💩 | VERY CRUDE JAX implementation...

.gitignore

@@ -166,3 +166,4 @@ dataset/
 old-output/
 output/
 *.wav
+models/

AudioUtils.py

@@ -0,0 +1,18 @@
+import torch
+import torch.nn.functional as F
+
+def stereo_tensor_to_mono(waveform):
+    if waveform.shape[0] > 1:
+        # Average across channels
+        mono_waveform = torch.mean(waveform, dim=0, keepdim=True)
+    else:
+        # Already mono
+        mono_waveform = waveform
+    return mono_waveform
+
+def stretch_tensor(tensor, target_length):
+    scale_factor = target_length / tensor.size(1)
+
+    tensor = F.interpolate(tensor, scale_factor=scale_factor, mode='linear', align_corners=False)
+
+    return tensor

README.md

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

data.py

@@ -1,50 +1,104 @@
+# 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 AudioUtils
 
+class AudioDatasetNumPy(Dataset): # Renamed slightly for clarity
+    audio_sample_rates = [11025]
+    MAX_LENGTH = 44100  # Define your desired maximum length here
 
-class AudioDataset(Dataset):
+    def __init__(self, input_dir):
-    audio_sample_rates = [8000, 11025, 16000, 22050]
+        """
-
+        Initializes the dataset. Device argument is removed.
-    def __init__(self, input_dir, target_duration=None, padding_mode='constant', padding_value=0.0):
+        """
-        self.input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith('.wav')]
+        self.input_files = [
-        self.target_duration = target_duration  # Duration in seconds or None if not set
+            os.path.join(root, f)
-        self.padding_mode = padding_mode
+            for root, _, files in os.walk(input_dir)
-        self.padding_value = padding_value
+            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):
-        high_quality_wav, sr_original = torchaudio.load(self.input_files[idx], normalize=True)
+        """
+        Loads audio, processes it, and returns NumPy arrays.
+        """
+        # --- 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
 
-        sample_rate = random.choice(self.audio_sample_rates)
+        # Generate low-quality audio with random downsampling
-        resample_transform = torchaudio.transforms.Resample(sr_original, sample_rate)
+        mangled_sample_rate = random.choice(self.audio_sample_rates)
-        low_quality_wav = resample_transform(high_quality_wav)
-        low_quality_wav = low_quality_wav
 
-        # Calculate target length based on desired duration and 16000 Hz
+        # Ensure sample rates are different before resampling
-        if self.target_duration is not None:
+        if original_sample_rate != mangled_sample_rate:
-            target_length = int(self.target_duration * 44100)
+            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)
+            low_quality_audio_pt = resample_transform_high(low_quality_audio_pt)
         else:
-            # Calculate duration of original high quality audio
+            # If rates match, just copy the tensor
-            target_length = high_quality_wav.size(1)
+            low_quality_audio_pt = high_quality_audio_pt.clone()
-
-        # Pad both to the calculated target length
-        high_quality_wav = self.stretch_tensor(high_quality_wav, target_length)
-        low_quality_wav = self.stretch_tensor(low_quality_wav, target_length)
 
 
-        return low_quality_wav, high_quality_wav
+        # --- Process Stereo to Mono (still using PyTorch tensors) ---
+        # Assuming AudioUtils.stereo_tensor_to_mono expects PyTorch Tensor (C, L)
+        # and returns PyTorch Tensor (1, L)
+        try:
+            high_quality_audio_pt_mono = AudioUtils.stereo_tensor_to_mono(high_quality_audio_pt)
+            low_quality_audio_pt_mono = AudioUtils.stereo_tensor_to_mono(low_quality_audio_pt)
+        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)
 
-    def stretch_tensor(self, tensor, target_length):
-        current_length = tensor.size(1)
-        scale_factor = target_length / current_length
 
-        # Resample the tensor using linear interpolation
+        # --- Convert to NumPy Arrays ---
-        tensor = F.interpolate(tensor.unsqueeze(0), scale_factor=scale_factor, mode='linear', align_corners=False).squeeze(0)
+        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)
 
