⚗️ | Experimenting still...
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18
AudioUtils.py
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18
AudioUtils.py
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@ -0,0 +1,18 @@
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import torch
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import torch.nn.functional as F
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def stereo_tensor_to_mono(waveform):
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if waveform.shape[0] > 1:
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# Average across channels
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mono_waveform = torch.mean(waveform, dim=0, keepdim=True)
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else:
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# Already mono
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mono_waveform = waveform
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return mono_waveform
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def stretch_tensor(tensor, target_length):
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scale_factor = target_length / tensor.size(1)
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tensor = F.interpolate(tensor, scale_factor=scale_factor, mode='linear', align_corners=False)
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return tensor
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36
data.py
36
data.py
@ -1,49 +1,31 @@
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from torch.utils.data import Dataset
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import torch.nn.functional as F
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import torch
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import torchaudio
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import os
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import random
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import torchaudio.transforms as T
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import AudioUtils
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class AudioDataset(Dataset):
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audio_sample_rates = [8000, 11025, 16000, 22050]
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#audio_sample_rates = [8000, 11025, 16000, 22050]
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audio_sample_rates = [11025]
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def __init__(self, input_dir, target_duration=None, padding_mode='constant', padding_value=0.0):
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def __init__(self, input_dir):
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self.input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith('.wav')]
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self.target_duration = target_duration # Duration in seconds or None if not set
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self.padding_mode = padding_mode
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self.padding_value = padding_value
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def __len__(self):
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return len(self.input_files)
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def __getitem__(self, idx):
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# Load high-quality audio
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high_quality_audio, original_sample_rate = torchaudio.load(self.input_files[idx], normalize=True)
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# Generate low-quality audio with random downsampling
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mangled_sample_rate = random.choice(self.audio_sample_rates)
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resample_transform = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
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low_quality_audio = resample_transform(high_quality_audio)
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# Calculate target length based on desired duration and 16000 Hz
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# if self.target_duration is not None:
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# target_length = int(self.target_duration * 44100)
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# else:
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# # Calculate duration of original high quality audio
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# target_length = high_quality_wav.size(1)
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# Pad both to the calculated target length
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# high_quality_wav = self.stretch_tensor(high_quality_wav, target_length)
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# low_quality_wav = self.stretch_tensor(low_quality_wav, target_length)
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return (high_quality_audio, original_sample_rate), (low_quality_audio, mangled_sample_rate)
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def stretch_tensor(self, tensor, target_length):
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current_length = tensor.size(1)
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scale_factor = target_length / current_length
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# Resample the tensor using linear interpolation
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tensor = F.interpolate(tensor.unsqueeze(0), scale_factor=scale_factor, mode='linear', align_corners=False).squeeze(0)
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return tensor
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return (AudioUtils.stereo_tensor_to_mono(high_quality_audio), original_sample_rate), (AudioUtils.stereo_tensor_to_mono(low_quality_audio), mangled_sample_rate)
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@ -3,22 +3,28 @@ import torch.nn as nn
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class SISUDiscriminator(nn.Module):
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def __init__(self):
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super(SISUDiscriminator, self).__init__()
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layers = 32
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self.model = nn.Sequential(
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nn.Conv1d(2, 128, kernel_size=3, padding=1),
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#nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(128, 256, kernel_size=3, padding=1),
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nn.Conv1d(1, layers, kernel_size=5, stride=2, padding=2),
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nn.BatchNorm1d(layers),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(256, 128, kernel_size=3, padding=1),
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#nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(128, 64, kernel_size=3, padding=1),
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#nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(64, 1, kernel_size=3, padding=1),
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#nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(layers, layers * 2, kernel_size=5, stride=2, padding=2),
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nn.BatchNorm1d(layers * 2),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(layers * 2, layers * 4, kernel_size=5, stride=2, padding=2),
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nn.BatchNorm1d(layers * 4),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(layers * 4, layers * 8, kernel_size=5, stride=2, padding=2),
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nn.BatchNorm1d(layers * 8),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(layers * 8, 1, kernel_size=3, padding=1),
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)
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self.global_avg_pool = nn.AdaptiveAvgPool1d(1) # Output size (1,)
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self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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x = self.model(x)
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x = self.global_avg_pool(x)
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x = x.view(-1, 1) # Flatten to (batch_size, 1)
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x = x.view(-1, 1)
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x = self.sigmoid(x)
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return x
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53
generator.py
53
generator.py
@ -1,39 +1,32 @@
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import torch.nn as nn
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class SISUGenerator(nn.Module):
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def __init__(self, upscale_scale=1):
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def __init__(self, upscale_scale=4): # No noise_dim parameter
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super(SISUGenerator, self).__init__()
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self.layers1 = nn.Sequential(
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nn.Conv1d(2, 128, kernel_size=3, padding=1),
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nn.LeakyReLU(0.2, inplace=True), # Activation
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nn.BatchNorm1d(128), # Batch Norm
