⚗️ | Experimenting...

AudioUtils.py

@@ -1,41 +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 stereo_tensor_to_mono(waveform: torch.Tensor) -> torch.Tensor:
-    mono_tensor = torch.mean(waveform, dim=0, keepdim=True)
-    return mono_tensor
+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)
 
-def pad_tensor(audio_tensor: torch.Tensor, target_length: int = 512) -> torch.Tensor:
-    padding_amount = target_length - audio_tensor.size(-1)
-    if padding_amount <= 0:
-        return audio_tensor
-
-    padded_audio_tensor = F.pad(audio_tensor, (0, padding_amount))
-
-    return padded_audio_tensor
-
-
-def split_audio(audio_tensor: torch.Tensor, chunk_size: int = 512, pad_last_tensor: bool = False) -> list[torch.Tensor]:
-    chunks = list(torch.split(audio_tensor, chunk_size, dim=1))
-
-    if pad_last_tensor:
-        last_chunk = chunks[-1]
-
-        if last_chunk.size(-1) < chunk_size:
-                    chunks[-1] = pad_tensor(last_chunk, chunk_size)
-
-    return chunks
-
-
-def reconstruct_audio(chunks: list[torch.Tensor]) -> torch.Tensor:
-    reconstructed_tensor = torch.cat(chunks, dim=-1)
-    return reconstructed_tensor
-
-
-def normalize(audio_tensor: torch.Tensor, eps: float = 1e-8) -> torch.Tensor:
-    max_val = torch.max(torch.abs(audio_tensor))
-    if max_val < eps:
-        return audio_tensor
-    return audio_tensor / max_val
+    return tensor

README.md

@@ -2,7 +2,7 @@
 
 ### Overview
 
-SISU (Super Ingenious Sound Upscaler) is an experimental project that is mostly vibe coded and uses GANs (Generative Adversarial Networks) to make low-quality audio better. The goal is to take not-so-good-sounding audio and turn it into high-quality, clear audio.
+SISU (Super Ingenious Sound Upscaler) is a project that uses GANs (Generative Adversarial Networks) to make low-quality audio better. The goal is to take not-so-good-sounding audio and turn it into high-quality, clear audio.
 
 ### Structure of the Project
 
@@ -18,7 +18,6 @@ SISU (Super Ingenious Sound Upscaler) is an experimental project that is mostly
 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.

app.py

@@ -1,128 +0,0 @@
-import argparse
-
-import torch
-import torchaudio
-import torchcodec
-import tqdm
-from accelerate import Accelerator
-
-import AudioUtils
-from generator import SISUGenerator
-
-# Init script argument parser
-parser = argparse.ArgumentParser(description="Training script")
-parser.add_argument("--device", type=str, default="cpu", help="Select device")
-parser.add_argument("--model", type=str, help="Model to use for upscaling")
-parser.add_argument(
-    "--clip_length",
-    type=int,
-    default=8000,
-    help="Internal clip length, leave unspecified if unsure",
-)
-parser.add_argument(
-    "--sample_rate", type=int, default=44100, help="Output clip sample rate"
-)
-parser.add_argument(
-    "--bitrate",
-    type=int,
-    default=192000,
-    help="Output clip bitrate",
-)
-parser.add_argument("-i", "--input", type=str, help="Input audio file")
-parser.add_argument("-o", "--output", type=str, help="Output audio file")
-
-args = parser.parse_args()
-
-if args.sample_rate < 8000:
-    print(
-        "Sample rate cannot be lower than 8000! (44100 is recommended for base models)"
-    )
-    exit()
-
-# ---------------------------
-# Init accelerator
-# ---------------------------
-
-accelerator = Accelerator(mixed_precision="bf16")
-
-# ---------------------------
-# Models
-# ---------------------------
-generator = SISUGenerator()
-
-accelerator.print("🔨 | Compiling models...")
-
-generator = torch.compile(generator)
-
-accelerator.print("✅ | Compiling done!")
-
-# ---------------------------
-# Prepare accelerator
-# ---------------------------
-
-generator = accelerator.prepare(generator)
-
-# ---------------------------
-# Checkpoint helpers
-# ---------------------------
-
-models_dir = args.model
-clip_length = args.clip_length
-input_audio = args.input
-output_audio = args.output
-
-if models_dir:
-    ckpt = torch.load(models_dir)
-
-    accelerator.unwrap_model(generator).load_state_dict(ckpt["G"])
-
-    accelerator.print("💾 | Loaded model!")
-else:
-    print(
-        "Generator model (--model) isn't specified. Do you have the trained model? If not, you need to train it OR acquire it from somewhere (DON'T ASK ME, YET!)"
-    )
-
-
-def start():
-    # To Mono!
-    decoder = torchcodec.decoders.AudioDecoder(input_audio)
-
-    decoded_samples = decoder.get_all_samples()
-    audio = decoded_samples.data
-    original_sample_rate = decoded_samples.sample_rate
-
-    # Support for multichannel audio
-    # audio = AudioUtils.stereo_tensor_to_mono(audio)
-    audio = AudioUtils.normalize(audio)
-
-    resample_transform = torchaudio.transforms.Resample(
-        original_sample_rate, args.sample_rate
-    )
-
-    audio = resample_transform(audio)
-
-    splitted_audio = AudioUtils.split_audio(audio, clip_length)
-    splitted_audio_on_device = [t.view(1, t.shape[0], t.shape[-1]).to(accelerator.device) for t in splitted_audio]
-    processed_audio = []
-    with torch.no_grad():
-        for clip in tqdm.tqdm(splitted_audio_on_device, desc="Processing..."):
-            channels = []
-            for audio_channel in torch.split(clip, 1, dim=1):
-                output_piece = generator(audio_channel)
-                channels.append(output_piece.detach().cpu())
-            output_clip = torch.cat(channels, dim=1)
-            processed_audio.append(output_clip)
-
-    reconstructed_audio = AudioUtils.reconstruct_audio(processed_audio)
-    reconstructed_audio = reconstructed_audio.squeeze(0)
-    print(f"🔊 | Saving {output_audio}!")
-    torchaudio.save_with_torchcodec(
-        uri=output_audio,
-        src=reconstructed_audio,
-        sample_rate=args.sample_rate,
-        channels_first=True,
-        compression=args.bitrate,
-    )
-
-
-start()

