Merge pull request 'Merge new-arch, because it has proven to give the best results' (#1) from new-arch into main

Reviewed-on: #1

data.py

@@ -1,104 +1,53 @@
-# Keep necessary PyTorch imports for torchaudio and Dataset structure
 from torch.utils.data import Dataset
+import torch.nn.functional as F
 import torch
 import torchaudio
-import torchaudio.transforms as T # Keep using torchaudio transforms
-
-# Import NumPy
-import numpy as np
-
 import os
 import random
-# Assume AudioUtils is available and works on PyTorch Tensors as before
+import torchaudio.transforms as T
 import AudioUtils
 
-class AudioDatasetNumPy(Dataset): # Renamed slightly for clarity
+class AudioDataset(Dataset):
     audio_sample_rates = [11025]
     MAX_LENGTH = 44100  # Define your desired maximum length here
 
-    def __init__(self, input_dir):
+    def __init__(self, input_dir, device):
-        """
+        self.input_files = [os.path.join(root, f) for root, _, files in os.walk(input_dir) for f in files if f.endswith('.wav')]
-        Initializes the dataset. Device argument is removed.
+        self.device = device
-        """
-        self.input_files = [
-            os.path.join(root, f)
-            for root, _, files in os.walk(input_dir)
-            for f in files if f.endswith('.wav')
-        ]
-        if not self.input_files:
-            print(f"Warning: No .wav files found in {input_dir}")
 
     def __len__(self):
         return len(self.input_files)
 
     def __getitem__(self, idx):
-        """
+        # Load high-quality audio
-        Loads audio, processes it, and returns NumPy arrays.
+        high_quality_audio, original_sample_rate = torchaudio.load(self.input_files[idx], normalize=True)
-        """
-        # --- Load and Resample using torchaudio (produces PyTorch tensors) ---
-        try:
-            high_quality_audio_pt, original_sample_rate = torchaudio.load(
-                self.input_files[idx], normalize=True
-            )
-        except Exception as e:
-            print(f"Error loading file {self.input_files[idx]}: {e}")
-            # Return None or raise error, or return dummy data if preferred
-            # Returning dummy data might hide issues
-            return None # Or handle appropriately
 
         # Generate low-quality audio with random downsampling
         mangled_sample_rate = random.choice(self.audio_sample_rates)
+        resample_transform_low = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
+        low_quality_audio = resample_transform_low(high_quality_audio)
 
-        # Ensure sample rates are different before resampling
+        resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
-        if original_sample_rate != mangled_sample_rate:
+        low_quality_audio = resample_transform_high(low_quality_audio)
-            resample_transform_low = T.Resample(original_sample_rate, mangled_sample_rate)
-            low_quality_audio_pt = resample_transform_low(high_quality_audio_pt)
 
-            resample_transform_high = T.Resample(mangled_sample_rate, original_sample_rate)
+        high_quality_audio = AudioUtils.stereo_tensor_to_mono(high_quality_audio)
-            low_quality_audio_pt = resample_transform_high(low_quality_audio_pt)
+        low_quality_audio = AudioUtils.stereo_tensor_to_mono(low_quality_audio)
-        else:
-            # If rates match, just copy the tensor
-            low_quality_audio_pt = high_quality_audio_pt.clone()
 
+        # Pad or truncate high-quality audio
+        if high_quality_audio.shape[1] < self.MAX_LENGTH:
+            padding = self.MAX_LENGTH - high_quality_audio.shape[1]
+            high_quality_audio = F.pad(high_quality_audio, (0, padding))
+        elif high_quality_audio.shape[1] > self.MAX_LENGTH:
+            high_quality_audio = high_quality_audio[:, :self.MAX_LENGTH]
 
-        # --- Process Stereo to Mono (still using PyTorch tensors) ---
+        # Pad or truncate low-quality audio
-        # Assuming AudioUtils.stereo_tensor_to_mono expects PyTorch Tensor (C, L)
+        if low_quality_audio.shape[1] < self.MAX_LENGTH:
-        # and returns PyTorch Tensor (1, L)
+            padding = self.MAX_LENGTH - low_quality_audio.shape[1]
-        try:
+            low_quality_audio = F.pad(low_quality_audio, (0, padding))
-            high_quality_audio_pt_mono = AudioUtils.stereo_tensor_to_mono(high_quality_audio_pt)
+        elif low_quality_audio.shape[1] > self.MAX_LENGTH:
-            low_quality_audio_pt_mono = AudioUtils.stereo_tensor_to_mono(low_quality_audio_pt)
+            low_quality_audio = low_quality_audio[:, :self.MAX_LENGTH]
-        except Exception as e:
-            # Handle cases where mono conversion might fail (e.g., already mono)
-            # This depends on how AudioUtils is implemented. Let's assume it handles it.
-             print(f"Warning: Mono conversion issue with {self.input_files[idx]}: {e}. Using original.")
-             high_quality_audio_pt_mono = high_quality_audio_pt if high_quality_audio_pt.shape[0] == 1 else torch.mean(high_quality_audio_pt, dim=0, keepdim=True)
-             low_quality_audio_pt_mono = low_quality_audio_pt if low_quality_audio_pt.shape[0] == 1 else torch.mean(low_quality_audio_pt, dim=0, keepdim=True)
 
+        high_quality_audio = high_quality_audio.to(self.device)
+        low_quality_audio = low_quality_audio.to(self.device)
 
-        # --- Convert to NumPy Arrays ---
+        return (high_quality_audio, original_sample_rate), (low_quality_audio, mangled_sample_rate)
-        high_quality_audio_np = high_quality_audio_pt_mono.numpy() # Shape (1, L)
-        low_quality_audio_np = low_quality_audio_pt_mono.numpy()   # Shape (1, L)
-
-
-        # --- Pad or Truncate using NumPy ---
-        def process_numpy_audio(audio_np, max_len):
-            current_len = audio_np.shape[1] # Length is axis 1 for shape (1, L)
-            if current_len < max_len:
-                padding_needed = max_len - current_len
-                # np.pad format: ((pad_before_ax0, pad_after_ax0), (pad_before_ax1, pad_after_ax1), ...)
-                # We only pad axis 1 (length) at the end
-                audio_np = np.pad(audio_np, ((0, 0), (0, padding_needed)), mode='constant', constant_values=0)
-            elif current_len > max_len:
-                # Truncate axis 1 (length)
-                audio_np = audio_np[:, :max_len]
-            return audio_np
-
-        high_quality_audio_np = process_numpy_audio(high_quality_audio_np, self.MAX_LENGTH)
-        low_quality_audio_np = process_numpy_audio(low_quality_audio_np, self.MAX_LENGTH)
-
-        # --- Remove Device Handling ---
-        # .to(self.device) is removed.
-
-        # --- Return NumPy arrays and metadata ---
-        # Note: Arrays likely have shape (1, MAX_LENGTH) here
-        return (high_quality_audio_np, original_sample_rate), (low_quality_audio_np, mangled_sample_rate)

discriminator.py

@@ -1,145 +1,63 @@
-import jax
+import torch
-import jax.numpy as jnp
+import torch.nn as nn
-from flax import linen as nn
+import torch.nn.utils as utils
-from typing import Sequence, Tuple
 