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nn.Conv1d(128, 256, kernel_size=3, padding=1),
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nn.LeakyReLU(0.2, inplace=True), # Activation
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nn.BatchNorm1d(256), # Batch Norm
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layer = 32
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# Convolution layers
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self.conv1 = nn.Sequential(
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nn.Conv1d(1, layer * 2, kernel_size=7, padding=1),
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nn.PReLU(),
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nn.Conv1d(layer * 2, layer * 5, kernel_size=5, padding=1),
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nn.PReLU(),
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nn.Conv1d(layer * 5, layer * 5, kernel_size=3, padding=1),
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nn.PReLU()
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)
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self.layers2 = nn.Sequential(
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nn.Conv1d(256, 128, kernel_size=3, padding=1),
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nn.LeakyReLU(0.2, inplace=True), # Activation
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nn.BatchNorm1d(128), # Batch Norm
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nn.Conv1d(128, 64, kernel_size=3, padding=1),
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nn.LeakyReLU(0.2, inplace=True), # Activation
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nn.BatchNorm1d(64), # Batch Norm
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nn.Conv1d(64, upscale_scale * 2, kernel_size=3, padding=1), # Output channels scaled
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# Transposed convolution for upsampling
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self.upsample = nn.ConvTranspose1d(layer * 5, layer * 5, kernel_size=upscale_scale, stride=upscale_scale)
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self.conv2 = nn.Sequential(
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nn.Conv1d(layer * 5, layer * 5, kernel_size=3, padding=1),
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nn.PReLU(),
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nn.Conv1d(layer * 5, layer * 2, kernel_size=5, padding=1),
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nn.PReLU(),
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nn.Conv1d(layer * 2, 1, kernel_size=7, padding=1)
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)
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self.upscale_factor = upscale_scale
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def pixel_shuffle_1d(self, input, upscale_factor):
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batch_size, channels, in_width = input.size()
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out_width = in_width * upscale_factor
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input_view = input.contiguous().view(batch_size, channels // upscale_factor, upscale_factor, in_width)
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shuffle_out = input_view.permute(0, 1, 3, 2).contiguous()
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return shuffle_out.view(batch_size, channels // upscale_factor, out_width)
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def forward(self, x, scale):
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x = self.layers1(x)
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upsample = nn.Upsample(scale_factor=scale, mode='nearest')
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x = upsample(x)
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x = self.layers2(x)
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x = self.pixel_shuffle_1d(x, self.upscale_factor)
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def forward(self, x, upscale_scale=4):
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x = self.conv1(x)
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x = self.upsample(x)
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x = self.conv2(x)
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return x
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@ -1,12 +1,14 @@
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filelock>=3.16.1
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fsspec>=2024.10.0
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Jinja2>=3.1.4
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MarkupSafe>=2.1.5
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mpmath>=1.3.0
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networkx>=3.4.2
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numpy>=2.1.2
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pillow>=11.0.0
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setuptools>=70.2.0
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sympy>=1.13.1
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tqdm>=4.67.1
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typing_extensions>=4.12.2
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filelock==3.16.1
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fsspec==2024.10.0
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Jinja2==3.1.4
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MarkupSafe==2.1.5
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mpmath==1.3.0
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networkx==3.4.2
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numpy==2.2.1
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pytorch-triton-rocm==3.2.0+git0d4682f0
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setuptools==70.2.0
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sympy==1.13.1
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torch==2.6.0.dev20241222+rocm6.2.4
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torchaudio==2.6.0.dev20241222+rocm6.2.4
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tqdm==4.67.1
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typing_extensions==4.12.2
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155
training.py
155
training.py
@ -6,66 +6,73 @@ import torch.nn.functional as F
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import torchaudio
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import tqdm
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import argparse
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import math
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from torch.utils.data import random_split
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from torch.utils.data import DataLoader
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import AudioUtils
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from data import AudioDataset
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from generator import SISUGenerator
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from discriminator import SISUDiscriminator
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# Mel Spectrogram Loss
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class MelSpectrogramLoss(nn.Module):
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def __init__(self, sample_rate=44100, n_fft=2048, hop_length=512, n_mels=128):
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super(MelSpectrogramLoss, self).__init__()
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self.mel_transform = torchaudio.transforms.MelSpectrogram(
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sample_rate=sample_rate,
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n_fft=n_fft,
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hop_length=hop_length,
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n_mels=n_mels
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).to(device) # Move to device
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def perceptual_loss(y_true, y_pred):
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return torch.mean((y_true - y_pred) ** 2)
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def forward(self, y_pred, y_true):
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mel_pred = self.mel_transform(y_pred)
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mel_true = self.mel_transform(y_true)
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return F.l1_loss(mel_pred, mel_true)
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def snr(y_true, y_pred):
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noise = y_true - y_pred
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signal_power = torch.mean(y_true ** 2)
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noise_power = torch.mean(noise ** 2)
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snr_db = 10 * torch.log10(signal_power / noise_power)
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return snr_db
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def discriminator_train(high_quality, low_quality, scale, real_labels, fake_labels):
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def discriminator_train(high_quality, low_quality, real_labels, fake_labels):
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optimizer_d.zero_grad()
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discriminator_decision_from_real = discriminator(high_quality)
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# TODO: Experiment with criterions HERE!