data.py

@@ -1,71 +1,52 @@
-import os
-import random
-
+from torch.utils.data import Dataset
+import torch.nn.functional as F
 import torch
 import torchaudio
-import torchcodec.decoders as decoders
-import tqdm
-from torch.utils.data import Dataset
-
-import AudioUtils
-
+import os
+import random
+from AudioUtils import stereo_tensor_to_mono, stretch_tensor
 
 class AudioDataset(Dataset):
-    audio_sample_rates = [8000, 11025, 12000, 16000, 22050, 24000, 32000, 44100]
+    audio_sample_rates = [11025]
 
-    def __init__(self, input_dir, clip_length: int = 512, normalize: bool = True):
-        self.clip_length = clip_length
-        self.normalize = normalize
-
-        input_files = [
-            os.path.join(input_dir, f)
-            for f in os.listdir(input_dir)
-            if os.path.isfile(os.path.join(input_dir, f))
-            and f.lower().endswith((".wav", ".mp3", ".flac"))
+    def __init__(self, input_dir):
+        self.input_files = [
+            os.path.join(root, f)
+            for root, _, files in os.walk(input_dir)
+            for f in files if f.endswith('.wav')
         ]
 
-        data = []
-        for audio_clip in tqdm.tqdm(
-            input_files, desc=f"Processing {len(input_files)} audio file(s)"
-        ):
-            decoder = decoders.AudioDecoder(audio_clip)
-            decoded_samples = decoder.get_all_samples()
-
-            audio = decoded_samples.data.float()
-            original_sample_rate = decoded_samples.sample_rate
-
-            if normalize:
-                audio = AudioUtils.normalize(audio)
-
-            splitted_high_quality_audio = AudioUtils.split_audio(audio, clip_length, True)
-
-            if not splitted_high_quality_audio:
-                continue
-
-            for splitted_audio_clip in splitted_high_quality_audio:
-                for audio_clip in torch.split(splitted_audio_clip, 1):
-                    data.append((audio_clip, original_sample_rate))
-
-        self.audio_data = data
-
     def __len__(self):
-        return len(self.audio_data)
+        return len(self.input_files)
 
     def __getitem__(self, idx):
-        audio_clip = self.audio_data[idx]
+        # Load high-quality audio
+        high_quality_path = self.input_files[idx]
+        high_quality_audio, original_sample_rate = torchaudio.load(high_quality_path)
+        high_quality_audio = stereo_tensor_to_mono(high_quality_audio)
+
+        # Generate low-quality audio with random downsampling
         mangled_sample_rate = random.choice(self.audio_sample_rates)
+        resample_low = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
+        low_quality_audio = resample_low(high_quality_audio)
 
-        resample_transform_low = torchaudio.transforms.Resample(
-            audio_clip[1], mangled_sample_rate
-        )
+        resample_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
+        low_quality_audio = resample_high(low_quality_audio)
 
-        resample_transform_high = torchaudio.transforms.Resample(
-            mangled_sample_rate, audio_clip[1]
-        )
+        # Pad or truncate to match a fixed length
+        target_length = 44100  # Adjust this based on your data
+        high_quality_audio = self.pad_or_truncate(high_quality_audio, target_length)
+        low_quality_audio = self.pad_or_truncate(low_quality_audio, target_length)
 