-        return tensor
+
+        # --- 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

@@ -1,24 +1,145 @@
-import torch.nn as nn
+import jax
+import jax.numpy as jnp
+from flax import linen as nn
+from typing import Sequence, Tuple
+
+# Assume InstanceNorm1d and AttentionBlock are defined as in the generator conversion
+# --- Custom InstanceNorm1d Implementation (from Generator) ---
+class InstanceNorm1d(nn.Module):
+    features: int
+    epsilon: float = 1e-5
+    use_scale: bool = True
+    use_bias: bool = True
+    @nn.compact
+    def __call__(self, x):
+        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)
+        var = jnp.var(x, axis=1, keepdims=True)
+        normalized = (x - mean) / jnp.sqrt(var + self.epsilon)
+        if self.use_scale:
+            scale = self.param('scale', nn.initializers.ones, (self.features,))
+            normalized *= scale
+        if self.use_bias:
+            bias = self.param('bias', nn.initializers.zeros, (self.features,))
+            normalized += bias
+        return normalized
+
+# --- AttentionBlock Implementation (from Generator) ---
+class AttentionBlock(nn.Module):
+    channels: int
+    @nn.compact
+    def __call__(self, x):
+        ks1 = (1,)
+        attention_weights = nn.Conv(features=self.channels // 4, kernel_size=ks1, padding='SAME')(x)
+        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)
+        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):
-    def __init__(self):
+    """SISUDiscriminator model translated to Flax."""
-        super(SISUDiscriminator, self).__init__()
+    base_channels: int = 16
-        self.model = nn.Sequential(
-            nn.Conv1d(2, 128, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(128, 256, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(256, 128, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(128, 64, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(64, 1, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
-        )
-        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)  # Output size (1,)
 
-    def forward(self, x):
+    @nn.compact
-        x = self.model(x)
+    def __call__(self, x):
-        x = self.global_avg_pool(x)
+        """
-        x = x.view(-1, 1)  # Flatten to (batch_size, 1)
+        Args:
-        return x
+            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
+
+        # Block 1: 1 -> ch, k=7, s=1, d=1, SN=T, IN=F
+        # NOTE: Spectral Norm omitted
+        y = DiscriminatorBlock(in_channels=1, out_channels=ch, kernel_size=7, stride=1, use_instance_norm=False)(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

file_utils.py

@@ -0,0 +1,28 @@
+import json
+
+filepath = "my_data.json"
+
+def write_data(filepath, data):
+  try:
+    with open(filepath, 'w') as f:
+      json.dump(data, f, indent=4)  # Use indent for pretty formatting
+    print(f"Data written to '{filepath}'")
+  except Exception as e:
+    print(f"Error writing to file: {e}")
+
+
+def read_data(filepath):
+  try:
+    with open(filepath, 'r') as f:
+      data = json.load(f)
+    print(f"Data read from '{filepath}'")
+    return data
+  except FileNotFoundError:
+    print(f"File not found: {filepath}")
+    return None
+  except json.JSONDecodeError:
+    print(f"Error decoding JSON from file: {filepath}")
+    return None
+  except Exception as e:
+    print(f"Error reading from file: {e}")
+    return None

