⚗️ | Added MultiPeriodDiscriminator implementation from HiFi-GAN

discriminator.py

@@ -1,70 +1,98 @@
+import torch
 import torch.nn as nn
 import torch.nn.utils as utils
+import numpy as np
 
+class PatchEmbedding(nn.Module):
+    """
+    Converts raw audio into a sequence of embeddings (tokens).
+    Small patch_size = Higher Precision (more tokens, finer detail).
+    Large patch_size = Lower Precision (fewer tokens, more global).
+    """
+    def __init__(self, in_channels, embed_dim, patch_size, spectral_norm=True):
+        super().__init__()
+        # We use a Conv1d with stride=patch_size to create non-overlapping patches
+        self.proj = nn.Conv1d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
 
-def discriminator_block(
+        if spectral_norm:
-    in_channels,
+            self.proj = utils.spectral_norm(self.proj)
-    out_channels,
+
-    kernel_size=15,
+    def forward(self, x):
-    stride=1,
+        # x shape: (batch, 1, 8000)
-    dilation=1
+        x = self.proj(x)  # shape: (batch, embed_dim, num_patches)
+        x = x.transpose(1, 2)  # shape: (batch, num_patches, embed_dim)
+        return x
+
+class TransformerDiscriminator(nn.Module):
+    def __init__(
+        self,
+        audio_length=8000,
+        patch_size=16,       # Lower this for higher precision (e.g., 8 or 16)
+        embed_dim=128,       # Dimension of the transformer tokens
+        depth=4,             # Number of Transformer blocks
+        heads=4,             # Number of attention heads
+        mlp_dim=256,         # Hidden dimension of the feed-forward layer
+        spectral_norm=True
     ):
-    padding = dilation * (kernel_size - 1) // 2
+        super().__init__()
 
-    conv_layer = nn.Conv1d(
+        # 1. Calculate sequence length
-        in_channels,
+        self.num_patches = audio_length // patch_size
-        out_channels,
+
-        kernel_size=kernel_size,
+        # 2. Patch Embedding (Tokenizer)
-        stride=stride,
+        self.patch_embed = PatchEmbedding(1, embed_dim, patch_size, spectral_norm)
-        dilation=dilation,
+
-        padding=padding
+        # 3. Class Token (like in BERT/ViT) to aggregate global info
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+
+        # 4. Positional Embedding (Learnable)
+        self.pos_embed = nn.Parameter(torch.randn(1, self.num_patches + 1, embed_dim))
+
+        # 5. Transformer Encoder
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=embed_dim,
+            nhead=heads,
+            dim_feedforward=mlp_dim,
+            dropout=0.1,
+            activation='gelu',
+            batch_first=True
         )
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=depth)
 
-    conv_layer = utils.spectral_norm(conv_layer)
+        # 6. Final Classification Head
-    leaky_relu = nn.LeakyReLU(0.2)
+        self.norm = nn.LayerNorm(embed_dim)
+        self.head = nn.Linear(embed_dim, 1)
 
-    return nn.Sequential(conv_layer, leaky_relu)
+        if spectral_norm:
+            self.head = utils.spectral_norm(self.head)
 
+        # Initialize weights
+        self._init_weights()
 
-class AttentionBlock(nn.Module):
+    def _init_weights(self):
-    def __init__(self, channels):
+        nn.init.normal_(self.cls_token, std=0.02)
-        super(AttentionBlock, self).__init__()
+        nn.init.normal_(self.pos_embed, std=0.02)
-        self.attention = nn.Sequential(
-            nn.Conv1d(channels, channels // 4, kernel_size=1),
-            nn.ReLU(),
-            nn.Conv1d(channels // 4, channels, kernel_size=1),
-            nn.Sigmoid(),
-        )
 
     def forward(self, x):
-        attention_weights = self.attention(x)
+        b, c, t = x.shape
-        return x + (x * attention_weights)
 
+        # --- 1. Tokenize Audio ---
+        x = self.patch_embed(x)  # (Batch, Num_Patches, Embed_Dim)
 
