Introduce code from Rust monorepo. This is a mess

tts/tacotron2/layers.py

@@ -0,0 +1,80 @@
+import torch
+from librosa.filters import mel as librosa_mel_fn
+from tacotron2.audio_processing import dynamic_range_compression
+from tacotron2.audio_processing import dynamic_range_decompression
+from tacotron2.stft import STFT
+
+
+class LinearNorm(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
+        super(LinearNorm, self).__init__()
+        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.linear_layer.weight,
+            gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, x):
+        return self.linear_layer(x)
+
+
+class ConvNorm(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
+                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
+        super(ConvNorm, self).__init__()
+        if padding is None:
+            assert(kernel_size % 2 == 1)
+            padding = int(dilation * (kernel_size - 1) / 2)
+
+        self.conv = torch.nn.Conv1d(in_channels, out_channels,
+                                    kernel_size=kernel_size, stride=stride,
+                                    padding=padding, dilation=dilation,
+                                    bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, signal):
+        conv_signal = self.conv(signal)
+        return conv_signal
+
+
+class TacotronSTFT(torch.nn.Module):
+    def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
+                 n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
+                 mel_fmax=8000.0):
+        super(TacotronSTFT, self).__init__()
+        self.n_mel_channels = n_mel_channels
+        self.sampling_rate = sampling_rate
+        self.stft_fn = STFT(filter_length, hop_length, win_length)
+        mel_basis = librosa_mel_fn(
+            sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
+        mel_basis = torch.from_numpy(mel_basis).float()
+        self.register_buffer('mel_basis', mel_basis)
+
+    def spectral_normalize(self, magnitudes):
+        output = dynamic_range_compression(magnitudes)
+        return output
+
+    def spectral_de_normalize(self, magnitudes):
+        output = dynamic_range_decompression(magnitudes)
+        return output
+
+    def mel_spectrogram(self, y):
+        """Computes mel-spectrograms from a batch of waves
+        PARAMS
+        ------
+        y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
+
+        RETURNS
+        -------
+        mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
+        """
+        assert(torch.min(y.data) >= -1)
+        assert(torch.max(y.data) <= 1)
+
+        magnitudes, phases = self.stft_fn.transform(y)
+        magnitudes = magnitudes.data
+        mel_output = torch.matmul(self.mel_basis, magnitudes)
+        mel_output = self.spectral_normalize(mel_output)
+        return mel_output