Refactor

egs/libritts/TTS/vocos/discriminators.py

@@ -0,0 +1,296 @@
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from torch.nn import Conv2d
+from torch.nn.utils import weight_norm
+from torchaudio.transforms import Spectrogram
+
+
+class MultiPeriodDiscriminator(nn.Module):
+    """
+    Multi-Period Discriminator module adapted from https://github.com/jik876/hifi-gan.
+    Additionally, it allows incorporating conditional information with a learned embeddings table.
+
+    Args:
+        periods (tuple[int]): Tuple of periods for each discriminator.
+        num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator.
+            Defaults to None.
+    """
+
+    def __init__(
+        self,
+        periods: Tuple[int, ...] = (2, 3, 5, 7, 11),
+        num_embeddings: Optional[int] = None,
+    ):
+        super().__init__()
+        self.discriminators = nn.ModuleList(
+            [DiscriminatorP(period=p, num_embeddings=num_embeddings) for p in periods]
+        )
+
+    def forward(
+        self,
+        y: torch.Tensor,
+        y_hat: torch.Tensor,
+        bandwidth_id: Optional[torch.Tensor] = None,
+    ) -> Tuple[
+        List[torch.Tensor],
+        List[torch.Tensor],
+        List[List[torch.Tensor]],
+        List[List[torch.Tensor]],
+    ]:
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for d in self.discriminators:
+            y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id)
+            y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id)
+            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
+
+
+class DiscriminatorP(nn.Module):
+    def __init__(
+        self,
+        period: int,
+        in_channels: int = 1,
+        kernel_size: int = 5,
+        stride: int = 3,
+        lrelu_slope: float = 0.1,
+        num_embeddings: Optional[int] = None,
+    ):
+        super().__init__()
+        self.period = period
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv2d(
+                        in_channels,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(kernel_size // 2, 0),
+                    )
+                ),
+                weight_norm(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(kernel_size // 2, 0),
+                    )
+                ),
+                weight_norm(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(kernel_size // 2, 0),
+                    )
+                ),
+                weight_norm(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(kernel_size // 2, 0),
+                    )
+                ),
+                weight_norm(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        (1, 1),
+                        padding=(kernel_size // 2, 0),
+                    )
+                ),
+            ]
+        )
+        if num_embeddings is not None:
+            self.emb = torch.nn.Embedding(
+                num_embeddings=num_embeddings, embedding_dim=1024
+            )
+            torch.nn.init.zeros_(self.emb.weight)
+
+        self.conv_post = weight_norm(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+        self.lrelu_slope = lrelu_slope
+
+    def forward(
+        self, x: torch.Tensor, cond_embedding_id: Optional[torch.Tensor] = None
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        x = x.unsqueeze(1)
+        fmap = []
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = torch.nn.functional.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for i, l in enumerate(self.convs):
+            x = l(x)
+            x = torch.nn.functional.leaky_relu(x, self.lrelu_slope)
+            if i > 0:
+                fmap.append(x)
+        if cond_embedding_id is not None:
+            emb = self.emb(cond_embedding_id)
+            h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True)
+        else:
+            h = 0
+        x = self.conv_post(x)
+        fmap.append(x)
+        x += h
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiResolutionDiscriminator(nn.Module):
+    def __init__(
+        self,
+        fft_sizes: Tuple[int, ...] = (2048, 1024, 512),
+        num_embeddings: Optional[int] = None,
+    ):
+        """
+        Multi-Resolution Discriminator module adapted from https://github.com/descriptinc/descript-audio-codec.
+        Additionally, it allows incorporating conditional information with a learned embeddings table.
+
+        Args:
+            fft_sizes (tuple[int]): Tuple of window lengths for FFT. Defaults to (2048, 1024, 512).
+            num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator.
+                Defaults to None.
+        """
+
+        super().__init__()
+        self.discriminators = nn.ModuleList(
+            [
+                DiscriminatorR(window_length=w, num_embeddings=num_embeddings)
+                for w in fft_sizes
+            ]
+        )
+
+    def forward(
+        self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: torch.Tensor = None
+    ) -> Tuple[
+        List[torch.Tensor],
+        List[torch.Tensor],
+        List[List[torch.Tensor]],
+        List[List[torch.Tensor]],
+    ]:
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+
+        for d in self.discriminators:
+            y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id)
+            y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id)
+            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
+
+
+class DiscriminatorR(nn.Module):
+    def __init__(
+        self,
+        window_length: int,
+        num_embeddings: Optional[int] = None,
+        channels: int = 32,
+        hop_factor: float = 0.25,
+        bands: Tuple[Tuple[float, float], ...] = (
+            (0.0, 0.1),
+            (0.1, 0.25),
+            (0.25, 0.5),
+            (0.5, 0.75),
+            (0.75, 1.0),
+        ),
+    ):
+        super().__init__()
+        self.window_length = window_length
+        self.hop_factor = hop_factor
+        self.spec_fn = Spectrogram(
+            n_fft=window_length,
+            hop_length=int(window_length * hop_factor),
+            win_length=window_length,
+            power=None,
+        )
+        n_fft = window_length // 2 + 1
+        bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
+        self.bands = bands
+        convs = lambda: nn.ModuleList(
+            [
+                weight_norm(nn.Conv2d(2, channels, (3, 9), (1, 1), padding=(1, 4))),
+                weight_norm(
+                    nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))
+                ),
+                weight_norm(
+                    nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))
+                ),
+                weight_norm(
+                    nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))
+                ),
+                weight_norm(
+                    nn.Conv2d(channels, channels, (3, 3), (1, 1), padding=(1, 1))
+                ),
+            ]
+        )
+        self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
+
+        if num_embeddings is not None:
+            self.emb = torch.nn.Embedding(
+                num_embeddings=num_embeddings, embedding_dim=channels
+            )
+            torch.nn.init.zeros_(self.emb.weight)
+
+        self.conv_post = weight_norm(
+            nn.Conv2d(channels, 1, (3, 3), (1, 1), padding=(1, 1))
+        )
+
+    def spectrogram(self, x):
+        # Remove DC offset
+        x = x - x.mean(dim=-1, keepdims=True)
+        # Peak normalize the volume of input audio
+        x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
+        x = self.spec_fn(x)
+        x = torch.view_as_real(x)
+        # x = rearrange(x, "b f t c -> b c t f")
+        x = x.permute(0, 3, 2, 1)
+        # Split into bands
+        x_bands = [x[..., b[0] : b[1]] for b in self.bands]
+        return x_bands
+
+    def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None):
+        x_bands = self.spectrogram(x)
+        fmap = []
+        x = []
+        for band, stack in zip(x_bands, self.band_convs):
+            for i, layer in enumerate(stack):
+                band = layer(band)
+                band = torch.nn.functional.leaky_relu(band, 0.1)
+                if i > 0:
+                    fmap.append(band)
+            x.append(band)
+        x = torch.cat(x, dim=-1)
+        if cond_embedding_id is not None:
+            emb = self.emb(cond_embedding_id)
+            h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True)
+        else:
+            h = 0
+        x = self.conv_post(x)
+        fmap.append(x)
+        x += h
+
+        return x, fmap