-class SISUDiscriminator(nn.Module):
+        # --- 2. Add CLS Token ---
-    def __init__(self, layers=8):
+        cls_tokens = self.cls_token.expand(b, -1, -1)
-        super(SISUDiscriminator, self).__init__()
+        x = torch.cat((cls_tokens, x), dim=1)  # (Batch, Num_Patches + 1, Embed_Dim)
-        self.discriminator_blocks = nn.Sequential(
-            # 1 -> 32
-            discriminator_block(2, layers),
-            AttentionBlock(layers),
-            # 32 -> 64
-            discriminator_block(layers, layers * 2, dilation=2),
-            # 64 -> 128
-            discriminator_block(layers * 2, layers * 4, dilation=4),
-            AttentionBlock(layers * 4),
-            # 128 -> 256
-            discriminator_block(layers * 4, layers * 8, stride=4),
-            # 256 -> 512
-            # discriminator_block(layers * 8, layers * 16, stride=4)
-        )
 
-        self.final_conv = nn.Conv1d(layers * 8, 1, kernel_size=3, padding=1)
+        # --- 3. Add Positional Embeddings ---
+        x = x + self.pos_embed
 
-        self.avg_pool = nn.AdaptiveAvgPool1d(1)
+        # --- 4. Transformer Layers ---
+        x = self.transformer(x)
 
-    def forward(self, x):
+        # --- 5. Classification (Use only CLS token) ---
-        x = self.discriminator_blocks(x)
+        cls_output = x[:, 0]  # Take the first token
-        x = self.final_conv(x)
+        cls_output = self.norm(cls_output)
-        x = self.avg_pool(x)
+
-        return x.squeeze(2)
+        score = self.head(cls_output) # (Batch, 1)
+
+        return score

generator.py

@@ -1,19 +1,20 @@
 import torch
 import torch.nn as nn
-
+from torch.nn.utils.parametrizations import weight_norm
 
 def GeneratorBlock(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
     padding = (kernel_size - 1) // 2 * dilation
+
     return nn.Sequential(
-        nn.Conv1d(
+
+        weight_norm(nn.Conv1d(
             in_channels,
             out_channels,
             kernel_size=kernel_size,
             stride=stride,
             dilation=dilation,
             padding=padding
-        ),
+        )),
-        nn.InstanceNorm1d(out_channels),
         nn.PReLU(num_parameters=1, init=0.1),
     )
 
@@ -22,9 +23,9 @@ class AttentionBlock(nn.Module):
     def __init__(self, channels):
         super(AttentionBlock, self).__init__()
         self.attention = nn.Sequential(
-            nn.Conv1d(channels, channels // 4, kernel_size=1),
+            weight_norm(nn.Conv1d(channels, channels // 4, kernel_size=1)),
             nn.ReLU(inplace=True),
-            nn.Conv1d(channels // 4, channels, kernel_size=1),
+            weight_norm(nn.Conv1d(channels // 4, channels, kernel_size=1)),
             nn.Sigmoid(),
         )
 
@@ -49,21 +50,21 @@ class ResidualInResidualBlock(nn.Module):
         x = self.attention(x)
         return x + residual
 
-def UpsampleBlock(in_channels, out_channels):
+def UpsampleBlock(in_channels, out_channels, scale_factor=2):
     return nn.Sequential(
-        nn.ConvTranspose1d(
+        nn.Upsample(scale_factor=scale_factor, mode='nearest'),
+        weight_norm(nn.Conv1d(
             in_channels=in_channels,
             out_channels=out_channels,
-            kernel_size=4,
+            kernel_size=3,
-            stride=2,
+            stride=1,
             padding=1
-        ),
+        )),
-        nn.InstanceNorm1d(out_channels),
         nn.PReLU(num_parameters=1, init=0.1)
     )
 
 class SISUGenerator(nn.Module):
-    def __init__(self, channels=32, num_rirb=1):
+    def __init__(self, channels=32, num_rirb=4):
         super(SISUGenerator, self).__init__()
 
         self.first_conv = GeneratorBlock(1, channels)
@@ -73,10 +74,9 @@ class SISUGenerator(nn.Module):
         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(
-        # self.rirb = nn.Sequential(
+            *[ResidualInResidualBlock(channels * 4) for _ in range(num_rirb)]
-        #     *[ResidualInResidualBlock(channels * 4) for _ in range(num_rirb)]
+        )
-        # )
 
         self.upsample = UpsampleBlock(channels * 4, channels * 2)
         self.upsample_attn = AttentionBlock(channels * 2)
@@ -87,13 +87,15 @@ class SISUGenerator(nn.Module):
         self.compress_2 = GeneratorBlock(channels * 2, channels)
 
         self.final_conv = nn.Sequential(
-            nn.Conv1d(channels, 1, kernel_size=7, padding=3),
+            weight_norm(nn.Conv1d(channels, 1, kernel_size=7, padding=3)),
             nn.Tanh()
         )
 
 
     def forward(self, x):
         residual_input = x
+
+        # Encoding
         x1 = self.first_conv(x)
 
         x2 = self.downsample(x1)
@@ -102,8 +104,10 @@ class SISUGenerator(nn.Module):
         x3 = self.downsample_2(x2)
         x3 = self.downsample_2_attn(x3)
 