-        low_audio_clip = resample_transform_high(resample_transform_low(audio_clip[0]))
-        if audio_clip[0].shape[1] < low_audio_clip.shape[1]:
-            low_audio_clip = low_audio_clip[:, :audio_clip[0].shape[1]]
-        elif audio_clip[0].shape[1] > low_audio_clip.shape[1]:
-            low_audio_clip = AudioUtils.pad_tensor(low_audio_clip, self.clip_length)
-        return ((audio_clip[0], low_audio_clip), (audio_clip[1], mangled_sample_rate))
+        return (high_quality_audio, original_sample_rate), (low_quality_audio, mangled_sample_rate)
+
+    def pad_or_truncate(self, tensor, target_length):
+        current_length = tensor.size(1)
+        if current_length < target_length:
+            # Pad with zeros
+            padding = target_length - current_length
+            tensor = F.pad(tensor, (0, padding))
+        else:
+            # Truncate to target length
+            tensor = tensor[:, :target_length]
+        return tensor

discriminator.py

@@ -1,179 +1,38 @@
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.utils.parametrizations import weight_norm, spectral_norm
+import torch.nn.utils as utils
 
-# -------------------------------------------------------------------
-# 1. Multi-Period Discriminator (MPD)
-#    Captures periodic structures (pitch/timbre) by folding audio.
-# -------------------------------------------------------------------
-
-class DiscriminatorP(nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-
-        # Use spectral_norm for stability, or weight_norm for performance
-        norm_f = spectral_norm if use_spectral_norm else weight_norm
-
-        # We use 2D convs because we "fold" the 1D audio into 2D (Period x Time)
-        self.convs = nn.ModuleList([
-            norm_f(nn.Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(2, 0))),
-            norm_f(nn.Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(2, 0))),
-            norm_f(nn.Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(2, 0))),
-            norm_f(nn.Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(2, 0))),
-            norm_f(nn.Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
-        ])
-
-        self.conv_post = norm_f(nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d conversion: [B, C, T] -> [B, C, T/P, P]
-        b, c, t = x.shape
-        if t % self.period != 0: # Pad if not divisible by period
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, 0.1)
-            fmap.append(x) # Store feature map for Feature Matching Loss
-
-        x = self.conv_post(x)
-        fmap.append(x)
-
-        # Flatten back to 1D for score
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(nn.Module):
-    def __init__(self, periods=[2, 3, 5, 7, 11]):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorP(p) for p in periods
-        ])
-
-    def forward(self, y, y_hat):
-        y_d_rs = [] # Real scores
-        y_d_gs = [] # Generated (Fake) scores
-        fmap_rs = [] # Real feature maps
-        fmap_gs = [] # Generated (Fake) feature maps
-
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-# -------------------------------------------------------------------
-# 2. Multi-Scale Discriminator (MSD)
-#    Captures structure at different audio resolutions (raw, x0.5, x0.25).
-# -------------------------------------------------------------------
-
-class DiscriminatorS(nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = spectral_norm if use_spectral_norm else weight_norm
-
-        # Standard 1D Convolutions with large receptive field
-        self.convs = nn.ModuleList([
-            norm_f(nn.Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(nn.Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(nn.Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(nn.Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(nn.Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(nn.Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(nn.Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, 0.1)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-        return x, fmap
-
-
-class MultiScaleDiscriminator(nn.Module):
-    def __init__(self):
-        super(MultiScaleDiscriminator, self).__init__()
-        # 3 Scales: Original, Downsampled x2, Downsampled x4
-        self.discriminators = nn.ModuleList([
-            DiscriminatorS(use_spectral_norm=True),
-            DiscriminatorS(),
-            DiscriminatorS(),
-        ])
-        self.meanpools = nn.ModuleList([
-            nn.AvgPool1d(4, 2, padding=2),
-            nn.AvgPool1d(4, 2, padding=2)
-        ])
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                # Downsample input for subsequent discriminators
-                y = self.meanpools[i-1](y)
-                y_hat = self.meanpools[i-1](y_hat)
-
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-# -------------------------------------------------------------------
-# 3. Master Wrapper
-#    Combines MPD and MSD into one class to fit your training script.
-# -------------------------------------------------------------------
+def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
+    padding = (kernel_size // 2) * dilation
+    return nn.Sequential(
+        utils.spectral_norm(
+            nn.Conv1d(in_channels, out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=padding
+            )
+        ),
+        nn.BatchNorm1d(out_channels),
+        nn.LeakyReLU(0.2, inplace=True)
+    )
 
 class SISUDiscriminator(nn.Module):
     def __init__(self):
         super(SISUDiscriminator, self).__init__()
-        self.mpd = MultiPeriodDiscriminator()
-        self.msd = MultiScaleDiscriminator()
-
-    def forward(self, y, y_hat):
-        # Return format:
-        # scores_real, scores_fake, features_real, features_fake
-
-        # Run Multi-Period
-        mpd_y_d_rs, mpd_y_d_gs, mpd_fmap_rs, mpd_fmap_gs = self.mpd(y, y_hat)
-
-        # Run Multi-Scale
-        msd_y_d_rs, msd_y_d_gs, msd_fmap_rs, msd_fmap_gs = self.msd(y, y_hat)
-
-        # Combine all results
-        return (
-            mpd_y_d_rs + msd_y_d_rs, # All real scores
-            mpd_y_d_gs + msd_y_d_gs, # All fake scores
-            mpd_fmap_rs + msd_fmap_rs, # All real feature maps
-            mpd_fmap_gs + msd_fmap_gs  # All fake feature maps
+        layers = 4
+        self.model = nn.Sequential(
+            discriminator_block(1, layers, kernel_size=7, stride=2, dilation=1),
+            discriminator_block(layers, layers * 2, kernel_size=5, stride=2, dilation=1),
+            discriminator_block(layers * 2, layers * 4, kernel_size=3, dilation=4),
+            discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=8),
+            discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=16),
+            discriminator_block(layers * 2, layers, kernel_size=5, dilation=2),
+            discriminator_block(layers, 1, kernel_size=3, stride=1)
         )
+        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
+
+    def forward(self, x):
+        x = self.model(x)
+        x = self.global_avg_pool(x)
+        return x.view(-1, 1)