generator.py

@@ -1,23 +1,173 @@
-import torch.nn as nn
+import jax
+import jax.numpy as jnp
+from flax import linen as nn
+from typing import Sequence, Tuple
+
+# --- Custom InstanceNorm1d Implementation ---
+class InstanceNorm1d(nn.Module):
+    """
+    Flax implementation of Instance Normalization for 1D data (NLC format).
+    Normalizes across the 'L' dimension.
+    """
+    features: int
+    epsilon: float = 1e-5
+    use_scale: bool = True
+    use_bias: bool = True
+
+    @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
+
+    @nn.compact
+    def __call__(self, x):
+        """
+        Args:
+            x: Input tensor (N, L, C)
+        Returns:
+            Attention-weighted output tensor (N, L, C)
+        """
+        # 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)
+
+        return x * attention_weights
+
+class ResidualInResidualBlock(nn.Module):
+    """ResidualInResidualBlock in Flax."""
+    channels: int
+    num_convs: int = 3
+
+    @nn.compact
+    def __call__(self, x):
+        """
+        Args:
+            x: Input tensor (N, L, C)
+        Returns:
+            Output tensor (N, L, C)
+        """
+        residual = x
+        y = x
+        # Sequentially apply ConvBlocks
+        for _ in range(self.num_convs):
+            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):
-    def __init__(self, upscale_scale=1): # No noise_dim parameter
+    """SISUGenerator model translated to Flax."""
-        super(SISUGenerator, self).__init__()
+    channels: int = 16
-        self.model = nn.Sequential(
+    num_rirb: int = 4
-            nn.Conv1d(2, 128, kernel_size=3, padding=1),
+    alpha: float = 1.0 # Non-learnable parameter, passed during init
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(128, 256, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
 
-            nn.Upsample(scale_factor=upscale_scale, mode='nearest'),
+    @nn.compact
+    def __call__(self, x):
+        """
+        Args:
+            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}")
 
-            nn.Conv1d(256, 128, kernel_size=3, padding=1),
+        residual_input = x
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(128, 64, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(64, 2, kernel_size=3, padding=1),
-            nn.Tanh()
-        )
 
-    def forward(self, x):
+        # Initial convolution block
-        return self.model(x)
+        # Flax Conv expects kernel_size as sequence
+        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

requirements.txt

@@ -1,12 +1,12 @@
-filelock>=3.16.1
+filelock==3.16.1
-fsspec>=2024.10.0
+fsspec==2024.10.0
-Jinja2>=3.1.4
+Jinja2==3.1.4
-MarkupSafe>=2.1.5
+MarkupSafe==2.1.5
-mpmath>=1.3.0
+mpmath==1.3.0
-networkx>=3.4.2
+networkx==3.4.2
-numpy>=2.1.2
+numpy==2.2.3
-pillow>=11.0.0
+pillow==11.0.0
-setuptools>=70.2.0
+setuptools==70.2.0
-sympy>=1.13.1
+sympy==1.13.3
-tqdm>=4.67.1
+tqdm==4.67.1
-typing_extensions>=4.12.2
+typing_extensions==4.12.2

test.py

@@ -1,10 +0,0 @@
-import torch.nn as nn
-import torch
-from discriminator import SISUDiscriminator
-
-
-discriminator = SISUDiscriminator()
-test_input = torch.randn(1, 2, 1000) # Example input (batch_size, channels, frames)
-output = discriminator(test_input)
-print(output)
-print("Output shape:", output.shape)

training.py

@@ -1,135 +1,481 @@
-import torch
+import jax
-import torch.nn as nn
+import jax.numpy as jnp
-import torch.optim as optim
+import optax
-
-import torch.nn.functional as F
-import torchaudio
 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
 
-from torch.utils.data import random_split
+import torch
 from torch.utils.data import DataLoader
 
-from data import AudioDataset
+import file_utils as Data
+from data import AudioDatasetNumPy
 from generator import SISUGenerator
 from discriminator import SISUDiscriminator
 
-# Check for CUDA availability
+# Init script argument parser
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+parser = argparse.ArgumentParser(description="Training script")
-print(f"Using device: {device}")
+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("--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()
+
+# Parameters
+sample_rate = 44100
+n_fft = 2048
+hop_length = 256
+win_length = n_fft
+n_mels = 128
+n_mfcc = 20 # If using MFCC
+
+debug = args.debug
+
+# Initialize JAX random key
+key = jax.random.PRNGKey(0)
 
 # Initialize dataset and dataloader
 dataset_dir = './dataset/good'
-dataset = AudioDataset(dataset_dir, target_duration=2.0)
+dataset = AudioDatasetNumPy(dataset_dir) # Use your JAX dataset
+train_data_loader = DataLoader(dataset, batch_size=4, shuffle=True) # Use your JAX DataLoader
 