-# Assume InstanceNorm1d and AttentionBlock are defined as in the generator conversion
+def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, spectral_norm=True, use_instance_norm=True):
-# --- Custom InstanceNorm1d Implementation (from Generator) ---
+    padding = (kernel_size // 2) * dilation
-class InstanceNorm1d(nn.Module):
+    conv_layer = nn.Conv1d(
-    features: int
+        in_channels,
-    epsilon: float = 1e-5
+        out_channels,
-    use_scale: bool = True
+        kernel_size=kernel_size,
-    use_bias: bool = True
+        stride=stride,
-    @nn.compact
+        dilation=dilation,
-    def __call__(self, x):
+        padding=padding
-        if x.shape[-1] != self.features:
+    )
-             raise ValueError(f"Input features {x.shape[-1]} does not match InstanceNorm1d features {self.features}")
+
-        mean = jnp.mean(x, axis=1, keepdims=True)
+    if spectral_norm:
-        var = jnp.var(x, axis=1, keepdims=True)
+        conv_layer = utils.spectral_norm(conv_layer)
-        normalized = (x - mean) / jnp.sqrt(var + self.epsilon)
+
-        if self.use_scale:
+    layers = [conv_layer]
-            scale = self.param('scale', nn.initializers.ones, (self.features,))
+    layers.append(nn.LeakyReLU(0.2, inplace=True))
-            normalized *= scale
+
-        if self.use_bias:
+    if use_instance_norm:
-            bias = self.param('bias', nn.initializers.zeros, (self.features,))
+        layers.append(nn.InstanceNorm1d(out_channels))
-            normalized += bias
+
-        return normalized
+    return nn.Sequential(*layers)
 
-# --- AttentionBlock Implementation (from Generator) ---
 class AttentionBlock(nn.Module):
-    channels: int
+    def __init__(self, channels):
-    @nn.compact
+        super(AttentionBlock, self).__init__()
-    def __call__(self, x):
+        self.attention = nn.Sequential(
-        ks1 = (1,)
+            nn.Conv1d(channels, channels // 4, kernel_size=1),
-        attention_weights = nn.Conv(features=self.channels // 4, kernel_size=ks1, padding='SAME')(x)
+            nn.ReLU(inplace=True),
-        attention_weights = nn.relu(attention_weights)
+            nn.Conv1d(channels // 4, channels, kernel_size=1),
-        attention_weights = nn.Conv(features=self.channels, kernel_size=ks1, padding='SAME')(attention_weights)
+            nn.Sigmoid()
-        attention_weights = nn.sigmoid(attention_weights)
+        )
+
+    def forward(self, x):
+        attention_weights = self.attention(x)
         return x * attention_weights
 
-# --- Converted Discriminator Modules ---
-
-class DiscriminatorBlock(nn.Module):
-    """Equivalent of the PyTorch discriminator_block function."""
-    in_channels: int # Needed for clarity, though not strictly used by layers if input shape is known
-    out_channels: int
-    kernel_size: int = 3
-    stride: int = 1
-    dilation: int = 1
-    # spectral_norm: bool = True # Flag for where SN would be applied
-    use_instance_norm: bool = True
-    negative_slope: float = 0.2
-
-    @nn.compact
-    def __call__(self, x):
-        """
-        Args:
-            x: Input tensor (N, L, C_in)
-        Returns:
-            Output tensor (N, L', C_out) - L' depends on stride/padding
-        """
-        # Flax Conv expects kernel_size, stride, dilation as sequences (tuples)
-        ks = (self.kernel_size,)
-        st = (self.stride,)
-        di = (self.dilation,)
-
-        # Padding='SAME' works reasonably well for stride=1 and stride=2 downsampling
-        # NOTE: Spectral Norm is omitted here.
-        # If implementing, you'd wrap or replace nn.Conv with a spectral-normalized version.
-        # conv_layer = SpectralNormConv1D(...) or wrap(nn.Conv(...))
-        y = nn.Conv(
-            features=self.out_channels,
-            kernel_size=ks,
-            strides=st,
-            kernel_dilation=di,
-            padding='SAME' # Often used in GANs
-        )(x)
-
-        # Apply LeakyReLU first (as in the original code if IN is used)
-        y = nn.leaky_relu(y, negative_slope=self.negative_slope)
-
-        # Conditionally apply InstanceNorm
-        if self.use_instance_norm:
-            y = InstanceNorm1d(features=self.out_channels)(y)
-
-        return y
-
 class SISUDiscriminator(nn.Module):
-    """SISUDiscriminator model translated to Flax."""
+    def __init__(self, base_channels=16):
-    base_channels: int = 16
+        super(SISUDiscriminator, self).__init__()
+        layers = base_channels
+        self.model = nn.Sequential(
+            discriminator_block(1, layers, kernel_size=7, stride=1, spectral_norm=True, use_instance_norm=False),
+            discriminator_block(layers, layers * 2, kernel_size=5, stride=2, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(layers * 2, layers * 4, kernel_size=5, stride=1, dilation=2, spectral_norm=True, use_instance_norm=True),
+            AttentionBlock(layers * 4),
+            discriminator_block(layers * 4, layers * 8, kernel_size=5, stride=1, dilation=4, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(layers * 8, layers * 4, kernel_size=5, stride=2, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(layers * 4, layers * 2, kernel_size=3, stride=1, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(layers * 2, layers, kernel_size=3, stride=1, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(layers, 1, kernel_size=3, stride=1, spectral_norm=False, use_instance_norm=False)
+        )
 
-    @nn.compact
+        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
-    def __call__(self, x):
-        """
-        Args:
-            x: Input tensor (N, L, 1) - assumes single channel input
-        Returns:
-            Output tensor (N, 1) - logits
-        """
-        if x.shape[-1] != 1:
-            raise ValueError(f"Input should have 1 channel (NLC format), got shape {x.shape}")
 
-        ch = self.base_channels
+    def forward(self, x):
-
+        x = self.model(x)
-        # Block 1: 1 -> ch, k=7, s=1, d=1, SN=T, IN=F
+        x = self.global_avg_pool(x)
-        # NOTE: Spectral Norm omitted
+        x = x.view(x.size(0), -1)
-        y = DiscriminatorBlock(in_channels=1, out_channels=ch, kernel_size=7, stride=1, use_instance_norm=False)(x)
+        return x
-
-        # Block 2: ch -> ch*2, k=5, s=2, d=1, SN=T, IN=T
-        # NOTE: Spectral Norm omitted
-        y = DiscriminatorBlock(in_channels=ch, out_channels=ch*2, kernel_size=5, stride=2, use_instance_norm=True)(y)
-
-        # Block 3: ch*2 -> ch*4, k=5, s=1, d=2, SN=T, IN=T
-        # NOTE: Spectral Norm omitted
-        y = DiscriminatorBlock(in_channels=ch*2, out_channels=ch*4, kernel_size=5, stride=1, dilation=2, use_instance_norm=True)(y)
-
-        # Attention Block
-        y = AttentionBlock(channels=ch*4)(y)
-
-        # Block 4: ch*4 -> ch*8, k=5, s=1, d=4, SN=T, IN=T
-        # NOTE: Spectral Norm omitted
-        y = DiscriminatorBlock(in_channels=ch*4, out_channels=ch*8, kernel_size=5, stride=1, dilation=4, use_instance_norm=True)(y)
-
-        # Block 5: ch*8 -> ch*4, k=5, s=2, d=1, SN=T, IN=T
-        # NOTE: Spectral Norm omitted
-        y = DiscriminatorBlock(in_channels=ch*8, out_channels=ch*4, kernel_size=5, stride=2, use_instance_norm=True)(y)
-
-        # Block 6: ch*4 -> ch*2, k=3, s=1, d=1, SN=T, IN=T
-        # NOTE: Spectral Norm omitted
-        y = DiscriminatorBlock(in_channels=ch*4, out_channels=ch*2, kernel_size=3, stride=1, use_instance_norm=True)(y)
-
-        # Block 7: ch*2 -> ch, k=3, s=1, d=1, SN=T, IN=T
-        # NOTE: Spectral Norm omitted
-        y = DiscriminatorBlock(in_channels=ch*2, out_channels=ch, kernel_size=3, stride=1, use_instance_norm=True)(y)
-
-        # Block 8: ch -> 1, k=3, s=1, d=1, SN=F, IN=F
-        # NOTE: Spectral Norm omitted (as per original config)
-        y = DiscriminatorBlock(in_channels=ch, out_channels=1, kernel_size=3, stride=1, use_instance_norm=False)(y)
-
-        # Global Average Pooling (across Length dimension)
-        pooled = jnp.mean(y, axis=1) # Shape becomes (N, C=1)
-
-        # Flatten (optional, as shape is likely already (N, 1))
-        output = jnp.reshape(pooled, (pooled.shape[0], -1)) # Shape (N, 1)
-
-        return output

generator.py

@@ -1,173 +1,74 @@
-import jax
+import torch
-import jax.numpy as jnp
+import torch.nn as nn
-from flax import linen as nn
-from typing import Sequence, Tuple
 