generator.py

@@ -1,126 +1,41 @@
-import torch
 import torch.nn as nn
-from torch.nn.utils.parametrizations import weight_norm
-
-def GeneratorBlock(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
-    padding = (kernel_size - 1) // 2 * dilation
 
+def conv_residual_block(in_channels, out_channels, kernel_size=3, dilation=1):
+    padding = (kernel_size // 2) * dilation
     return nn.Sequential(
-
-        weight_norm(nn.Conv1d(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            dilation=dilation,
-            padding=padding
-        )),
-        nn.PReLU(num_parameters=1, init=0.1),
-    )
-
-
-class AttentionBlock(nn.Module):
-    def __init__(self, channels):
-        super(AttentionBlock, self).__init__()
-        self.attention = nn.Sequential(
-            weight_norm(nn.Conv1d(channels, channels // 4, kernel_size=1)),
-            nn.ReLU(inplace=True),
-            weight_norm(nn.Conv1d(channels // 4, channels, kernel_size=1)),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        attention_weights = self.attention(x)
-        return x + (x * attention_weights)
-
-
-class ResidualInResidualBlock(nn.Module):
-    def __init__(self, channels, num_convs=3):
-        super(ResidualInResidualBlock, self).__init__()
-
-        self.conv_layers = nn.Sequential(
-            *[GeneratorBlock(channels, channels) for _ in range(num_convs)]
-        )
-
-        self.attention = AttentionBlock(channels)
-
-    def forward(self, x):
-        residual = x
-        x = self.conv_layers(x)
-        x = self.attention(x)
-        return x + residual
-
-def UpsampleBlock(in_channels, out_channels, scale_factor=2):
-    return nn.Sequential(
-        nn.Upsample(scale_factor=scale_factor, mode='nearest'),
-        weight_norm(nn.Conv1d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1
-        )),
-        nn.PReLU(num_parameters=1, init=0.1)
+        nn.Conv1d(in_channels, out_channels, kernel_size, dilation=dilation, padding=padding),
+        nn.BatchNorm1d(out_channels),
+        nn.PReLU(),
+        nn.Conv1d(out_channels, out_channels, kernel_size, dilation=dilation, padding=padding),
+        nn.BatchNorm1d(out_channels)
     )
 
 class SISUGenerator(nn.Module):
-    def __init__(self, channels=32, num_rirb=4):
+    def __init__(self):
         super(SISUGenerator, self).__init__()
-
-        self.first_conv = GeneratorBlock(1, channels)
-
-        self.downsample = GeneratorBlock(channels, channels * 2, stride=2)
-        self.downsample_attn = AttentionBlock(channels * 2)
-        self.downsample_2 = GeneratorBlock(channels * 2, channels * 4, stride=2)
-        self.downsample_2_attn = AttentionBlock(channels * 4)
-
-        self.rirb = nn.Sequential(
-            *[ResidualInResidualBlock(channels * 4) for _ in range(num_rirb)]
+        layers = 4
+        self.conv1 = nn.Sequential(
+            nn.Conv1d(1, layers, kernel_size=7, padding=3),
+            nn.BatchNorm1d(layers),
+            nn.PReLU()
         )
 
-        self.upsample = UpsampleBlock(channels * 4, channels * 2)
-        self.upsample_attn = AttentionBlock(channels * 2)
-        self.compress_1 = GeneratorBlock(channels * 4, channels * 2)
-
-        self.upsample_2 = UpsampleBlock(channels * 2, channels)
-        self.upsample_2_attn = AttentionBlock(channels)
-        self.compress_2 = GeneratorBlock(channels * 2, channels)
-
-        self.final_conv = nn.Sequential(
-            weight_norm(nn.Conv1d(channels, 1, kernel_size=7, padding=3)),
-            nn.Tanh()
+        self.conv_blocks = nn.Sequential(
+            conv_residual_block(layers, layers, kernel_size=3, dilation=1),
+            conv_residual_block(layers, layers * 2, kernel_size=5, dilation=2),
+            conv_residual_block(layers * 2, layers * 4, kernel_size=3, dilation=16),
+            conv_residual_block(layers * 4, layers * 2, kernel_size=5, dilation=8),
+            conv_residual_block(layers * 2, layers, kernel_size=5, dilation=2),
+            conv_residual_block(layers, layers, kernel_size=3, dilation=1)
         )
 