-dataset_size = len(dataset)
+models_dir = "models"
-train_size = int(dataset_size * .9)
+os.makedirs(models_dir, exist_ok=True)
-val_size = int(dataset_size-train_size)
+audio_output_dir = "output"
+os.makedirs(audio_output_dir, exist_ok=True)
 
-train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
 
-train_data_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
+# ========= MODELS =========
-val_data_loader = DataLoader(val_dataset, batch_size=8, shuffle=True)
 
-# Initialize models and move them to device
+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)
 
-generator = generator.to(device)
+    # === FIX ===
-discriminator = discriminator.to(device)
+    # Transpose the array from (batch, channels, length) to (batch, length, channels)
+    # 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
 
-# Loss
+    print(sample_input_array.shape) # Should now print (4, 44100, 1)
-criterion_g = nn.L1Loss()
+    # === END FIX ===
-criterion_d = nn.BCEWithLogitsLoss()
 
-# Optimizers
+except StopIteration:
-optimizer_g = optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
+    print("Error: Data loader is empty. Cannot initialize models.")
-optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0001, betas=(0.5, 0.999))
+    exit() # Exit if no data is available
 
-# 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 snr(y_true, y_pred):
+key, init_key_g, init_key_d = jax.random.split(key, 3)
-    noise = y_true - y_pred
+generator_model = SISUGenerator()
-    signal_power = torch.mean(y_true ** 2)
+discriminator_model = SISUDiscriminator()
-    noise_power = torch.mean(noise ** 2)
-    snr_db = 10 * torch.log10(signal_power / noise_power)
-    return snr_db
 
-def discriminator_train(discriminator, optimizer, criterion, generator, real_labels, fake_labels, high_quality, low_quality):
+# Initialize parameters
-    optimizer.zero_grad()
+generator_params = generator_model.init(init_key_g, sample_input_array)['params']
+discriminator_params = discriminator_model.init(init_key_d, sample_input_array)['params']
 
-    discriminator_decision_from_real = discriminator(high_quality)
+# Define apply functions
-    d_loss_real = criterion(discriminator_decision_from_real, real_labels)
+generator_apply_fn = generator_model.apply
+discriminator_apply_fn = discriminator_model.apply
 
-    generator_output = generator(low_quality)
-    discriminator_decision_from_fake = discriminator(generator_output.detach())
-    d_loss_fake = criterion(discriminator_decision_from_fake, fake_labels)
 
-    d_loss = (d_loss_real + d_loss_fake) / 2.0
+# 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
 
-    d_loss.backward()
+# Optimizers (using Optax)
-    nn.utils.clip_grad_norm_(discriminator.parameters(), max_norm=1.0) #Gradient Clipping
+optimizer_g = optax.adam(learning_rate=0.0001, b1=0.5, b2=0.999)
-    optimizer.step()
+optimizer_d = optax.adam(learning_rate=0.0001, b1=0.5, b2=0.999)
-    # print(f"Discriminator Loss: {d_loss.item():.4f}, Mean Real Logit: {discriminator_decision_from_real.mean().item():.2f}, Mean Fake Logit: {discriminator_decision_from_fake.mean().item():.2f}")
+
+# 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:
+        with open(f"{models_dir}/temp_generator.pkl", 'rb') as f:
+            loaded_state = pickle.load(f)
+            generator_params = loaded_state['params']
+            generator_opt_state = loaded_state['opt_state']
+        with open(f"{models_dir}/temp_discriminator.pkl", 'rb') as f:
+            loaded_state = pickle.load(f)
+            discriminator_params = loaded_state['params']
+            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:
+    try:
+        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:
+    try:
+        with open(args.discriminator, 'rb') as f:
+            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}")
+
+
+# Initialize JAX audio transforms
+# mel_transform_fn = MelSpectrogramJAX(
+#     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
+# )
+
+# 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
 