-# --- Custom InstanceNorm1d Implementation ---
+def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
-class InstanceNorm1d(nn.Module):
+    return nn.Sequential(
-    """
+        nn.Conv1d(
-    Flax implementation of Instance Normalization for 1D data (NLC format).
+            in_channels,
-    Normalizes across the 'L' dimension.
+            out_channels,
-    """
+            kernel_size=kernel_size,
-    features: int
+            dilation=dilation,
-    epsilon: float = 1e-5
+            padding=(kernel_size // 2) * dilation
-    use_scale: bool = True
+        ),
-    use_bias: bool = True
+        nn.InstanceNorm1d(out_channels),
-
+        nn.PReLU()
-    @nn.compact
+    )
-    def __call__(self, x):
-        """
-        Args:
-            x: Input tensor of shape (batch, length, features)
-
-        Returns:
-            Normalized tensor.
-        """
-        if x.shape[-1] != self.features:
-             raise ValueError(f"Input features {x.shape[-1]} does not match InstanceNorm1d features {self.features}")
-
-        # Calculate mean and variance across the length dimension (axis=1)
-        # Keep dims for broadcasting
-        mean = jnp.mean(x, axis=1, keepdims=True)
-        # Variance calculation using mean needs care for numerical stability if needed,
-        # but jnp.var should handle it.
-        var = jnp.var(x, axis=1, keepdims=True)
-
-        # Normalize
-        normalized = (x - mean) / jnp.sqrt(var + self.epsilon)
-
-        # Apply learnable scale and bias if enabled
-        if self.use_scale:
-            # Parameter shape: (features,) to broadcast across N and L
-            scale = self.param('scale', nn.initializers.ones, (self.features,))
-            normalized *= scale
-        if self.use_bias:
-            # Parameter shape: (features,)
-            bias = self.param('bias', nn.initializers.zeros, (self.features,))
-            normalized += bias
-
-        return normalized
-
-# --- Converted Modules ---
-
-class ConvBlock(nn.Module):
-    """Equivalent of the PyTorch conv_block function."""
-    out_channels: int
-    kernel_size: int = 3
-    dilation: int = 1
-
-    @nn.compact
-    def __call__(self, x):
-        """
-        Args:
-            x: Input tensor (N, L, C_in)
-        Returns:
-            Output tensor (N, L, C_out)
-        """
-        # Flax Conv expects kernel_size and dilation as sequences (tuples)
-        ks = (self.kernel_size,)
-        di = (self.dilation,)
-
-        # Padding='SAME' attempts to preserve the length dimension for stride=1
-        x = nn.Conv(
-            features=self.out_channels,
-            kernel_size=ks,
-            kernel_dilation=di,
-            padding='SAME'
-        )(x)
-        x = InstanceNorm1d(features=self.out_channels)(x) # Use custom InstanceNorm
-        x = nn.PReLU()(x) # PReLU learns 'alpha' parameter per channel
-        return x
 
 class AttentionBlock(nn.Module):
-    """Simple Channel Attention Block in Flax."""
+    """
-    channels: int
+    Simple Channel Attention Block. Learns to weight channels based on their importance.
-
+    """
-    @nn.compact
+    def __init__(self, channels):
-    def __call__(self, x):
+        super(AttentionBlock, self).__init__()
-        """
+        self.attention = nn.Sequential(
-        Args:
+            nn.Conv1d(channels, channels // 4, kernel_size=1),
-            x: Input tensor (N, L, C)
+            nn.ReLU(inplace=True),
-        Returns:
+            nn.Conv1d(channels // 4, channels, kernel_size=1),
-            Attention-weighted output tensor (N, L, C)
+            nn.Sigmoid()
-        """
+        )
-        # Flax Conv expects kernel_size as a sequence (tuple)
-        ks1 = (1,)
-        attention_weights = nn.Conv(
-            features=self.channels // 4, kernel_size=ks1, padding='SAME'
-        )(x)
-        # NOTE: PyTorch used inplace=True, JAX/Flax don't modify inplace
-        attention_weights = nn.relu(attention_weights)
-        attention_weights = nn.Conv(
-            features=self.channels, kernel_size=ks1, padding='SAME'
-        )(attention_weights)
-        attention_weights = nn.sigmoid(attention_weights)
 
+    def forward(self, x):
+        attention_weights = self.attention(x)
         return x * attention_weights
 
 class ResidualInResidualBlock(nn.Module):
-    """ResidualInResidualBlock in Flax."""
+    def __init__(self, channels, num_convs=3):
-    channels: int
+        super(ResidualInResidualBlock, self).__init__()
-    num_convs: int = 3
 
-    @nn.compact
+        self.conv_layers = nn.Sequential(
-    def __call__(self, x):
+            *[conv_block(channels, channels) for _ in range(num_convs)]
-        """
+        )
-        Args:
+
-            x: Input tensor (N, L, C)
+        self.attention = AttentionBlock(channels)
-        Returns:
+
-            Output tensor (N, L, C)
+    def forward(self, x):
-        """
         residual = x
-        y = x
+        x = self.conv_layers(x)
-        # Sequentially apply ConvBlocks
+        x = self.attention(x)
-        for _ in range(self.num_convs):
+        return x + residual
-            y = ConvBlock(
-                out_channels=self.channels,
-                kernel_size=3, # Assuming kernel_size 3 as in original conv_block default
-                dilation=1    # Assuming dilation 1 as in original conv_block default
-            )(y)
-
-        y = AttentionBlock(channels=self.channels)(y)
-        return y + residual
 
 class SISUGenerator(nn.Module):
-    """SISUGenerator model translated to Flax."""
+    def __init__(self, channels=16, num_rirb=4, alpha=1.0):
-    channels: int = 16
+        super(SISUGenerator, self).__init__()
-    num_rirb: int = 4
+        self.alpha = alpha
-    alpha: float = 1.0 # Non-learnable parameter, passed during init
 
-    @nn.compact
+        self.conv1 = nn.Sequential(
-    def __call__(self, x):
+            nn.Conv1d(1, channels, kernel_size=7, padding=3),
-        """
+            nn.InstanceNorm1d(channels),
-        Args:
+            nn.PReLU(),
-            x: Input tensor (N, L, 1) - assumes single channel input
+        )
-        Returns:
-            Output tensor (N, L, 1)
-        """
-        if x.shape[-1] != 1:
-            raise ValueError(f"Input should have 1 channel (NLC format), got shape {x.shape}")
 