+        # Bottleneck (Deep Residual processing)
         x_rirb = self.rirb(x3)
 
+        # Decoding with Skip Connections
         up1 = self.upsample(x_rirb)
         up1 = self.upsample_attn(up1)
 

training.py

@@ -3,7 +3,6 @@ import datetime
 import os
 
 import torch
-import torch.nn as nn
 import torch.optim as optim
 import tqdm
 from accelerate import Accelerator
@@ -23,7 +22,7 @@ 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("--num_workers", type=int, default=4, help="DataLoader num_workers") # Increased workers slightly
 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"
@@ -94,8 +93,6 @@ scheduler_d = torch.optim.lr_scheduler.ReduceLROnPlateau(
     optimizer_d, mode="min", factor=0.5, patience=5
 )
 
-criterion_d = nn.MSELoss()
-
 # ---------------------------
 # Prepare accelerator
 # ---------------------------
@@ -131,6 +128,7 @@ def save_ckpt(path, epoch):
 start_epoch = 0
 if args.resume:
     ckpt_path = os.path.join(models_dir, "last.pt")
+    if os.path.exists(ckpt_path):
         ckpt = torch.load(ckpt_path)
 
         accelerator.unwrap_model(generator).load_state_dict(ckpt["G"])
@@ -142,12 +140,13 @@ if args.resume:
 
         start_epoch = ckpt.get("epoch", 1)
         accelerator.print(f"🔁 | Resumed from epoch {start_epoch}!")
-
+    else:
-real_buf = torch.full((loader_batch_size, 1), 1, device=accelerator.device, dtype=torch.float32)
+        accelerator.print("⚠️ | Resume requested but no checkpoint found. Starting fresh.")
-fake_buf = torch.zeros((loader_batch_size, 1), device=accelerator.device, dtype=torch.float32)
 
 accelerator.print("🏋️ | Started training...")
 
+smallest_loss = float('inf')
+
 try:
     for epoch in range(start_epoch, args.epochs):
         generator.train()
@@ -164,11 +163,6 @@ try:
             (high_quality, low_quality),
             (high_sample_rate, low_sample_rate),
         ) 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)
 
@@ -179,10 +173,7 @@ try:
                 d_loss = discriminator_train(
                     high_quality,
                     low_quality.detach(),
-                    real_labels,
-                    fake_labels,
                     discriminator,
-                    criterion_d,
                     generator_output.detach()
                 )
 
@@ -197,10 +188,8 @@ try:
                 g_total, g_adv = generator_train(
                     low_quality,
                     high_quality,
-                    real_labels,
                     generator,
                     discriminator,
-                    criterion_d,
                     generator_output
                 )
 
@@ -241,6 +230,9 @@ try:
         scheduler_g.step(mean_g)
 
         save_ckpt(os.path.join(models_dir, "last.pt"), epoch)
+        if smallest_loss > mean_g:
+            smallest_loss = mean_g
+            save_ckpt(os.path.join(models_dir, "latest-smallest_loss.pt"), epoch)
         accelerator.print(f"🤝 | Epoch {epoch} done | D {mean_d:.4f} | G {mean_g:.4f}")
 
 except Exception:

utils/TrainingTools.py

@@ -1,58 +1,113 @@
 import torch
-
+import torch.nn.functional as F
 from utils.MultiResolutionSTFTLoss import MultiResolutionSTFTLoss
 