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# Forward pass for real samples
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discriminator_decision_from_real = discriminator(high_quality[0])
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d_loss_real = criterion_d(discriminator_decision_from_real, real_labels)
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generator_output = generator(low_quality, scale)
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discriminator_decision_from_fake = discriminator(generator_output.detach())
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# TODO: Experiment with criterions HERE!
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integer_scale = math.ceil(high_quality[1]/low_quality[1])
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# Forward pass for fake samples (from generator output)
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generator_output = generator(low_quality[0], integer_scale)
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resample_transform = torchaudio.transforms.Resample(low_quality[1] * integer_scale, high_quality[1]).to(device)
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resampled = resample_transform(generator_output.detach())
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discriminator_decision_from_fake = discriminator(resampled)
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d_loss_fake = criterion_d(discriminator_decision_from_fake, fake_labels)
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# Combine real and fake losses
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d_loss = (d_loss_real + d_loss_fake) / 2.0
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# Backward pass and optimization
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d_loss.backward()
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nn.utils.clip_grad_norm_(discriminator.parameters(), max_norm=1.0) #Gradient Clipping
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nn.utils.clip_grad_norm_(discriminator.parameters(), max_norm=1.0) # Gradient Clipping
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optimizer_d.step()
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return d_loss
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def generator_train(low_quality, scale, real_labels):
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def generator_train(low_quality, real_labels, target_sample_rate=44100):
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optimizer_g.zero_grad()
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generator_output = generator(low_quality, scale)
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discriminator_decision = discriminator(generator_output)
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# TODO: Fix this shit
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scale = math.ceil(target_sample_rate/low_quality[1])
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# Forward pass for fake samples (from generator output)
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generator_output = generator(low_quality[0], scale)
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resample_transform = torchaudio.transforms.Resample(low_quality[1] * scale, target_sample_rate).to(device)
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resampled = resample_transform(generator_output)
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discriminator_decision = discriminator(resampled)
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g_loss = criterion_g(discriminator_decision, real_labels)
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g_loss.backward()
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optimizer_g.step()
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return generator_output
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return resampled
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# Init script argument parser
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parser = argparse.ArgumentParser(description="Training script")
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parser.add_argument("--generator", type=str, default=None,
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help="Path to the generator model file")
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parser.add_argument("--discriminator", type=str, default=None,
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help="Path to the discriminator model file")
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args = parser.parse_args()
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# Check for CUDA availability
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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@ -73,28 +80,38 @@ print(f"Using device: {device}")
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# Initialize dataset and dataloader
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dataset_dir = './dataset/good'
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dataset = AudioDataset(dataset_dir, target_duration=2.0)
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dataset = AudioDataset(dataset_dir)
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dataset_size = len(dataset)
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train_size = int(dataset_size * .9)
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val_size = int(dataset_size-train_size)
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# ========= MULTIPLE =========
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train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
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# dataset_size = len(dataset)
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# train_size = int(dataset_size * .9)
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# val_size = int(dataset_size-train_size)
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train_data_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)
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val_data_loader = DataLoader(val_dataset, batch_size=1, shuffle=True)
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#train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
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# train_data_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)
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# val_data_loader = DataLoader(val_dataset, batch_size=1, shuffle=True)
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# ========= SINGLE =========