+        self.rir_blocks = nn.Sequential(
+            *[ResidualInResidualBlock(channels) for _ in range(num_rirb)]
+        )
+
+        self.final_layer = nn.Conv1d(channels, 1, kernel_size=3, padding=1)
+
+    def forward(self, x):
         residual_input = x
-
+        x = self.conv1(x)
-        # Initial convolution block
+        x_rirb_out = self.rir_blocks(x)
-        # Flax Conv expects kernel_size as sequence
+        learned_residual = self.final_layer(x_rirb_out)
-        ks7 = (7,)
-        ks3 = (3,)
-        y = nn.Conv(features=self.channels, kernel_size=ks7, padding='SAME')(x)
-        y = InstanceNorm1d(features=self.channels)(y)
-        y = nn.PReLU()(y)
-
-        # Residual-in-Residual Blocks
-        rirb_out = y
-        for _ in range(self.num_rirb):
-            rirb_out = ResidualInResidualBlock(channels=self.channels)(rirb_out)
-
-        # Final layer
-        learned_residual = nn.Conv(
-            features=1, kernel_size=ks3, padding='SAME'
-        )(rirb_out)
-
-        # Combine with input residual
         output = residual_input + self.alpha * learned_residual
+
         return output

training.py

@@ -1,33 +1,46 @@
-import jax
-import jax.numpy as jnp
-import optax
-import tqdm
-import pickle # Using pickle for simplicity to save JAX states
-import os
-import argparse
-# You might need a JAX-compatible library for audio loading/saving or convert to numpy
-import scipy.io.wavfile as wavfile # Example for saving audio
-
 import torch
+import torch.nn as nn
+import torch.optim as optim
+
+import torch.nn.functional as F
+import torchaudio
+import tqdm
+
+import argparse
+
+import math
+
+import os
+
+from torch.utils.data import random_split
 from torch.utils.data import DataLoader
 
-import file_utils as Data
+import AudioUtils
-from data import AudioDatasetNumPy
+from data import AudioDataset
 from generator import SISUGenerator
 from discriminator import SISUDiscriminator
 
+from training_utils import discriminator_train, generator_train
+import file_utils as Data
+
+import torchaudio.transforms as T
+
 # Init script argument parser
 parser = argparse.ArgumentParser(description="Training script")
 parser.add_argument("--generator", type=str, default=None,
                     help="Path to the generator model file")
 parser.add_argument("--discriminator", type=str, default=None,
                     help="Path to the discriminator model file")
-parser.add_argument("--epoch", type=int, default=0, help="Starting epoch for model versioning")
+parser.add_argument("--device", type=str, default="cpu",  help="Select device")
+parser.add_argument("--epoch", type=int, default=0, help="Current epoch for model versioning")
 parser.add_argument("--debug", action="store_true",  help="Print debug logs")
 parser.add_argument("--continue_training", action="store_true", help="Continue training using temp_generator and temp_discriminator models")
 
 args = parser.parse_args()
 
+device = torch.device(args.device if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
 # Parameters
 sample_rate = 44100
 n_fft = 2048
@@ -36,446 +49,146 @@ win_length = n_fft
 n_mels = 128
 n_mfcc = 20 # If using MFCC
 
-debug = args.debug
+mfcc_transform = T.MFCC(
+    sample_rate,
+    n_mfcc,
+    melkwargs = {'n_fft': n_fft, 'hop_length': hop_length}
+).to(device)
 
-# Initialize JAX random key
+mel_transform = T.MelSpectrogram(
-key = jax.random.PRNGKey(0)
+    sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length,
+    win_length=win_length, n_mels=n_mels, power=1.0 # Magnitude Mel
+).to(device)
+
+stft_transform = T.Spectrogram(
+    n_fft=n_fft, win_length=win_length, hop_length=hop_length
+).to(device)
+
+debug = args.debug
 
 # Initialize dataset and dataloader
 dataset_dir = './dataset/good'
-dataset = AudioDatasetNumPy(dataset_dir) # Use your JAX dataset
+dataset = AudioDataset(dataset_dir, device)
-train_data_loader = DataLoader(dataset, batch_size=4, shuffle=True) # Use your JAX DataLoader
-
 models_dir = "models"
 os.makedirs(models_dir, exist_ok=True)
 audio_output_dir = "output"
 os.makedirs(audio_output_dir, exist_ok=True)
 
+# ========= SINGLE =========
+
+train_data_loader = DataLoader(dataset, batch_size=64, shuffle=True)
+
 
 # ========= MODELS =========
 
-try:
+generator = SISUGenerator()
-    # Fetch the first batch
+discriminator = SISUDiscriminator()
-    first_batch = next(iter(train_data_loader))
-    # The batch is a tuple: ((high_quality_audio_np, high_quality_sample_rate), (low_quality_audio_np, low_quality_sample_rate))
-    # We need the high-quality audio NumPy array batch for initialization
-    sample_input_np = first_batch[0][0] # Get the high-quality audio NumPy array batch
-    # Convert the NumPy array batch to a JAX array
-    sample_input_array = jnp.array(sample_input_np)
 
-    # === FIX ===
+epoch: int = args.epoch
-    # Transpose the array from (batch, channels, length) to (batch, length, channels)
+epoch_from_file = Data.read_data(f"{models_dir}/epoch_data.json")
-    # The original shape from DataLoader is likely (batch, channels, length) like (4, 1, 44100)
-    # The generator expects NLC format (batch, length, channels) i.e., (4, 44100, 1)
-    if sample_input_array.ndim == 3 and sample_input_array.shape[1] == 1:
-         sample_input_array = jnp.transpose(sample_input_array, (0, 2, 1)) # Swap axes 1 and 2
 
-    print(sample_input_array.shape) # Should now print (4, 44100, 1)
-    # === END FIX ===
-
-except StopIteration:
-    print("Error: Data loader is empty. Cannot initialize models.")
-    exit() # Exit if no data is available
-
-
-key, init_key_g, init_key_d = jax.random.split(key, 3)
-generator_model = SISUGenerator()
-discriminator_model = SISUDiscriminator()
-
-# Initialize parameters
-generator_params = generator_model.init(init_key_g, sample_input_array)['params']
-discriminator_params = discriminator_model.init(init_key_d, sample_input_array)['params']
-
-# Define apply functions
-generator_apply_fn = generator_model.apply
-discriminator_apply_fn = discriminator_model.apply
-
-
-# Loss functions (JAX equivalents)
-# Optax provides common loss functions. BCEWithLogitsLoss is equivalent to
-# sigmoid_binary_cross_entropy in Optax combined with a sigmoid activation
-# in the model output or handling logits directly. Assuming your discriminator
-# outputs logits, optax.sigmoid_binary_cross_entropy is appropriate.
-criterion_d = optax.sigmoid_binary_cross_entropy
-criterion_l1 = optax.sigmoid_binary_cross_entropy # For Mel, STFT, MFCC losses
-
-# Optimizers (using Optax)
-optimizer_g = optax.adam(learning_rate=0.0001, b1=0.5, b2=0.999)
-optimizer_d = optax.adam(learning_rate=0.0001, b1=0.5, b2=0.999)
-
-# Initialize optimizer states
-generator_opt_state = optimizer_g.init(generator_params)
-discriminator_opt_state = optimizer_d.init(discriminator_params)
-
-# Schedulers - Optax has learning rate schedules. ReduceLROnPlateau
-# is stateful and usually handled outside the jitted training step,
-# or you can implement a custom learning rate schedule in Optax that
-# takes a metric. For simplicity here, we won't directly replicate the
-# PyTorch ReduceLROnPlateau but you could add logic based on losses
-# in the main loop to adjust the learning rate if needed.
-
-
-# Load saved state if continuing training
-start_epoch = args.epoch
 if args.continue_training:
-    try:
+    generator.load_state_dict(torch.load(f"{models_dir}/temp_generator.pt", map_location=device, weights_only=True))
-        with open(f"{models_dir}/temp_generator.pkl", 'rb') as f:
+    discriminator.load_state_dict(torch.load(f"{models_dir}/temp_generator.pt", map_location=device, weights_only=True))
-            loaded_state = pickle.load(f)
+    epoch = epoch_from_file["epoch"] + 1
-            generator_params = loaded_state['params']
+else:
-            generator_opt_state = loaded_state['opt_state']
+    if args.generator is not None:
-        with open(f"{models_dir}/temp_discriminator.pkl", 'rb') as f:
+        generator.load_state_dict(torch.load(args.generator, map_location=device, weights_only=True))
-            loaded_state = pickle.load(f)
+    if args.discriminator is not None:
-            discriminator_params = loaded_state['params']
+        discriminator.load_state_dict(torch.load(args.discriminator, map_location=device, weights_only=True))
-            discriminator_opt_state = loaded_state['opt_state']
-        epoch_data = Data.read_data(f"{models_dir}/epoch_data.json")
-        start_epoch = epoch_data.get("epoch", 0) + 1
-        print(f"Continuing training from epoch {start_epoch}")
-    except FileNotFoundError:
-        print("Continue training requested but temp models not found. Starting from scratch.")
-    except Exception as e:
-        print(f"Error loading temp models: {e}. Starting from scratch.")
 