+        self.final_layer = nn.Sequential(
+            nn.Conv1d(layers, 1, kernel_size=3, padding=1)
+        )
 
     def forward(self, x):
-        residual_input = x
-
-        # Encoding
-        x1 = self.first_conv(x)
-
-        x2 = self.downsample(x1)
-        x2 = self.downsample_attn(x2)
-
-        x3 = self.downsample_2(x2)
-        x3 = self.downsample_2_attn(x3)
-
-        # Bottleneck (Deep Residual processing)
-        x_rirb = self.rirb(x3)
-
-        # Decoding with Skip Connections
-        up1 = self.upsample(x_rirb)
-        up1 = self.upsample_attn(up1)
-
-        cat1 = torch.cat((up1, x2), dim=1)
-        comp1 = self.compress_1(cat1)
-
-        up2 = self.upsample_2(comp1)
-        up2 = self.upsample_2_attn(up2)
-
-        cat2 = torch.cat((up2, x1), dim=1)
-        comp2 = self.compress_2(cat2)
-
-        learned_residual = self.final_conv(comp2)
-
-        output = residual_input + learned_residual
-        return output
+        residual = x
+        x = self.conv1(x)
+        x = self.conv_blocks(x) + x  # Adding residual connection after blocks
+        x = self.final_layer(x)
+        return x + residual

requirements.txt

@@ -0,0 +1,14 @@
+filelock==3.16.1
+fsspec==2024.10.0
+Jinja2==3.1.4
+MarkupSafe==2.1.5
+mpmath==1.3.0
+networkx==3.4.2
+numpy==2.2.1
+pytorch-triton-rocm==3.2.0+git0d4682f0
+setuptools==70.2.0
+sympy==1.13.1
+torch==2.6.0.dev20241222+rocm6.2.4
+torchaudio==2.6.0.dev20241222+rocm6.2.4
+tqdm==4.67.1
+typing_extensions==4.12.2

training.py

@@ -1,247 +1,164 @@
-import argparse
-import datetime
-import os
-
 import torch
 import torch.nn as nn
 import torch.optim as optim
+
+import torch.nn.functional as F
+import torchaudio
 import tqdm
-from accelerate import Accelerator
-from torch.utils.data import DataLoader, DistributedSampler
 
+import argparse
+
+import math
+
+from torch.utils.data import random_split
+from torch.utils.data import DataLoader
+
+import AudioUtils
 from data import AudioDataset
-from discriminator import SISUDiscriminator
 from generator import SISUGenerator
-from utils.TrainingTools import discriminator_train, generator_train
+from discriminator import SISUDiscriminator
+
+def perceptual_loss(y_true, y_pred):
+    return torch.mean((y_true - y_pred) ** 2)
+
+def discriminator_train(high_quality, low_quality, real_labels, fake_labels):
+    optimizer_d.zero_grad()
+
+    # Forward pass for real samples
+    discriminator_decision_from_real = discriminator(high_quality[0])
+    d_loss_real = criterion_d(discriminator_decision_from_real, real_labels)
+
+    # Forward pass for fake samples (from generator output)
+    generator_output = generator(low_quality[0])
+    discriminator_decision_from_fake = discriminator(generator_output.detach())
+    d_loss_fake = criterion_d(discriminator_decision_from_fake, fake_labels)
+
+    # Combine real and fake losses
+    d_loss = (d_loss_real + d_loss_fake) / 2.0
+
+    # Backward pass and optimization
+    d_loss.backward()
+    nn.utils.clip_grad_norm_(discriminator.parameters(), max_norm=1.0)  # Gradient Clipping
+    optimizer_d.step()
+
+    return d_loss
+
+def generator_train(low_quality, real_labels):
+    optimizer_g.zero_grad()
+
+    # Forward pass for fake samples (from generator output)
+    generator_output = generator(low_quality[0])
+    discriminator_decision = discriminator(generator_output)
+    g_loss = criterion_g(discriminator_decision, real_labels)
+
+    g_loss.backward()
+    optimizer_g.step()
+    return generator_output
+
+def first(objects):
+    if len(objects) >= 1:
+        return objects[0]
+    return objects
+
+# 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")
 
-# ---------------------------
-# Argument parsing
-# ---------------------------
-parser = argparse.ArgumentParser(description="Training script (safer defaults)")
-parser.add_argument("--resume", action="store_true", help="Resume training")
-parser.add_argument(
-    "--epochs", type=int, default=5000, help="Number of training epochs"
-)
-parser.add_argument("--batch_size", type=int, default=8, help="Batch size")
-parser.add_argument("--num_workers", type=int, default=4, help="DataLoader num_workers") # Increased workers slightly
-parser.add_argument("--debug", action="store_true", help="Print debug logs")
-parser.add_argument(
-    "--no_pin_memory", action="store_true", help="Disable pin_memory even on CUDA"
-)
 args = parser.parse_args()
 
-# ---------------------------
-# Init accelerator
-# ---------------------------
+# Check for CUDA availability
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
 
-accelerator = Accelerator(mixed_precision="bf16")
+# Initialize dataset and dataloader
+dataset_dir = './dataset/good'
+dataset = AudioDataset(dataset_dir)
 
