ignore staff

.gitignore

@@ -1,4 +1,4 @@
 .idea/**
 .ipynb_checkpoints/**
 nohup.out
-__pycache__/
+__pycache__/**

denoisers/.ipynb_checkpoints/SpectralGating-checkpoint.py

@@ -1,26 +0,0 @@
-import noisereduce as nr
-import torch
-import torchaudio
-
-
-class SpectralGating(torch.nn.Module):
-    """example: wav_noisy = '/media/public/datasets/denoising/DS_10283_2791/noisy_trainset_56spk_wav/p312_002.wav' """
-    def __init__(self, rate=16000):
-        super(SpectralGating, self).__init__()
-        self.rate = rate
-
-    def forward(self, wav):
-        reduced_noise = torch.Tensor(nr.reduce_noise(y=wav, sr=self.rate))
-        return reduced_noise
-    
-    def predict(self, wav_path, out_path):
-        data, rate = torchaudio.load(wav_path)
-        reduced_noise = torch.Tensor(nr.reduce_noise(y=data, sr=rate))
-        torchaudio.save(out_path, reduced_noise, rate)
-        return reduced_noise
-
-    
-    
-
-
-

denoisers/.ipynb_checkpoints/demucs-checkpoint.py

@@ -1,67 +0,0 @@
-import torch
-
-
-class Encoder(torch.nn.Module):
-    def __init__(self, in_channels, out_channels,
-                 kernel_size_1=8, stride_1=4,
-                 kernel_size_2=1, stride_2=1):
-        super(Encoder, self).__init__()
-
-        self.conv1 = torch.nn.Conv1d(in_channels=in_channels, out_channels=out_channels,
-                                     kernel_size=kernel_size_1, stride=stride_1)
-        self.relu1 = torch.nn.ReLU()
-        self.conv2 = torch.nn.Conv1d(in_channels=out_channels, out_channels=2 * out_channels,
-                                     kernel_size=kernel_size_2, stride=stride_2)
-        self.glu = torch.nn.GLU()
-
-    def forward(self, x):
-        x = self.relu1(self.conv1(x))
-        x = self.glu(self.conv2(x))
-        return x
-
-
-class Decoder(torch.nn.Module):
-    def __init__(self, in_channels, out_channels,
-                 kernel_size_1=3, stride_1=1,
-                 kernel_size_2=8, stride_2=4):
-        super(Decoder, self).__init__()
-
-        self.conv1 = torch.nn.Conv1d(in_channels=in_channels, out_channels=2 * in_channels,
-                                     kernel_size=kernel_size_1, stride=stride_1)
-        self.glu = torch.nn.GLU()
-        self.conv2 = torch.nn.ConvTranspose1d(in_channels=in_channels, out_channels=out_channels,
-                                              kernel_size=kernel_size_2, stride=stride_2)
-        self.relu = torch.nn.ReLU()
-
-    def forward(self, x):
-        x = self.glu(self.conv1(x))
-        x = self.relu(self.conv2(x))
-        return x
-
-
-class Demucs(torch.nn.Module):
-    def __init__(self):
-        super(Demucs, self).__init__()
-
-        self.encoder1 = Encoder(in_channels=1, out_channels=64)
-        self.encoder2 = Encoder(in_channels=64, out_channels=128)
-        self.encoder3 = Encoder(in_channels=128, out_channels=256)
-
-        self.lstm = torch.nn.LSTM(input_size=256, hidden_size=256, num_layers=2)
-
-        self.decoder1 = Decoder(in_channels=256, out_channels=128)
-        self.decoder2 = Decoder(in_channels=128, out_channels=64)
-        self.decoder3 = Decoder(in_channels=64, out_channels=1)
-
-    def forward(self, x):
-        out1 = self.encoder1(x)
-        out2 = self.encoder2(out1)
-        out3 = self.encoder3(out2)
-
-        x = self.lstm(out3)
-
-        x = self.decoder1(x + out3)
-        x = self.decoder2(x + out2)
-        x = self.decoder3(x + out1)
-
-        return x