-if args.generator is not None:
+generator = generator.to(device)
-    try:
+discriminator = discriminator.to(device)
-        with open(args.generator, 'rb') as f:
-             loaded_state = pickle.load(f)
-             generator_params = loaded_state['params']
-        print(f"Loaded generator from {args.generator}")
-    except FileNotFoundError:
-        print(f"Generator model not found at {args.generator}")
 
-if args.discriminator is not None:
+# Loss
-    try:
+criterion_g = nn.BCEWithLogitsLoss()
-        with open(args.discriminator, 'rb') as f:
+criterion_d = nn.BCEWithLogitsLoss()
-            loaded_state = pickle.load(f)
-            discriminator_params = loaded_state['params']
-        print(f"Loaded discriminator from {args.discriminator}")
-    except FileNotFoundError:
-        print(f"Discriminator model not found at {args.discriminator}")
 
+# Optimizers
+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))
 
-# Initialize JAX audio transforms
+# Scheduler
-# mel_transform_fn = MelSpectrogramJAX(
+scheduler_g = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_g, mode='min', factor=0.5, patience=5)
-#     sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length,
+scheduler_d = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_d, mode='min', factor=0.5, patience=5)
-#     win_length=win_length, n_mels=n_mels, power=1.0 # Magnitude Mel
-# )
-
-# stft_transform_fn = SpectrogramJAX(
-#     n_fft=n_fft, win_length=win_length, hop_length=hop_length
-# )
-
-# mfcc_transform_fn = MFCCJAX(
-#     sample_rate,
-#     n_mfcc,
-#     melkwargs = {'n_fft': n_fft, 'hop_length': hop_length}
-# )
-
-
-# ========= JAX TRAINING STEPS =========
-
-@jax.jit
-def discriminator_train_step(
-    discriminator_params,
-    generator_params,
-    discriminator_opt_state,
-    high_quality_audio, # JAX array (batch, length, channels)
-    low_quality_audio, # JAX array (batch, length, channels)
-    real_labels, # JAX array
-    fake_labels, # JAX array
-    discriminator_apply_fn,
-    generator_apply_fn,
-    discriminator_optimizer,
-    criterion_d,
-    key # JAX random key
-):
-    # Split key for potential randomness in model application
-    key, disc_key, gen_key = jax.random.split(key, 3)
-
-    def loss_fn(d_params):
-        # Generate fake audio
-        # Note: Generator is not being trained in this step, so its parameters are static
-        # Ensure low_quality_audio is in the expected NLC format (batch, length, channels)
-        if low_quality_audio.ndim == 2: # Assuming (batch, length), add channel dim
-             low_quality_audio = jnp.expand_dims(low_quality_audio, axis=-1)
-        elif low_quality_audio.ndim == 1: # Assuming (length), add batch and channel dims
-             low_quality_audio = jnp.expand_dims(jnp.expand_dims(low_quality_audio, axis=0), axis=-1)
-
-
-        enhanced_audio, _ = generator_apply_fn({'params': generator_params}, gen_key, low_quality_audio)
-
-        # Pass data through the discriminator
-        # Ensure enhanced_audio has a leading dimension if not already present (e.g., batch size)
-        if enhanced_audio.ndim == 2: # Assuming (length, channel), add batch dim
-            enhanced_audio = jnp.expand_dims(enhanced_audio, axis=0)
-        elif enhanced_audio.ndim == 1: # Assuming (length), add batch and channel dims
-             enhanced_audio = jnp.expand_dims(jnp.expand_dims(enhanced_audio, axis=0), axis=-1)
-
-
-        # Ensure high_quality_audio is in the expected NLC format (batch, length, channels)
-        if high_quality_audio.ndim == 2: # Assuming (batch, length), add channel dim
-             high_quality_audio = jnp.expand_dims(high_quality_audio, axis=-1)
-        elif high_quality_audio.ndim == 1: # Assuming (length), add batch and channel dims
-             high_quality_audio = jnp.expand_dims(jnp.expand_dims(high_quality_audio, axis=0), axis=-1)
-
-
-        real_output = discriminator_apply_fn({'params': d_params}, disc_key, high_quality_audio)
-        fake_output = discriminator_apply_fn({'params': d_params}, disc_key, enhanced_audio)
-
-        # Calculate loss (criterion_d is assumed to be Optax's sigmoid_binary_cross_entropy or similar)
-        # Ensure the shapes match the labels (batch_size, 1)
-        real_output = real_output.reshape(-1, 1)
-        fake_output = fake_output.reshape(-1, 1)
-
-        real_loss = jnp.mean(criterion_d(real_output, real_labels))
-        fake_loss = jnp.mean(criterion_d(fake_output, fake_labels))
-        total_loss = real_loss + fake_loss
-        return total_loss, (real_loss, fake_loss)
-
-    # Compute gradients
-    # Use jax.value_and_grad to get both the loss value and the gradients
-    (loss, (real_loss, fake_loss)), grads = jax.value_and_grad(loss_fn, has_aux=True)(discriminator_params)
-
-    # Apply updates
-    updates, new_discriminator_opt_state = discriminator_optimizer.update(grads, discriminator_opt_state, discriminator_params)
-    new_discriminator_params = optax.apply_updates(discriminator_params, updates)
-
-    return new_discriminator_params, new_discriminator_opt_state, loss, key
-
-
-@jax.jit
-def generator_train_step(
-    generator_params,
-    discriminator_params,
-    generator_opt_state,
-    low_quality_audio, # JAX array (batch, length, channels)
-    high_quality_audio, # JAX array (batch, length, channels)
-    real_labels, # JAX array
-    generator_apply_fn,
-    discriminator_apply_fn,
-    generator_optimizer,
-    criterion_d, # Adversarial loss
-    criterion_l1, # Feature matching loss
-    key # JAX random key
-):
-    # Split key for potential randomness
-    key, gen_key, disc_key = jax.random.split(key, 3)
-
-    def loss_fn(g_params):
-        # Ensure low_quality_audio is in the expected NLC format (batch, length, channels)
-        if low_quality_audio.ndim == 2: # Assuming (batch, length), add channel dim
-             low_quality_audio = jnp.expand_dims(low_quality_audio, axis=-1)
-        elif low_quality_audio.ndim == 1: # Assuming (length), add batch and channel dims
-             low_quality_audio = jnp.expand_dims(jnp.expand_dims(low_quality_audio, axis=0), axis=-1)
-
-        # Generate enhanced audio
-        enhanced_audio, _ = generator_apply_fn({'params': g_params}, gen_key, low_quality_audio)
-
-        # Ensure enhanced_audio has a leading dimension if not already present
-        if enhanced_audio.ndim == 2: # Assuming (length, channel), add batch dim
-            enhanced_audio = jnp.expand_dims(enhanced_audio, axis=0)
-        elif enhanced_audio.ndim == 1: # Assuming (length), add batch and channel dims
-             enhanced_audio = jnp.expand_dims(jnp.expand_dims(enhanced_audio, axis=0), axis=-1)
-
-
-        # Calculate adversarial loss (generator wants discriminator to think fake is real)
-        # Note: Discriminator is not being trained in this step, so its parameters are static
-        fake_output = discriminator_apply_fn({'params': discriminator_params}, disc_key, enhanced_audio)
-        # Ensure the shape matches the labels (batch_size, 1)
-        fake_output = fake_output.reshape(-1, 1)
-        adversarial_loss = jnp.mean(criterion_d(fake_output, real_labels)) # Generator wants fake_output to be close to real_labels (1s)
-
-        # Feature matching losses (assuming you add these back later)
-        # You would need to implement JAX versions of your audio transforms
-        # mel_loss = criterion_l1(mel_transform_fn(enhanced_audio), mel_transform_fn(high_quality_audio))
-        # stft_loss = criterion_l1(stft_transform_fn(enhanced_audio), stft_transform_fn(high_quality_audio))
-        # mfcc_loss = criterion_l1(mfcc_transform_fn(enhanced_audio), mfcc_transform_fn(high_quality_audio))
-
-        # combined_loss = adversarial_loss + mel_loss + stft_loss + mfcc_loss
-        combined_loss = adversarial_loss # For now, only adversarial loss
-
-        # Return combined_loss and any other metrics needed for logging/analysis
-        # For now, just adversarial loss and enhanced_audio
-        return combined_loss, (adversarial_loss, enhanced_audio) # Add other losses here when implemented
-
-    # Compute gradients
-    # Update: loss_fn now returns (loss, (aux1, aux2, ...))
-    (loss, (adversarial_loss_val, enhanced_audio)), grads = jax.value_and_grad(loss_fn, has_aux=True)(generator_params)
-
-    # Apply updates
-    updates, new_generator_opt_state = generator_optimizer.update(grads, generator_opt_state, generator_params)
-    new_generator_params = optax.apply_updates(generator_params, updates)
-
-    # Return the loss components separately along with the enhanced audio and key
-    return new_generator_params, new_generator_opt_state, loss, adversarial_loss_val, enhanced_audio, key
-
-
-# ========= MAIN TRAINING LOOP =========
 