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train_data_loader = DataLoader(dataset, batch_size=1, shuffle=True)
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# Initialize models and move them to device
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generator = SISUGenerator()
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discriminator = SISUDiscriminator()
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if args.generator is not None:
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generator.load_state_dict(torch.load(args.generator, weights_only=True))
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if args.discriminator is not None:
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discriminator.load_state_dict(torch.load(args.discriminator, weights_only=True))
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generator = generator.to(device)
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discriminator = discriminator.to(device)
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# Loss
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criterion_g = nn.L1Loss()
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criterion_g_mel = MelSpectrogramLoss().to(device)
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criterion_d = nn.BCEWithLogitsLoss()
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criterion_d = nn.BCELoss()
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# Optimizers
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optimizer_g = optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
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@ -109,39 +126,40 @@ def start_training():
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# Training loop
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# ========= DISCRIMINATOR PRE-TRAINING =========
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discriminator_epochs = 1
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for discriminator_epoch in range(discriminator_epochs):
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# discriminator_epochs = 1
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# for discriminator_epoch in range(discriminator_epochs):
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# ========= TRAINING =========
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for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Epoch {discriminator_epoch+1}/{discriminator_epochs}"):
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high_quality_sample = high_quality_clip[0].to(device)
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low_quality_sample = low_quality_clip[0].to(device)
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# # ========= TRAINING =========
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# for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Epoch {discriminator_epoch+1}/{discriminator_epochs}"):
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# high_quality_sample = high_quality_clip[0].to(device)
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# low_quality_sample = low_quality_clip[0].to(device)
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scale = high_quality_clip[0].shape[2]/low_quality_clip[0].shape[2]
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# scale = high_quality_clip[0].shape[2]/low_quality_clip[0].shape[2]
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# ========= LABELS =========
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batch_size = high_quality_sample.size(0)
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real_labels = torch.ones(batch_size, 1).to(device)
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fake_labels = torch.zeros(batch_size, 1).to(device)
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# # ========= LABELS =========
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# batch_size = high_quality_sample.size(0)
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# real_labels = torch.ones(batch_size, 1).to(device)
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# fake_labels = torch.zeros(batch_size, 1).to(device)
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# ========= DISCRIMINATOR =========
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discriminator.train()
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discriminator_train(high_quality_sample, low_quality_sample, scale, real_labels, fake_labels)
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||||
# # ========= DISCRIMINATOR =========
|
||||
# discriminator.train()
|
||||
# discriminator_train(high_quality_sample, low_quality_sample, scale, real_labels, fake_labels)
|
||||
|
||||
torch.save(discriminator.state_dict(), "models/discriminator-single-shot-pre-train.pt")
|
||||
# torch.save(discriminator.state_dict(), "models/discriminator-single-shot-pre-train.pt")
|
||||
|
||||
generator_epochs = 500
|
||||
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"Epoch {generator_epoch+1}/{generator_epochs}"):
|
||||
high_quality_sample = high_quality_clip[0].to(device)
|
||||
low_quality_sample = low_quality_clip[0].to(device)
|
||||
|
||||
scale = high_quality_clip[0].shape[2]/low_quality_clip[0].shape[2]
|
||||
# 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])
|
||||
|
||||
# ========= LABELS =========
|
||||
batch_size = high_quality_clip[0].size(0)
|
||||
@ -150,21 +168,20 @@ def start_training():
|
||||
|
||||
# ========= DISCRIMINATOR =========
|
||||
discriminator.train()
|
||||
for _ in range(3):
|
||||
discriminator_train(high_quality_sample, low_quality_sample, scale, real_labels, fake_labels)
|
||||
discriminator_train(high_quality_sample, low_quality_sample, real_labels, fake_labels)
|
||||
|
||||
# ========= GENERATOR =========
|
||||
generator.train()
|
||||
generator_output = generator_train(low_quality_sample, scale, real_labels)
|
||||
generator_output = generator_train(low_quality_sample, real_labels, high_quality_sample[1])
|
||||
|
||||
# ========= SAVE LATEST AUDIO =========
|
||||
high_quality_audio = high_quality_clip
|
||||
low_quality_audio = low_quality_clip
|
||||
ai_enhanced_audio = (generator_output, high_quality_clip[1])
|
||||
|
||||
metric = snr(high_quality_audio[0].to(device), ai_enhanced_audio[0])
|
||||
print(f"Generator metric {metric}!")
|
||||
scheduler_g.step(metric)
|
||||
#metric = snr(high_quality_audio[0].to(device), ai_enhanced_audio[0])
|
||||
#print(f"Generator metric {metric}!")
|
||||
#scheduler_g.step(metric)
|
||||
|
||||
if generator_epoch % 10 == 0:
|
||||
print(f"Saved epoch {generator_epoch}!")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user