⚗️ | Added MultiPeriodDiscriminator implementation from HiFi-GAN

discriminator.py

@@ -1,98 +1,179 @@
 import torch
 import torch.nn as nn
-import torch.nn.utils as utils
+import torch.nn.functional as F
-import numpy as np
+from torch.nn.utils.parametrizations import weight_norm, spectral_norm
 
-class PatchEmbedding(nn.Module):
+# -------------------------------------------------------------------
-    """
+# 1. Multi-Period Discriminator (MPD)
-    Converts raw audio into a sequence of embeddings (tokens).
+#    Captures periodic structures (pitch/timbre) by folding audio.
-    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)
 
-        if spectral_norm:
+class DiscriminatorP(nn.Module):
-            self.proj = utils.spectral_norm(self.proj)
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+
+        # Use spectral_norm for stability, or weight_norm for performance
+        norm_f = spectral_norm if use_spectral_norm else weight_norm
+
+        # We use 2D convs because we "fold" the 1D audio into 2D (Period x Time)
+        self.convs = nn.ModuleList([
+            norm_f(nn.Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(2, 0))),
+            norm_f(nn.Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(2, 0))),
+            norm_f(nn.Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(2, 0))),
+            norm_f(nn.Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(2, 0))),
+            norm_f(nn.Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
+        ])
+
+        self.conv_post = norm_f(nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
 
     def forward(self, x):
-        # x shape: (batch, 1, 8000)
+        fmap = []
-        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):
+        # 1d to 2d conversion: [B, C, T] -> [B, C, T/P, P]
-    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
-    ):
-        super().__init__()
-
-        # 1. Calculate sequence length
-        self.num_patches = audio_length // patch_size
-
-        # 2. Patch Embedding (Tokenizer)
-        self.patch_embed = PatchEmbedding(1, embed_dim, patch_size, spectral_norm)
-
-        # 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)
-
-        # 6. Final Classification Head
-        self.norm = nn.LayerNorm(embed_dim)
-        self.head = nn.Linear(embed_dim, 1)
-
-        if spectral_norm:
-            self.head = utils.spectral_norm(self.head)
-
-        # Initialize weights
-        self._init_weights()
-
-    def _init_weights(self):
-        nn.init.normal_(self.cls_token, std=0.02)
-        nn.init.normal_(self.pos_embed, std=0.02)
-
-    def forward(self, x):
         b, c, t = x.shape
+        if t % self.period != 0: # Pad if not divisible by period
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
 
-        # --- 1. Tokenize Audio ---
+        x = x.view(b, c, t // self.period, self.period)
-        x = self.patch_embed(x)  # (Batch, Num_Patches, Embed_Dim)
 
-        # --- 2. Add CLS Token ---
+        for l in self.convs:
-        cls_tokens = self.cls_token.expand(b, -1, -1)
+            x = l(x)
-        x = torch.cat((cls_tokens, x), dim=1)  # (Batch, Num_Patches + 1, Embed_Dim)
+            x = F.leaky_relu(x, 0.1)
+            fmap.append(x) # Store feature map for Feature Matching Loss
 
-        # --- 3. Add Positional Embeddings ---
+        x = self.conv_post(x)
-        x = x + self.pos_embed
+        fmap.append(x)
 
-        # --- 4. Transformer Layers ---
+        # Flatten back to 1D for score
-        x = self.transformer(x)
+        x = torch.flatten(x, 1, -1)
 
-        # --- 5. Classification (Use only CLS token) ---
+        return x, fmap
-        cls_output = x[:, 0]  # Take the first token
-        cls_output = self.norm(cls_output)
 
-        score = self.head(cls_output) # (Batch, 1)
 
-        return score
+class MultiPeriodDiscriminator(nn.Module):
+    def __init__(self, periods=[2, 3, 5, 7, 11]):
+        super(MultiPeriodDiscriminator, self).__init__()
+        self.discriminators = nn.ModuleList([
+            DiscriminatorP(p) for p in periods
+        ])
+
+    def forward(self, y, y_hat):
+        y_d_rs = [] # Real scores
+        y_d_gs = [] # Generated (Fake) scores
+        fmap_rs = [] # Real feature maps
+        fmap_gs = [] # Generated (Fake) feature maps
+
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+# -------------------------------------------------------------------
+# 2. Multi-Scale Discriminator (MSD)
+#    Captures structure at different audio resolutions (raw, x0.5, x0.25).
+# -------------------------------------------------------------------
+
+class DiscriminatorS(nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = spectral_norm if use_spectral_norm else weight_norm
+
+        # Standard 1D Convolutions with large receptive field
+        self.convs = nn.ModuleList([
+            norm_f(nn.Conv1d(1, 16, 15, 1, padding=7)),
+            norm_f(nn.Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+            norm_f(nn.Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+            norm_f(nn.Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+            norm_f(nn.Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+            norm_f(nn.Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(nn.Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, 0.1)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+        return x, fmap
+
+
+class MultiScaleDiscriminator(nn.Module):
+    def __init__(self):
+        super(MultiScaleDiscriminator, self).__init__()
+        # 3 Scales: Original, Downsampled x2, Downsampled x4
+        self.discriminators = nn.ModuleList([
+            DiscriminatorS(use_spectral_norm=True),
+            DiscriminatorS(),
+            DiscriminatorS(),
+        ])
+        self.meanpools = nn.ModuleList([
+            nn.AvgPool1d(4, 2, padding=2),
+            nn.AvgPool1d(4, 2, padding=2)
+        ])
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+
+        for i, d in enumerate(self.discriminators):
+            if i != 0:
+                # Downsample input for subsequent discriminators
+                y = self.meanpools[i-1](y)
+                y_hat = self.meanpools[i-1](y_hat)
+
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+# -------------------------------------------------------------------
+# 3. Master Wrapper
+#    Combines MPD and MSD into one class to fit your training script.
+# -------------------------------------------------------------------
+
+class SISUDiscriminator(nn.Module):
+    def __init__(self):
+        super(SISUDiscriminator, self).__init__()
+        self.mpd = MultiPeriodDiscriminator()
+        self.msd = MultiScaleDiscriminator()
+
+    def forward(self, y, y_hat):
+        # Return format:
+        # scores_real, scores_fake, features_real, features_fake
+
+        # Run Multi-Period
+        mpd_y_d_rs, mpd_y_d_gs, mpd_fmap_rs, mpd_fmap_gs = self.mpd(y, y_hat)
+
+        # Run Multi-Scale
+        msd_y_d_rs, msd_y_d_gs, msd_fmap_rs, msd_fmap_gs = self.msd(y, y_hat)
+
+        # Combine all results
+        return (
+            mpd_y_d_rs + msd_y_d_rs, # All real scores
+            mpd_y_d_gs + msd_y_d_gs, # All fake scores
+            mpd_fmap_rs + msd_fmap_rs, # All real feature maps
+            mpd_fmap_gs + msd_fmap_gs  # All fake feature maps
+        )