-# 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:
+def feature_matching_loss(fmap_r, fmap_g):
-    absolute_difference = torch.abs(input_one - input_two)
+    """
-    return torch.mean(absolute_difference)
+    Computes L1 distance between real and fake feature maps.
+    """
+    loss = 0
+    for dr, dg in zip(fmap_r, fmap_g):
+        for rl, gl in zip(dr, dg):
+            # Stop gradient on real features to save memory/computation
+            rl = rl.detach()
+            loss += torch.mean(torch.abs(rl - gl))
+
+    # Scale by number of feature maps to keep loss magnitude reasonable
+    return loss * 2
+
+
+def discriminator_loss(disc_real_outputs, disc_generated_outputs):
+    """
+    Least Squares GAN Loss (LSGAN) for the Discriminator.
+    Objective: Real -> 1, Fake -> 0
+    """
+    loss = 0
+    r_losses = []
+    g_losses = []
+
+    # Iterate over both MPD and MSD outputs
+    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
+        # Real should be 1.0
+        r_loss = torch.mean((dr - 1) ** 2)
+        # Fake should be 0.0
+        g_loss = torch.mean(dg ** 2)
+
+        loss += (r_loss + g_loss)
+        r_losses.append(r_loss.item())
+        g_losses.append(g_loss.item())
+
+    return loss, r_losses, g_losses
+
+
+def generator_adv_loss(disc_generated_outputs):
+    """
+    Least Squares GAN Loss for the Generator.
+    Objective: Fake -> 1 (Fool the discriminator)
+    """
+    loss = 0
+    for dg in zip(disc_generated_outputs):
+        dg = dg[0] # Unpack tuple
+        loss += torch.mean((dg - 1) ** 2)
+    return loss
 
 
 def discriminator_train(
     high_quality,
     low_quality,
-    high_labels,
-    low_labels,
     discriminator,
-    criterion,
     generator_output
 ):
+    # 1. Forward pass through the Ensemble Discriminator
+    # Note: We pass inputs separately now: (Real_Target, Fake_Candidate)
+    # We detach generator_output because we are only optimizing D here
+    y_d_rs, y_d_gs, _, _ = discriminator(high_quality, generator_output.detach())
 
-    real_pair = torch.cat((low_quality, high_quality), dim=1)
+    # 2. Calculate Loss (LSGAN)
-    decision_real = discriminator(real_pair)
+    d_loss, _, _ = discriminator_loss(y_d_rs, y_d_gs)
-    d_loss_real = criterion(decision_real, high_labels)
 
-    fake_pair = torch.cat((low_quality, generator_output), dim=1)
-    decision_fake = discriminator(fake_pair)
-    d_loss_fake = criterion(decision_fake, low_labels)
-
-    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, generator_output):
+    low_quality,
+    high_quality,
+    generator,
+    discriminator,
+    generator_output
+):
+    # 1. Forward pass through Discriminator
+    # We do NOT detach generator_output here, we need gradients for G
+    y_d_rs, y_d_gs, fmap_rs, fmap_gs = discriminator(high_quality, generator_output)
 
-    fake_pair = torch.cat((low_quality, generator_output), dim=1)
+    # 2. Adversarial Loss (Try to fool D into thinking G is Real)
+    loss_gen_adv = generator_adv_loss(y_d_gs)
 
-    discriminator_decision = discriminator(fake_pair)
+    # 3. Feature Matching Loss (Force G to match internal features of D)
-    adversarial_loss = adv_criterion(discriminator_decision, real_labels)
+    loss_fm = feature_matching_loss(fmap_rs, fmap_gs)
 
-    mae_loss = signal_mae(generator_output, high_quality)
+    # 4. Mel-Spectrogram / STFT Loss (Audio Quality)
     stft_loss = stft_loss_fn(high_quality, generator_output)["total"]
 
-    lambda_mae = 10.0
+    # -----------------------------------------
-    lambda_stft = 2.5
+    # 5. Combine Losses
-    lambda_adv = 2.5
+    # -----------------------------------------
-    combined_loss = (lambda_mae * mae_loss) + (lambda_stft * stft_loss) + (lambda_adv * adversarial_loss)
+    # Recommended weights for HiFi-GAN/EnCodec style architectures:
-    return combined_loss, adversarial_loss
+    # STFT is dominant (45), FM provides stability (2), Adv provides texture (1)
+
+    lambda_stft = 45.0
+    lambda_fm = 2.0
+    lambda_adv = 1.0
+
+    combined_loss = (lambda_stft * stft_loss) + \
+                    (lambda_fm * loss_fm) + \
+                    (lambda_adv * loss_gen_adv)
+
+    return combined_loss, loss_gen_adv