 def start_training():
-    global generator_params, discriminator_params, generator_opt_state, discriminator_opt_state, key
     generator_epochs = 5000
-
     for generator_epoch in range(generator_epochs):
-        current_epoch = start_epoch + generator_epoch
+        low_quality_audio = (torch.empty((1)), 1)
+        high_quality_audio = (torch.empty((1)), 1)
+        ai_enhanced_audio = (torch.empty((1)), 1)
 
-        # These will hold the last processed audio examples from a batch for saving
+        times_correct = 0
-        last_high_quality_audio = None
-        last_low_quality_audio = None
-        last_ai_enhanced_audio = None
-        last_sample_rate = None
 
+        # ========= TRAINING =========
+        for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Training epoch {generator_epoch+1}/{generator_epochs}, Current epoch {epoch+1}"):
+        # for high_quality_clip, low_quality_clip in train_data_loader:
+            high_quality_sample = (high_quality_clip[0], high_quality_clip[1])
+            low_quality_sample = (low_quality_clip[0], low_quality_clip[1])
 
-        # Use tqdm for progress bar
+            # ========= LABELS =========
-        for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Training epoch {generator_epoch+1}/{generator_epochs}, Current epoch {current_epoch}"):
+            batch_size = high_quality_clip[0].size(0)
-
+            real_labels = torch.ones(batch_size, 1).to(device)
-            # high_quality_clip and low_quality_clip are tuples: (audio_array, sample_rate_array)
+            fake_labels = torch.zeros(batch_size, 1).to(device)
-            # Extract audio arrays and sample rates (assuming batch dimension is first)
-            # The arrays are NumPy arrays at this point, likely in (batch, channels, length) format
-            high_quality_audio_batch_np = high_quality_clip[0]
-            low_quality_audio_batch_np = low_quality_clip[0]
-            sample_rate_batch_np = high_quality_clip[1] # Assuming sample rates are the same for paired clips
-
-            # Convert NumPy arrays to JAX arrays and transpose to NLC format (batch, length, channels)
-            # Only transpose if the shape is (batch, channels, length)
-            if high_quality_audio_batch_np.ndim == 3 and high_quality_audio_batch_np.shape[1] == 1:
-                 high_quality_audio_batch = jnp.transpose(jnp.array(high_quality_audio_batch_np), (0, 2, 1))
-            else:
-                 high_quality_audio_batch = jnp.array(high_quality_audio_batch_np) # Assume already NLC or handle other cases
-
-            if low_quality_audio_batch_np.ndim == 3 and low_quality_audio_batch_np.shape[1] == 1:
-                 low_quality_audio_batch = jnp.transpose(jnp.array(low_quality_audio_batch_np), (0, 2, 1))
-            else:
-                 low_quality_audio_batch = jnp.array(low_quality_audio_batch_np) # Assume already NLC or handle other cases
-
-            sample_rate_batch = jnp.array(sample_rate_batch_np)
-
-
-            batch_size = high_quality_audio_batch.shape[0]
-            # Create labels - JAX arrays
-            real_labels = jnp.ones((batch_size, 1))
-            fake_labels = jnp.zeros((batch_size, 1))
-
-            # Split key for each batch
-            key, batch_key = jax.random.split(key)
 
             # ========= DISCRIMINATOR =========
-            # Call the jitted discriminator training step
+            discriminator.train()
-            discriminator_params, discriminator_opt_state, d_loss, batch_key = discriminator_train_step(
+            d_loss = discriminator_train(
-                discriminator_params,
+                high_quality_sample,
-                generator_params,
+                low_quality_sample,
-                discriminator_opt_state,
-                high_quality_audio_batch,
-                low_quality_audio_batch,
                 real_labels,
                 fake_labels,
-                discriminator_apply_fn,
+                discriminator,
-                generator_apply_fn,
+                generator,
-                optimizer_d,
                 criterion_d,
-                batch_key
+                optimizer_d
             )
 
             # ========= GENERATOR =========
-            # Call the jitted generator training step
+            generator.train()
-            generator_params, generator_opt_state, combined_loss, adversarial_loss, enhanced_audio_batch, batch_key = generator_train_step(
+            generator_output, combined_loss, adversarial_loss, mel_l1_tensor, log_stft_l1_tensor, mfcc_l_tensor = generator_train(
-                generator_params,
+                low_quality_sample,
-                discriminator_params,
+                high_quality_sample,
-                generator_opt_state,
+                real_labels,
-                low_quality_audio_batch,
+                generator,
-                high_quality_audio_batch,
+                discriminator,
-                real_labels, # Generator tries to make fake data look real
-                generator_apply_fn,
-                discriminator_apply_fn,
-                optimizer_g,
                 criterion_d,
-                criterion_l1,
+                optimizer_g,
-                batch_key
+                device,
+                mel_transform,
+                stft_transform,
+                mfcc_transform
             )
 