training.py

@@ -3,6 +3,7 @@ import datetime
 import os
 
 import torch
+import torch.nn as nn
 import torch.optim as optim
 import tqdm
 from accelerate import Accelerator
@@ -39,10 +40,13 @@ accelerator = Accelerator(mixed_precision="bf16")
 # Models
 # ---------------------------
 generator = SISUGenerator()
+# Note: SISUDiscriminator is now an Ensemble (MPD + MSD)
 discriminator = SISUDiscriminator()
 
 accelerator.print("🔨 | Compiling models...")
 
+# Torch compile is great, but if you hit errors with the new List/Tuple outputs
+# of the discriminator, you might need to disable it for D.
 generator = torch.compile(generator)
 discriminator = torch.compile(discriminator)
 
@@ -108,21 +112,24 @@ models_dir = "./models"
 os.makedirs(models_dir, exist_ok=True)
 
 
-def save_ckpt(path, epoch):
+def save_ckpt(path, epoch, loss=None, is_best=False):
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
-        accelerator.save(
+        state = {
-            {
             "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(),
+            "schedD": scheduler_d.state_dict()
-            },
+        }
-            path,
+
-        )
+        accelerator.save(state, os.path.join(models_dir, "last.pt"))
+
+        if is_best:
+            accelerator.save(state, os.path.join(models_dir, "best.pt"))
+            accelerator.print(f"🌟 | New best model saved with G Loss: {loss:.4f}")
 
 
 start_epoch = 0
@@ -143,9 +150,8 @@ if args.resume:
     else:
         accelerator.print("⚠️ | Resume requested but no checkpoint found. Starting fresh.")
 
-accelerator.print("🏋️ | Started training...")
 
-smallest_loss = float('inf')
+accelerator.print("🏋️ | Started training...")
 
 try:
     for epoch in range(start_epoch, args.epochs):
@@ -172,7 +178,6 @@ try:
             with accelerator.autocast():
                 d_loss = discriminator_train(
                     high_quality,
-                    low_quality.detach(),
                     discriminator,
                     generator_output.detach()
                 )
@@ -218,7 +223,6 @@ try:
             steps += 1
             progress_bar.set_description(f"Epoch {epoch} | D {discriminator_time}μs | G {generator_time}μs")
 
-        # epoch averages & schedulers
         if steps == 0:
             accelerator.print("🪹 | No steps in epoch (empty dataloader?). Exiting.")
             break
@@ -230,9 +234,6 @@ 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

@@ -2,13 +2,14 @@ import torch
 import torch.nn.functional as F
 from utils.MultiResolutionSTFTLoss import MultiResolutionSTFTLoss
 
-
+# Keep STFT settings as is
 stft_loss_fn = MultiResolutionSTFTLoss(
     fft_sizes=[512, 1024, 2048],
     hop_sizes=[64, 128, 256],
     win_lengths=[256, 512, 1024]
 )
 
+
 def feature_matching_loss(fmap_r, fmap_g):
     """
     Computes L1 distance between real and fake feature maps.
@@ -16,11 +17,9 @@ def feature_matching_loss(fmap_r, fmap_g):
     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
 
 
@@ -33,11 +32,8 @@ def discriminator_loss(disc_real_outputs, disc_generated_outputs):
     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)
@@ -61,16 +57,11 @@ def generator_adv_loss(disc_generated_outputs):
 
 def discriminator_train(
     high_quality,
-    low_quality,
     discriminator,
     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())
 
-    # 2. Calculate Loss (LSGAN)
     d_loss, _, _ = discriminator_loss(y_d_rs, y_d_gs)
 
     return d_loss
@@ -83,25 +74,14 @@ def generator_train(
     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)
 
-    # 2. Adversarial Loss (Try to fool D into thinking G is Real)
     loss_gen_adv = generator_adv_loss(y_d_gs)
 
-    # 3. Feature Matching Loss (Force G to match internal features of D)
     loss_fm = feature_matching_loss(fmap_rs, fmap_gs)
 
-    # 4. Mel-Spectrogram / STFT Loss (Audio Quality)
     stft_loss = stft_loss_fn(high_quality, generator_output)["total"]
 
-    # -----------------------------------------
-    # 5. Combine Losses
-    # -----------------------------------------
-    # Recommended weights for HiFi-GAN/EnCodec style architectures:
-    # STFT is dominant (45), FM provides stability (2), Adv provides texture (1)
-
     lambda_stft = 45.0
     lambda_fm = 2.0
     lambda_adv = 1.0