
+
+        up2 = self.upsample_2(comp1)
+        up2 = self.upsample_2_attn(up2)
+
+        cat2 = torch.cat((up2, x1), dim=1)
+        comp2 = self.compress_2(cat2)
+
+        learned_residual = self.final_conv(comp2)
+
+        output = residual_input + learned_residual
+        return output

training.py

@@ -1,4 +1,5 @@
 import argparse
+import datetime
 import os
 
 import torch
@@ -52,7 +53,7 @@ accelerator.print("✅ | Compiling done!")
 # Dataset / DataLoader
 # ---------------------------
 accelerator.print("📊 | Fetching dataset...")
-dataset = AudioDataset("./dataset")
+dataset = AudioDataset("./dataset", 8192)
 
 sampler = DistributedSampler(dataset) if accelerator.num_processes > 1 else None
 pin_memory = torch.cuda.is_available() and not args.no_pin_memory
@@ -93,7 +94,6 @@ scheduler_d = torch.optim.lr_scheduler.ReduceLROnPlateau(
     optimizer_d, mode="min", factor=0.5, patience=5
 )
 
-criterion_g = nn.BCEWithLogitsLoss()
 criterion_d = nn.MSELoss()
 
 # ---------------------------
@@ -143,12 +143,8 @@ if args.resume:
     start_epoch = ckpt.get("epoch", 1)
     accelerator.print(f"🔁 | Resumed from epoch {start_epoch}!")
 
-real_buf = torch.full(
+real_buf = torch.full((loader_batch_size, 1), 1, device=accelerator.device, dtype=torch.float32)
-    (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)
-)
-fake_buf = torch.zeros(
-    (loader_batch_size, 1), device=accelerator.device, dtype=torch.float32
-)
 
 accelerator.print("🏋️ | Started training...")
 
@@ -157,35 +153,45 @@ try:
         generator.train()
         discriminator.train()
 
+        discriminator_time = 0
+        generator_time = 0
+
         running_d, running_g, steps = 0.0, 0.0, 0
 
+        progress_bar = tqdm.tqdm(train_loader, desc=f"Epoch {epoch} | D {discriminator_time}μs | G {generator_time}μs")
+
         for i, (
             (high_quality, low_quality),
             (high_sample_rate, low_sample_rate),
-        ) in enumerate(tqdm.tqdm(train_loader, desc=f"Epoch {epoch}")):
+        ) in enumerate(progress_bar):
             batch_size = high_quality.size(0)
 
             real_labels = real_buf[:batch_size].to(accelerator.device)
             fake_labels = fake_buf[:batch_size].to(accelerator.device)
 
+            with accelerator.autocast():
+                generator_output = generator(low_quality)
+
             # --- Discriminator ---
+            d_time = datetime.datetime.now()
             optimizer_d.zero_grad(set_to_none=True)
             with accelerator.autocast():
                 d_loss = discriminator_train(
                     high_quality,
-                    low_quality,
+                    low_quality.detach(),
                     real_labels,
                     fake_labels,
                     discriminator,
-                    generator,
                     criterion_d,
+                    generator_output.detach()
                 )
 
             accelerator.backward(d_loss)
-            torch.nn.utils.clip_grad_norm_(discriminator.parameters(), 1)
             optimizer_d.step()
+            discriminator_time = (datetime.datetime.now() - d_time).microseconds
 
             # --- Generator ---
+            g_time = datetime.datetime.now()
             optimizer_g.zero_grad(set_to_none=True)
             with accelerator.autocast():
                 g_total, g_adv = generator_train(
@@ -195,11 +201,13 @@ try:
                     generator,
                     discriminator,
                     criterion_d,
+                    generator_output
                 )
 
             accelerator.backward(g_total)
             torch.nn.utils.clip_grad_norm_(generator.parameters(), 1)
             optimizer_g.step()
+            generator_time = (datetime.datetime.now() - g_time).microseconds
 
             d_val = accelerator.gather(d_loss.detach()).mean()
             g_val = accelerator.gather(g_total.detach()).mean()
@@ -219,6 +227,7 @@ try:
                 )
 
             steps += 1
+            progress_bar.set_description(f"Epoch {epoch} | D {discriminator_time}μs | G {generator_time}μs")
 