-            # Print debug logs (requires waiting for JIT compilation on first step)
             if debug:
-                # Use .block_until_ready() to ensure computation is finished before printing
+                print(f"D_LOSS: {d_loss.item():.4f}, COMBINED_LOSS: {combined_loss.item():.4f}, ADVERSARIAL_LOSS: {adversarial_loss.item():.4f}, MEL_L1_LOSS: {mel_l1_tensor.item():.4f}, LOG_STFT_L1_LOSS: {log_stft_l1_tensor.item():.4f}, MFCC_LOSS: {mfcc_l_tensor.item():.4f}")
-                # In a real scenario, you might want to log metrics less frequently
+            scheduler_d.step(d_loss.detach())
-                d_loss_val = d_loss.block_until_ready().item()
+            scheduler_g.step(adversarial_loss.detach())
-                combined_loss_val = combined_loss.block_until_ready().item()
-                adversarial_loss_val = adversarial_loss.block_until_ready().item()
-                # Assuming other losses are returned by generator_train_step and unpacked
-                # mel_loss_val = mel_l1_tensor.block_until_ready().item() if mel_l1_tensor is not None else 0
-                # stft_loss_val = log_stft_l1_tensor.block_until_ready().item() if log_stft_l1_tensor is not None else 0
-                # mfcc_loss_val = mfcc_l_tensor.block_until_ready().item() if mfcc_l_tensor is not None else 0
-                print(f"D_LOSS: {d_loss_val:.4f}, G_COMBINED_LOSS: {combined_loss_val:.4f}, G_ADVERSARIAL_LOSS: {adversarial_loss_val:.4f}")
-                # Print other losses here when implemented and returned
 
-            # Schedulers - Implement your learning rate scheduling logic here if needed
+            # ========= SAVE LATEST AUDIO =========
-            # based on the losses (e.g., reducing learning rate if loss plateaus).
+            high_quality_audio = (high_quality_clip[0][0], high_quality_clip[1][0])
-            # This logic would typically live outside the jitted step function.
+            low_quality_audio = (low_quality_clip[0][0], low_quality_clip[1][0])
-            # For Optax, you might use a schedule within the optimizer definition
+            ai_enhanced_audio = (generator_output[0], high_quality_clip[1][0])
-            # or update the learning rate of the optimizer manually.
+
+        new_epoch = generator_epoch+epoch
+
+        if generator_epoch % 25 == 0:
+            print(f"Saved epoch {new_epoch}!")
+            torchaudio.save(f"{audio_output_dir}/epoch-{new_epoch}-audio-crap.wav", low_quality_audio[0].cpu().detach(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
+            torchaudio.save(f"{audio_output_dir}/epoch-{new_epoch}-audio-ai.wav", ai_enhanced_audio[0].cpu().detach(), ai_enhanced_audio[1])
+            torchaudio.save(f"{audio_output_dir}/epoch-{new_epoch}-audio-orig.wav", high_quality_audio[0].cpu().detach(), high_quality_audio[1])
+
+        #if debug:
+        #    print(generator.state_dict().keys())
+        #    print(discriminator.state_dict().keys())
+        torch.save(discriminator.state_dict(), f"{models_dir}/temp_discriminator.pt")
+        torch.save(generator.state_dict(), f"{models_dir}/temp_generator.pt")
+        Data.write_data(f"{models_dir}/epoch_data.json", {"epoch": new_epoch})
 
 
-            # ========= SAVE LATEST AUDIO (from the last batch processed) =========
+    torch.save(discriminator, "models/epoch-5000-discriminator.pt")
-            # Access the first sample of the batch for saving
+    torch.save(generator, "models/epoch-5000-generator.pt")
-            # Ensure enhanced_audio_batch has a batch dimension and is in NLC format
+    print("Training complete!")
-            if enhanced_audio_batch.ndim == 2: # Assuming (length, channel), add batch dim
-                enhanced_audio_batch = jnp.expand_dims(enhanced_audio_batch, axis=0)
-            elif enhanced_audio_batch.ndim == 1: # Assuming (length), add batch and channel dims
-                 enhanced_audio_batch = jnp.expand_dims(jnp.expand_dims(enhanced_audio_batch, axis=0), axis=-1)
-
-
-            last_high_quality_audio = high_quality_audio_batch[0]
-            last_low_quality_audio = low_quality_audio_batch[0]
-            last_ai_enhanced_audio = enhanced_audio_batch[0]
-            last_sample_rate = sample_rate_batch[0].item() # Assuming sample rate is scalar per batch item
-
-
-        # Save audio files periodically (outside the batch loop)
-        if generator_epoch % 25 == 0 and last_high_quality_audio is not None:
-            print(f"Saving audio for epoch {current_epoch}!")
-            try:
-                # Convert JAX arrays to NumPy arrays for saving
-                # Transpose back to (length, channels) or (length) if needed by wavfile.write
-                # Assuming the models output (length, 1) or (length) after removing batch dim
-                low_quality_audio_np_save = jax.device_get(last_low_quality_audio)
-                ai_enhanced_audio_np_save = jax.device_get(last_ai_enhanced_audio)
-                high_quality_audio_np_save = jax.device_get(last_high_quality_audio)
-
-                # Remove the channel dimension if it's 1 for saving with wavfile
-                if low_quality_audio_np_save.shape[-1] == 1:
-                    low_quality_audio_np_save = low_quality_audio_np_save.squeeze(axis=-1)
-                if ai_enhanced_audio_np_save.shape[-1] == 1:
-                    ai_enhanced_audio_np_save = ai_enhanced_audio_np_save.squeeze(axis=-1)
-                if high_quality_audio_np_save.shape[-1] == 1:
-                    high_quality_audio_np_save = high_quality_audio_np_save.squeeze(axis=-1)
-
-
-                wavfile.write(f"{audio_output_dir}/epoch-{current_epoch}-audio-crap.wav", last_sample_rate, low_quality_audio_np_save.astype(jnp.int16)) # Assuming audio is int16
-                wavfile.write(f"{audio_output_dir}/epoch-{current_epoch}-audio-ai.wav", last_sample_rate, ai_enhanced_audio_np_save.astype(jnp.int16)) # Assuming audio is int16
-                wavfile.write(f"{audio_output_dir}/epoch-{current_epoch}-audio-orig.wav", last_sample_rate, high_quality_audio_np_save.astype(jnp.int16)) # Assuming audio is int16
-            except Exception as e:
-                print(f"Error saving audio files: {e}")
-
-
-        # Save model states periodically (outside the batch loop)
-        # Use pickle to save parameters and optimizer states
-        try:
-            with open(f"{models_dir}/temp_discriminator.pkl", 'wb') as f:
-                pickle.dump({'params': jax.device_get(discriminator_params), 'opt_state': jax.device_get(discriminator_opt_state)}, f)
-            with open(f"{models_dir}/temp_generator.pkl", 'wb') as f:
-                pickle.dump({'params': jax.device_get(generator_params), 'opt_state': jax.device_get(generator_opt_state)}, f)
-            Data.write_data(f"{models_dir}/epoch_data.json", {"epoch": current_epoch})
-        except Exception as e:
-             print(f"Error saving temp model states: {e}")
-
-
-    # Save final model states after all epochs
-    print("Training complete! Saving final models.")
-    try:
-        with open(f"{models_dir}/epoch-{start_epoch + generator_epochs - 1}-discriminator.pkl", 'wb') as f:
-            pickle.dump({'params': jax.device_get(discriminator_params)}, f)
-        with open(f"{models_dir}/epoch-{start_epoch + generator_epochs - 1}-generator.pkl", 'wb') as f:
-            pickle.dump({'params': jax.device_get(generator_params)}, f)
-    except Exception as e:
-        print(f"Error saving final model states: {e}")
-
 
 start_training()