-    # Training loop
-    # discriminator_epochs = 1000
-    generator_epochs = 500
     for generator_epoch in range(generator_epochs):
-        low_quality_audio = torch.empty((1))
+        current_epoch = start_epoch + generator_epoch
-        high_quality_audio = torch.empty((1))
-        ai_enhanced_audio = torch.empty((1))
 
-        # Training
+        # These will hold the last processed audio examples from a batch for saving
-        for low_quality, high_quality in tqdm.tqdm(train_data_loader, desc=f"Epoch {generator_epoch+1}/{generator_epochs}"):
+        last_high_quality_audio = None
-            high_quality = high_quality.to(device)
+        last_low_quality_audio = None
-            low_quality = low_quality.to(device)
+        last_ai_enhanced_audio = None
+        last_sample_rate = None
 
-            batch_size = high_quality.size(0)
-            real_labels = torch.ones(batch_size, 1).to(device)
-            fake_labels = torch.zeros(batch_size, 1).to(device)
 
-            # Train Discriminator
+        # Use tqdm for progress bar
-            discriminator.train()
+        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}"):
 
-            for _ in range(3):
+            # high_quality_clip and low_quality_clip are tuples: (audio_array, sample_rate_array)
-                discriminator_train(discriminator, optimizer_d, criterion_d, generator, real_labels, fake_labels, high_quality, low_quality)
+            # 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
 
-            # Train Generator
+            # Convert NumPy arrays to JAX arrays and transpose to NLC format (batch, length, channels)
-            generator.train()
+            # Only transpose if the shape is (batch, channels, length)
-            optimizer_g.zero_grad()
+            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
 
-            # Generator loss: how well fake data fools the discriminator
+            if low_quality_audio_batch_np.ndim == 3 and low_quality_audio_batch_np.shape[1] == 1:
-            generator_output = generator(low_quality)
+                 low_quality_audio_batch = jnp.transpose(jnp.array(low_quality_audio_batch_np), (0, 2, 1))
-            discriminator_decision = discriminator(generator_output)  # No detach here
+            else:
-            g_loss = criterion_g(discriminator_decision, real_labels)  # Train generator to produce real-like outputs
+                 low_quality_audio_batch = jnp.array(low_quality_audio_batch_np) # Assume already NLC or handle other cases
 
-            g_loss.backward()
+            sample_rate_batch = jnp.array(sample_rate_batch_np)
-            optimizer_g.step()
 
-            low_quality_audio = low_quality
-            high_quality_audio = high_quality
-            ai_enhanced_audio = generator_output
 
-        metric = snr(high_quality_audio, ai_enhanced_audio)
+            batch_size = high_quality_audio_batch.shape[0]
-        print(f"Generator metric {metric}!")
+            # Create labels - JAX arrays
-        scheduler_g.step(metric)
+            real_labels = jnp.ones((batch_size, 1))
+            fake_labels = jnp.zeros((batch_size, 1))
 
-        if generator_epoch % 10 == 0:
+            # Split key for each batch
-            print(f"Saved epoch {generator_epoch}!")
+            key, batch_key = jax.random.split(key)
-            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-crap.wav", low_quality_audio[0].cpu(), 44100)
-            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-ai.wav", ai_enhanced_audio[0].cpu(), 44100)
-            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-orig.wav", high_quality_audio[0].cpu(), 44100)
 