         # epoch averages & schedulers
         if steps == 0:

utils/MultiResolutionSTFTLoss.py

@@ -7,18 +7,13 @@ 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],
+        fft_sizes: List[int] = [512, 1024, 2048, 4096, 8192],
-        hop_sizes: List[int] = [120, 240, 50],
+        hop_sizes: List[int] = [64, 128, 256, 512, 1024],
-        win_lengths: List[int] = [600, 1200, 240],
+        win_lengths: List[int] = [256, 512, 1024, 2048, 4096],
         eps: float = 1e-7,
+        center: bool = True
     ):
         super().__init__()
 
@@ -26,15 +21,14 @@ class MultiResolutionSTFTLoss(nn.Module):
         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):
+        for i, (n_fft, hop_len, win_len) in enumerate(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,
+                window_fn=torch.hann_window,
-                power=None,  # Keep complex output
+                power=None,
-                center=True,
+                center=center,
                 pad_mode="reflect",
                 normalized=False,
             )
@@ -43,12 +37,6 @@ class MultiResolutionSTFTLoss(nn.Module):
     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:
@@ -58,28 +46,21 @@ class MultiResolutionSTFTLoss(nn.Module):
         mag_loss = 0.0
 
         for stft in self.stft_transforms:
-            stft = stft.to(y_pred.device)
+            stft.window = stft.window.to(y_true.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 ---
+            log_mag_pred = torch.log(stft_mag_pred + self.eps)
-            mag_loss += F.l1_loss(
+            log_mag_true = torch.log(stft_mag_true + self.eps)
-                torch.log(stft_mag_pred + self.eps),
+            mag_loss += F.l1_loss(log_mag_pred, log_mag_true)
-                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

utils/TrainingTools.py

@@ -1,12 +1,17 @@
 import torch
 
-# In case if needed again...
+from utils.MultiResolutionSTFTLoss import MultiResolutionSTFTLoss
-# from utils.MultiResolutionSTFTLoss import MultiResolutionSTFTLoss
-#
-# stft_loss_fn = MultiResolutionSTFTLoss(
-#     fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240]
-# )
 
+# stft_loss_fn = MultiResolutionSTFTLoss(
+#     fft_sizes=[512, 1024, 2048, 4096],
+#     hop_sizes=[128, 256, 512, 1024],
+#     win_lengths=[512, 1024, 2048, 4096]
+# )
+stft_loss_fn = MultiResolutionSTFTLoss(
+    fft_sizes=[512, 1024, 2048],
+    hop_sizes=[64, 128, 256],
+    win_lengths=[256, 512, 1024]
+)
 
 def signal_mae(input_one: torch.Tensor, input_two: torch.Tensor) -> torch.Tensor:
     absolute_difference = torch.abs(input_one - input_two)
@@ -19,42 +24,35 @@ def discriminator_train(
     high_labels,
     low_labels,
     discriminator,
-    generator,
     criterion,
+    generator_output
 ):
-    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)
+    real_pair = torch.cat((low_quality, high_quality), dim=1)
-    d_loss_low = criterion(decision_low, low_labels)
+    decision_real = discriminator(real_pair)
-    # print(f"Is this real?: {discriminator_decision_from_fake} | {d_loss_fake}")
+    d_loss_real = criterion(decision_real, high_labels)
 
-    with torch.no_grad():
+    fake_pair = torch.cat((low_quality, generator_output), dim=1)
-        generator_quality = generator(low_quality)
+    decision_fake = discriminator(fake_pair)
-    decision_gen = discriminator(generator_quality)
+    d_loss_fake = criterion(decision_fake, low_labels)
-    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
 
+    d_loss = (d_loss_real + d_loss_fake) / 2.0
     return d_loss
 
 
 def generator_train(
-    low_quality, high_quality, real_labels, generator, discriminator, adv_criterion
+    low_quality, high_quality, real_labels, generator, discriminator, adv_criterion, generator_output):
-):
-    generator_output = generator(low_quality)
 
-    discriminator_decision = discriminator(generator_output)
+    fake_pair = torch.cat((low_quality, generator_output), dim=1)
+
+    discriminator_decision = discriminator(fake_pair)
     adversarial_loss = adv_criterion(discriminator_decision, real_labels)
 
-    # Signal similarity
+    mae_loss = signal_mae(generator_output, high_quality)
-    similarity_loss = signal_mae(generator_output, high_quality)
+    stft_loss = stft_loss_fn(high_quality, generator_output)["total"]
-
-    combined_loss = adversarial_loss + (similarity_loss * 100)
 
+    lambda_mae = 10.0
+    lambda_stft = 2.5
+    lambda_adv = 2.5
+    combined_loss = (lambda_mae * mae_loss) + (lambda_stft * stft_loss) + (lambda_adv * adversarial_loss)
     return combined_loss, adversarial_loss