training.txt

@@ -1,194 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-
-import torch.nn.functional as F
-import torchaudio
-import tqdm
-
-import argparse
-
-import math
-
-import os
-
-from torch.utils.data import random_split
-from torch.utils.data import DataLoader
-
-import AudioUtils
-from data import AudioDataset
-from generator import SISUGenerator
-from discriminator import SISUDiscriminator
-
-from training_utils import discriminator_train, generator_train
-import file_utils as Data
-
-import torchaudio.transforms as T
-
-# Init script argument parser
-parser = argparse.ArgumentParser(description="Training script")
-parser.add_argument("--generator", type=str, default=None,
-                    help="Path to the generator model file")
-parser.add_argument("--discriminator", type=str, default=None,
-                    help="Path to the discriminator model file")
-parser.add_argument("--device", type=str, default="cpu",  help="Select device")
-parser.add_argument("--epoch", type=int, default=0, help="Current epoch for model versioning")
-parser.add_argument("--debug", action="store_true",  help="Print debug logs")
-parser.add_argument("--continue_training", action="store_true", help="Continue training using temp_generator and temp_discriminator models")
-
-args = parser.parse_args()
-
-device = torch.device(args.device if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# Parameters
-sample_rate = 44100
-n_fft = 2048
-hop_length = 256
-win_length = n_fft
-n_mels = 128
-n_mfcc = 20 # If using MFCC
-
-mfcc_transform = T.MFCC(
-    sample_rate,
-    n_mfcc,
-    melkwargs = {'n_fft': n_fft, 'hop_length': hop_length}
-).to(device)
-
-mel_transform = T.MelSpectrogram(
-    sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length,
-    win_length=win_length, n_mels=n_mels, power=1.0 # Magnitude Mel
-).to(device)
-
-stft_transform = T.Spectrogram(
-    n_fft=n_fft, win_length=win_length, hop_length=hop_length
-).to(device)
-
-debug = args.debug
-
-# Initialize dataset and dataloader
-dataset_dir = './dataset/good'
-dataset = AudioDataset(dataset_dir, device)
-models_dir = "models"
-os.makedirs(models_dir, exist_ok=True)
-audio_output_dir = "output"
-os.makedirs(audio_output_dir, exist_ok=True)
-
-# ========= SINGLE =========
-
-train_data_loader = DataLoader(dataset, batch_size=64, shuffle=True)
-
-
-# ========= MODELS =========
-
-generator = SISUGenerator()
-discriminator = SISUDiscriminator()
-
-epoch: int = args.epoch
-epoch_from_file = Data.read_data(f"{models_dir}/epoch_data.json")
-
-if args.continue_training:
-    generator.load_state_dict(torch.load(f"{models_dir}/temp_generator.pt", map_location=device, weights_only=True))
-    discriminator.load_state_dict(torch.load(f"{models_dir}/temp_generator.pt", map_location=device, weights_only=True))
-    epoch = epoch_from_file["epoch"] + 1
-else:
-    if args.generator is not None:
-        generator.load_state_dict(torch.load(args.generator, map_location=device, weights_only=True))
-    if args.discriminator is not None:
-        discriminator.load_state_dict(torch.load(args.discriminator, map_location=device, weights_only=True))
-
-generator = generator.to(device)
-discriminator = discriminator.to(device)
-
-# Loss
-criterion_g = nn.BCEWithLogitsLoss()
-criterion_d = nn.BCEWithLogitsLoss()
-
-# Optimizers
-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))
-
-# Scheduler
-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)
-
-def start_training():
-    generator_epochs = 5000
-    for generator_epoch in range(generator_epochs):
-        low_quality_audio = (torch.empty((1)), 1)
-        high_quality_audio = (torch.empty((1)), 1)
-        ai_enhanced_audio = (torch.empty((1)), 1)
-
-        times_correct = 0
-
-        # ========= TRAINING =========
-        for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Training epoch {generator_epoch+1}/{generator_epochs}, Current epoch {epoch+1}"):
-        # for high_quality_clip, low_quality_clip in train_data_loader:
-            high_quality_sample = (high_quality_clip[0], high_quality_clip[1])
-            low_quality_sample = (low_quality_clip[0], low_quality_clip[1])
-
-            # ========= 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 =========
-            discriminator.train()
-            d_loss = discriminator_train(
-                high_quality_sample,
-                low_quality_sample,
-                real_labels,
-                fake_labels,
-                discriminator,
-                generator,
-                criterion_d,
-                optimizer_d
-            )
-
-            # ========= GENERATOR =========
-            generator.train()
-            generator_output, combined_loss, adversarial_loss, mel_l1_tensor, log_stft_l1_tensor, mfcc_l_tensor = generator_train(
-                low_quality_sample,
-                high_quality_sample,
-                real_labels,
-                generator,
-                discriminator,
-                criterion_d,
-                optimizer_g,
-                device,
-                mel_transform,
-                stft_transform,
-                mfcc_transform
-            )
-
-            if debug:
-                print(f"D_LOSS: {d_loss.item():.4f}, COMBINED_LOSS: {combined_loss.item():.4f}, ADVERSARIAL_LOSS: {adversarial_loss.item():.4f}, MEL_L1_LOSS: {mel_l1_tensor.item():.4f}, LOG_STFT_L1_LOSS: {log_stft_l1_tensor.item():.4f}, MFCC_LOSS: {mfcc_l_tensor.item():.4f}")
-            scheduler_d.step(d_loss.detach())
-            scheduler_g.step(adversarial_loss.detach())
-
-            # ========= SAVE LATEST AUDIO =========
-            high_quality_audio = (high_quality_clip[0][0], high_quality_clip[1][0])
-            low_quality_audio = (low_quality_clip[0][0], low_quality_clip[1][0])
-            ai_enhanced_audio = (generator_output[0], high_quality_clip[1][0])
-
-        new_epoch = generator_epoch+epoch
-
-        if generator_epoch % 25 == 0:
-            print(f"Saved epoch {new_epoch}!")
-            torchaudio.save(f"{audio_output_dir}/epoch-{new_epoch}-audio-crap.wav", low_quality_audio[0].cpu().detach(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
-            torchaudio.save(f"{audio_output_dir}/epoch-{new_epoch}-audio-ai.wav", ai_enhanced_audio[0].cpu().detach(), ai_enhanced_audio[1])
-            torchaudio.save(f"{audio_output_dir}/epoch-{new_epoch}-audio-orig.wav", high_quality_audio[0].cpu().detach(), high_quality_audio[1])
-
-        #if debug:
-        #    print(generator.state_dict().keys())
-        #    print(discriminator.state_dict().keys())
-        torch.save(discriminator.state_dict(), f"{models_dir}/temp_discriminator.pt")
-        torch.save(generator.state_dict(), f"{models_dir}/temp_generator.pt")
-        Data.write_data(f"{models_dir}/epoch_data.json", {"epoch": new_epoch})
-
-
-    torch.save(discriminator, "models/epoch-5000-discriminator.pt")
-    torch.save(generator, "models/epoch-5000-generator.pt")
-    print("Training complete!")
-
-start_training()

training_utils.py

@@ -20,10 +20,12 @@ def mel_spectrogram_l1_loss(mel_transform: T.MelSpectrogram, y_true: torch.Tenso
     mel_spec_true = mel_transform(y_true)
     mel_spec_pred = mel_transform(y_pred)
 
+    # Ensure same time dimension length (due to potential framing differences)
     min_len = min(mel_spec_true.shape[-1], mel_spec_pred.shape[-1])
     mel_spec_true = mel_spec_true[..., :min_len]
     mel_spec_pred = mel_spec_pred[..., :min_len]
 
+    # L1 Loss (Mean Absolute Error)
     loss = torch.mean(torch.abs(mel_spec_true - mel_spec_pred))
     return loss