-        if generator_epoch % 50 == 0:
+            # ========= DISCRIMINATOR =========
-            torch.save(discriminator.state_dict(), "discriminator.pt")
+            # Call the jitted discriminator training step
-            torch.save(generator.state_dict(), "generator.pt")
+            discriminator_params, discriminator_opt_state, d_loss, batch_key = discriminator_train_step(
+                discriminator_params,
+                generator_params,
+                discriminator_opt_state,
+                high_quality_audio_batch,
+                low_quality_audio_batch,
+                real_labels,
+                fake_labels,
+                discriminator_apply_fn,
+                generator_apply_fn,
+                optimizer_d,
+                criterion_d,
+                batch_key
+            )
+
+            # ========= GENERATOR =========
+            # Call the jitted generator training step
+            generator_params, generator_opt_state, combined_loss, adversarial_loss, enhanced_audio_batch, batch_key = generator_train_step(
+                generator_params,
+                discriminator_params,
+                generator_opt_state,
+                low_quality_audio_batch,
+                high_quality_audio_batch,
+                real_labels, # Generator tries to make fake data look real
+                generator_apply_fn,
+                discriminator_apply_fn,
+                optimizer_g,
+                criterion_d,
+                criterion_l1,
+                batch_key
+            )
+
+            # Print debug logs (requires waiting for JIT compilation on first step)
+            if debug:
+                # Use .block_until_ready() to ensure computation is finished before printing
+                # In a real scenario, you might want to log metrics less frequently
+                d_loss_val = d_loss.block_until_ready().item()
+                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
+            # based on the losses (e.g., reducing learning rate if loss plateaus).
+            # This logic would typically live outside the jitted step function.
+            # For Optax, you might use a schedule within the optimizer definition
+            # or update the learning rate of the optimizer manually.
+
+
+            # ========= SAVE LATEST AUDIO (from the last batch processed) =========
+            # Access the first sample of the batch for saving
+            # Ensure enhanced_audio_batch has a batch dimension and is in NLC format
+            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}")
 
-    torch.save(discriminator.state_dict(), "discriminator.pt")
-    torch.save(generator.state_dict(), "generator.pt")
-    print("Training complete!")
 
 start_training()