-# ---------------------------
-# Models
-# ---------------------------
+# ========= SINGLE =========
+
+train_data_loader = DataLoader(dataset, batch_size=16, shuffle=True)
+
+# Initialize models and move them to device
 generator = SISUGenerator()
-# Note: SISUDiscriminator is now an Ensemble (MPD + MSD)
 discriminator = SISUDiscriminator()
 
-accelerator.print("🔨 | Compiling models...")
+if args.generator is not None:
+    generator.load_state_dict(torch.load(args.generator, weights_only=True))
+if args.discriminator is not None:
+    discriminator.load_state_dict(torch.load(args.discriminator, weights_only=True))
 
-# Torch compile is great, but if you hit errors with the new List/Tuple outputs
-# of the discriminator, you might need to disable it for D.
-generator = torch.compile(generator)
-discriminator = torch.compile(discriminator)
+generator = generator.to(device)
+discriminator = discriminator.to(device)
 
-accelerator.print("✅ | Compiling done!")
+# Loss
+criterion_g = nn.MSELoss()
+criterion_d = nn.BCELoss()
 
-# ---------------------------
-# Dataset / DataLoader
-# ---------------------------
-accelerator.print("📊 | Fetching dataset...")
-dataset = AudioDataset("./dataset", 8192)
+# 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))
 
-sampler = DistributedSampler(dataset) if accelerator.num_processes > 1 else None
-pin_memory = torch.cuda.is_available() and not args.no_pin_memory
+# 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)
 
-train_loader = DataLoader(
-    dataset,
-    sampler=sampler,
-    batch_size=args.batch_size,
-    shuffle=(sampler is None),
-    num_workers=args.num_workers,
-    pin_memory=pin_memory,
-    persistent_workers=pin_memory,
-)
+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)
 
-if not train_loader or not train_loader.batch_size or train_loader.batch_size == 0:
-    accelerator.print("🪹 | There is no data to train with! Exiting...")
-    exit()
+        times_correct = 0
 
-loader_batch_size = train_loader.batch_size
+        # ========= TRAINING =========
+        for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Epoch {generator_epoch+1}/{generator_epochs}"):
+        # for high_quality_clip, low_quality_clip in train_data_loader:
+            high_quality_sample = (high_quality_clip[0].to(device), high_quality_clip[1])
+            low_quality_sample = (low_quality_clip[0].to(device), low_quality_clip[1])
 
-accelerator.print("✅ | Dataset fetched!")
+            # ========= 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)
 
-# ---------------------------
-# Losses / Optimizers / Scalers
-# ---------------------------
-
-optimizer_g = optim.AdamW(
-    generator.parameters(), lr=0.0003, betas=(0.5, 0.999), weight_decay=0.0001
-)
-optimizer_d = optim.AdamW(
-    discriminator.parameters(), lr=0.0003, betas=(0.5, 0.999), weight_decay=0.0001
-)
-
-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
-)
-
-# ---------------------------
-# Prepare accelerator
-# ---------------------------
-
-generator, discriminator, optimizer_g, optimizer_d, train_loader = accelerator.prepare(
-    generator, discriminator, optimizer_g, optimizer_d, train_loader
-)
-
-# ---------------------------
-# Checkpoint helpers
-# ---------------------------
-models_dir = "./models"
-os.makedirs(models_dir, exist_ok=True)
-
-
-def save_ckpt(path, epoch, loss=None, is_best=False):
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        state = {
-            "epoch": epoch,
-            "G": accelerator.unwrap_model(generator).state_dict(),
-            "D": accelerator.unwrap_model(discriminator).state_dict(),
-            "optG": optimizer_g.state_dict(),
-            "optD": optimizer_d.state_dict(),
-            "schedG": scheduler_g.state_dict(),
-            "schedD": scheduler_d.state_dict()
-        }
-
-        accelerator.save(state, os.path.join(models_dir, "last.pt"))
-
-        if is_best:
-            accelerator.save(state, os.path.join(models_dir, "best.pt"))
-            accelerator.print(f"🌟 | New best model saved with G Loss: {loss:.4f}")
-
-
-start_epoch = 0
-if args.resume:
-    ckpt_path = os.path.join(models_dir, "last.pt")
-    if os.path.exists(ckpt_path):
-        ckpt = torch.load(ckpt_path)
-
-        accelerator.unwrap_model(generator).load_state_dict(ckpt["G"])
-        accelerator.unwrap_model(discriminator).load_state_dict(ckpt["D"])
-        optimizer_g.load_state_dict(ckpt["optG"])
-        optimizer_d.load_state_dict(ckpt["optD"])
-        scheduler_g.load_state_dict(ckpt["schedG"])
-        scheduler_d.load_state_dict(ckpt["schedD"])
-
-        start_epoch = ckpt.get("epoch", 1)
-        accelerator.print(f"🔁 | Resumed from epoch {start_epoch}!")
-    else:
-        accelerator.print("⚠️ | Resume requested but no checkpoint found. Starting fresh.")
-
-
-accelerator.print("🏋️ | Started training...")
-
-try:
-    for epoch in range(start_epoch, args.epochs):
-        generator.train()
+            # ========= DISCRIMINATOR =========
             discriminator.train()
+            discriminator_train(high_quality_sample, low_quality_sample, real_labels, fake_labels)
 
-        discriminator_time = 0
-        generator_time = 0
+            # ========= GENERATOR =========
+            generator.train()
+            generator_output = generator_train(low_quality_sample, real_labels)
 
-        running_d, running_g, steps = 0.0, 0.0, 0
+            # ========= SAVE LATEST AUDIO =========
+            high_quality_audio = (first(high_quality_clip[0]), high_quality_clip[1][0])
+            low_quality_audio = (first(low_quality_clip[0]), low_quality_clip[1][0])
+            ai_enhanced_audio = (first(generator_output[0]), high_quality_clip[1][0])
+            print(high_quality_audio)
 
-        progress_bar = tqdm.tqdm(train_loader, desc=f"Epoch {epoch} | D {discriminator_time}μs | G {generator_time}μs")
+            print(f"Saved epoch {generator_epoch}!")
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-crap.wav", low_quality_audio[0][0].cpu(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-ai.wav", ai_enhanced_audio[0][0].cpu(), ai_enhanced_audio[1])
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-orig.wav", high_quality_audio[0][0].cpu(), high_quality_audio[1])
 
-        for i, (
-            (high_quality, low_quality),
-            (high_sample_rate, low_sample_rate),
-        ) in enumerate(progress_bar):
-            with accelerator.autocast():
-                generator_output = generator(low_quality)
+        #metric = snr(high_quality_audio[0].to(device), ai_enhanced_audio[0])
+        #print(f"Generator metric {metric}!")
+        #scheduler_g.step(metric)
 
-            # --- Discriminator ---
-            d_time = datetime.datetime.now()
-            optimizer_d.zero_grad(set_to_none=True)
-            with accelerator.autocast():
-                d_loss = discriminator_train(
-                    high_quality,
-                    discriminator,
-                    generator_output.detach()
-                )
+        if generator_epoch % 10 == 0:
+            print(f"Saved epoch {generator_epoch}!")
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-crap.wav", low_quality_audio[0][0].cpu(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-ai.wav", ai_enhanced_audio[0][0].cpu(), ai_enhanced_audio[1])
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-orig.wav", high_quality_audio[0][0].cpu(), high_quality_audio[1])
 
-            accelerator.backward(d_loss)
-            optimizer_d.step()
-            discriminator_time = (datetime.datetime.now() - d_time).microseconds
+        torch.save(discriminator.state_dict(), f"models/current-epoch-discriminator.pt")
+        torch.save(generator.state_dict(), f"models/current-epoch-generator.pt")
 
-            # --- Generator ---
-            g_time = datetime.datetime.now()
-            optimizer_g.zero_grad(set_to_none=True)
-            with accelerator.autocast():
-                g_total, g_adv = generator_train(
-                    low_quality,
-                    high_quality,
-                    generator,
-                    discriminator,
-                    generator_output
-                )
+    torch.save(discriminator.state_dict(), "models/epoch-5000-discriminator.pt")
+    torch.save(generator.state_dict(), "models/epoch-5000-generator.pt")
+    print("Training complete!")
 
-            accelerator.backward(g_total)
-            torch.nn.utils.clip_grad_norm_(generator.parameters(), 1)
-            optimizer_g.step()
-            generator_time = (datetime.datetime.now() - g_time).microseconds
-
-            d_val = accelerator.gather(d_loss.detach()).mean()
-            g_val = accelerator.gather(g_total.detach()).mean()
-
-            if torch.isfinite(d_val):
-                running_d += d_val.item()
-            else:
-                accelerator.print(
-                    f"🫥 | NaN in discriminator loss at step {i}, skipping update."
-                )
-
-            if torch.isfinite(g_val):
-                running_g += g_val.item()
-            else:
-                accelerator.print(
-                    f"🫥 | NaN in generator loss at step {i}, skipping update."
-                )
-
-            steps += 1
-            progress_bar.set_description(f"Epoch {epoch} | D {discriminator_time}μs | G {generator_time}μs")
-
-        if steps == 0:
-            accelerator.print("🪹 | No steps in epoch (empty dataloader?). Exiting.")
-            break
-
-        mean_d = running_d / steps
-        mean_g = running_g / steps
-
-        scheduler_d.step(mean_d)
-        scheduler_g.step(mean_g)
-
-        save_ckpt(os.path.join(models_dir, "last.pt"), epoch)
-        accelerator.print(f"🤝 | Epoch {epoch} done | D {mean_d:.4f} | G {mean_g:.4f}")
-
-except Exception:
-    try:
-        save_ckpt(os.path.join(models_dir, "crash_last.pt"), epoch)
-        accelerator.print(f"💾 | Saved crash checkpoint for epoch {epoch}")
-    except Exception as e:
-        accelerator.print("😬 | Failed saving crash checkpoint:", e)
-    raise
-
-accelerator.print("🏁 | Training finished.")
+start_training()