training.txt

@@ -0,0 +1,194 @@
+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

@@ -0,0 +1,142 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+import torchaudio
+import torchaudio.transforms as T
+
+def gpu_mfcc_loss(mfcc_transform, y_true, y_pred):
+    mfccs_true = mfcc_transform(y_true)
+    mfccs_pred = mfcc_transform(y_pred)
+
+    min_len = min(mfccs_true.shape[2], mfccs_pred.shape[2])
+    mfccs_true = mfccs_true[:, :, :min_len]
+    mfccs_pred = mfccs_pred[:, :, :min_len]
+
+    loss = torch.mean((mfccs_true - mfccs_pred)**2)
+    return loss
+
+def mel_spectrogram_l1_loss(mel_transform: T.MelSpectrogram, y_true: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:
+    mel_spec_true = mel_transform(y_true)
+    mel_spec_pred = mel_transform(y_pred)
+
+    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]
+
+    loss = torch.mean(torch.abs(mel_spec_true - mel_spec_pred))
+    return loss
+
+def mel_spectrogram_l2_loss(mel_transform: T.MelSpectrogram, y_true: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:
+    mel_spec_true = mel_transform(y_true)
+    mel_spec_pred = mel_transform(y_pred)
+
+    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]
+
+    loss = torch.mean((mel_spec_true - mel_spec_pred)**2)
+    return loss
+
+def log_stft_magnitude_loss(stft_transform: T.Spectrogram, y_true: torch.Tensor, y_pred: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    stft_mag_true = stft_transform(y_true)
+    stft_mag_pred = stft_transform(y_pred)
+
+    min_len = min(stft_mag_true.shape[-1], stft_mag_pred.shape[-1])
+    stft_mag_true = stft_mag_true[..., :min_len]
+    stft_mag_pred = stft_mag_pred[..., :min_len]
+
+    loss = torch.mean(torch.abs(torch.log(stft_mag_true + eps) - torch.log(stft_mag_pred + eps)))
+    return loss
+
+def spectral_convergence_loss(stft_transform: T.Spectrogram, y_true: torch.Tensor, y_pred: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    stft_mag_true = stft_transform(y_true)
+    stft_mag_pred = stft_transform(y_pred)
+
+    min_len = min(stft_mag_true.shape[-1], stft_mag_pred.shape[-1])
+    stft_mag_true = stft_mag_true[..., :min_len]
+    stft_mag_pred = stft_mag_pred[..., :min_len]
+
+    norm_true = torch.linalg.norm(stft_mag_true, ord='fro', dim=(-2, -1))
+    norm_diff = torch.linalg.norm(stft_mag_true - stft_mag_pred, ord='fro', dim=(-2, -1))
+
+    loss = torch.mean(norm_diff / (norm_true + eps))
+    return loss
+
+def discriminator_train(high_quality, low_quality, real_labels, fake_labels, discriminator, generator, criterion, optimizer):
+    optimizer.zero_grad()
+
+    # Forward pass for real samples
+    discriminator_decision_from_real = discriminator(high_quality[0])
+    d_loss_real = criterion(discriminator_decision_from_real, real_labels)
+
+    with torch.no_grad():
+        generator_output = generator(low_quality[0])
+    discriminator_decision_from_fake = discriminator(generator_output)
+    d_loss_fake = criterion(discriminator_decision_from_fake, fake_labels.expand_as(discriminator_decision_from_fake))
+
+    d_loss = (d_loss_real + d_loss_fake) / 2.0
+
+    d_loss.backward()
+    # Optional: Gradient Clipping (can be helpful)
+    # nn.utils.clip_grad_norm_(discriminator.parameters(), max_norm=1.0)  # Gradient Clipping
+    optimizer.step()
+
+    return d_loss
+
+def generator_train(
+    low_quality,
+    high_quality,
+    real_labels,
+    generator,
+    discriminator,
+    adv_criterion,
+    g_optimizer,
+    device,
+    mel_transform: T.MelSpectrogram,
+    stft_transform: T.Spectrogram,
+    mfcc_transform: T.MFCC,
+    lambda_adv: float = 1.0,
+    lambda_mel_l1: float = 10.0,
+    lambda_log_stft: float = 1.0,
+    lambda_mfcc: float = 1.0
+):
+    g_optimizer.zero_grad()
+
+    generator_output = generator(low_quality[0])
+
+    discriminator_decision = discriminator(generator_output)
+    adversarial_loss = adv_criterion(discriminator_decision, real_labels.expand_as(discriminator_decision))
+
+    mel_l1 = 0.0
+    log_stft_l1 = 0.0
+    mfcc_l = 0.0
+
+    # Calculate Mel L1 Loss if weight is positive
+    if lambda_mel_l1 > 0:
+        mel_l1 = mel_spectrogram_l1_loss(mel_transform, high_quality[0], generator_output)
+
+    # Calculate Log STFT L1 Loss if weight is positive
+    if lambda_log_stft > 0:
+        log_stft_l1 = log_stft_magnitude_loss(stft_transform, high_quality[0], generator_output)
+
+    # Calculate MFCC Loss if weight is positive
+    if lambda_mfcc > 0:
+        mfcc_l = gpu_mfcc_loss(mfcc_transform, high_quality[0], generator_output)
+
+    mel_l1_tensor = torch.tensor(mel_l1, device=device) if isinstance(mel_l1, float) else mel_l1
+    log_stft_l1_tensor = torch.tensor(log_stft_l1, device=device) if isinstance(log_stft_l1, float) else log_stft_l1
+    mfcc_l_tensor = torch.tensor(mfcc_l, device=device) if isinstance(mfcc_l, float) else mfcc_l
+
+    combined_loss = (lambda_adv * adversarial_loss) + \
+                    (lambda_mel_l1 * mel_l1_tensor) + \
+                    (lambda_log_stft * log_stft_l1_tensor) + \
+                    (lambda_mfcc * mfcc_l_tensor)
+
+    combined_loss.backward()
+    # Optional: Gradient Clipping
+    # nn.utils.clip_grad_norm_(generator.parameters(), max_norm=1.0)
+    g_optimizer.step()
+
+    # 6. Return values for logging
+    return generator_output, combined_loss, adversarial_loss, mel_l1_tensor, log_stft_l1_tensor, mfcc_l_tensor