utils/MultiResolutionSTFTLoss.py

@@ -1,68 +0,0 @@
-from typing import Dict, List
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchaudio.transforms as T
-
-
-class MultiResolutionSTFTLoss(nn.Module):
-    def __init__(
-        self,
-        fft_sizes: List[int] = [512, 1024, 2048, 4096, 8192],
-        hop_sizes: List[int] = [64, 128, 256, 512, 1024],
-        win_lengths: List[int] = [256, 512, 1024, 2048, 4096],
-        eps: float = 1e-7,
-        center: bool = True
-    ):
-        super().__init__()
-
-        self.eps = eps
-        self.n_resolutions = len(fft_sizes)
-
-        self.stft_transforms = nn.ModuleList()
-        for i, (n_fft, hop_len, win_len) in enumerate(zip(fft_sizes, hop_sizes, win_lengths)):
-            stft = T.Spectrogram(
-                n_fft=n_fft,
-                hop_length=hop_len,
-                win_length=win_len,
-                window_fn=torch.hann_window,
-                power=None,
-                center=center,
-                pad_mode="reflect",
-                normalized=False,
-            )
-            self.stft_transforms.append(stft)
-
-    def forward(
-        self, y_true: torch.Tensor, y_pred: torch.Tensor
-    ) -> Dict[str, torch.Tensor]:
-        if y_true.dim() == 3 and y_true.size(1) == 1:
-            y_true = y_true.squeeze(1)
-        if y_pred.dim() == 3 and y_pred.size(1) == 1:
-            y_pred = y_pred.squeeze(1)
-
-        sc_loss = 0.0
-        mag_loss = 0.0
-
-        for stft in self.stft_transforms:
-            stft.window = stft.window.to(y_true.device)
-            stft_true = stft(y_true)
-            stft_pred = stft(y_pred)
-
-            stft_mag_true = torch.abs(stft_true)
-            stft_mag_pred = torch.abs(stft_pred)
-
-            norm_true = torch.linalg.norm(stft_mag_true, dim=(-2, -1))
-            norm_diff = torch.linalg.norm(stft_mag_true - stft_mag_pred, dim=(-2, -1))
-            sc_loss += torch.mean(norm_diff / (norm_true + self.eps))
-
-            log_mag_pred = torch.log(stft_mag_pred + self.eps)
-            log_mag_true = torch.log(stft_mag_true + self.eps)
-            mag_loss += F.l1_loss(log_mag_pred, log_mag_true)
-
-        sc_loss /= self.n_resolutions
-        mag_loss /= self.n_resolutions
-        total_loss = sc_loss + mag_loss
-
-        return {"total": total_loss, "sc": sc_loss, "mag": mag_loss}

utils/TrainingTools.py

@@ -1,93 +0,0 @@
-import torch
-import torch.nn.functional as F
-from utils.MultiResolutionSTFTLoss import MultiResolutionSTFTLoss
-
-# Keep STFT settings as is
-stft_loss_fn = MultiResolutionSTFTLoss(
-    fft_sizes=[512, 1024, 2048],
-    hop_sizes=[64, 128, 256],
-    win_lengths=[256, 512, 1024]
-)
-
-
-def feature_matching_loss(fmap_r, fmap_g):
-    """
-    Computes L1 distance between real and fake feature maps.
-    """
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            rl = rl.detach()
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    """
-    Least Squares GAN Loss (LSGAN) for the Discriminator.
-    Objective: Real -> 1, Fake -> 0
-    """
-    loss = 0
-    r_losses = []
-    g_losses = []
-
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((dr - 1) ** 2)
-        g_loss = torch.mean(dg ** 2)
-
-        loss += (r_loss + g_loss)
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_adv_loss(disc_generated_outputs):
-    """
-    Least Squares GAN Loss for the Generator.
-    Objective: Fake -> 1 (Fool the discriminator)
-    """
-    loss = 0
-    for dg in zip(disc_generated_outputs):
-        dg = dg[0] # Unpack tuple
-        loss += torch.mean((dg - 1) ** 2)
-    return loss
-
-
-def discriminator_train(
-    high_quality,
-    discriminator,
-    generator_output
-):
-    y_d_rs, y_d_gs, _, _ = discriminator(high_quality, generator_output.detach())
-
-    d_loss, _, _ = discriminator_loss(y_d_rs, y_d_gs)
-
-    return d_loss
-
-
-def generator_train(
-    low_quality,
-    high_quality,
-    generator,
-    discriminator,
-    generator_output
-):
-    y_d_rs, y_d_gs, fmap_rs, fmap_gs = discriminator(high_quality, generator_output)
-
-    loss_gen_adv = generator_adv_loss(y_d_gs)
-
-    loss_fm = feature_matching_loss(fmap_rs, fmap_gs)
-
-    stft_loss = stft_loss_fn(high_quality, generator_output)["total"]
-
-    lambda_stft = 45.0
-    lambda_fm = 2.0
-    lambda_adv = 1.0
-
-    combined_loss = (lambda_stft * stft_loss) + \
-                    (lambda_fm * loss_fm) + \
-                    (lambda_adv * loss_gen_adv)
-
-    return combined_loss, loss_gen_adv