WIP inference

2026-10-28 09:08:54 +00:00 · 2022-12-07 21:34:00 -05:00
parent 899d81efcc
commit 4837def3c2
5 changed files with 320 additions and 5 deletions
@@ -0,0 +1,113 @@
+import torch
+import torch.nn as nn
+
+# @torch.jit.export
+# def generate(self, x: torch.Tensor) -> torch.Tensor:
+#     x = self.encoder(x)
+#     return self.decoder.generate(x)
+class AcousticModelScripted(nn.Module):
+    def __init__(self, discrete: bool = False, upsample: bool = True):
+        super().__init__()
+        self.encoder = Encoder(discrete, upsample)
+        self.decoder = Decoder()
+
+    def forward(self, x: torch.Tensor, mels: torch.Tensor) -> torch.Tensor:
+        x = self.encoder(x)
+        return self.decoder(x, mels)
+
+    @torch.jit.export
+    def generate(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.encoder(x)
+        return self.decoder.generate(x)
+
+
+class Encoder(nn.Module):
+    def __init__(self, discrete: bool = False, upsample: bool = True):
+        super().__init__()
+        self.embedding = nn.Embedding(100 + 1, 256) if discrete else None
+        self.prenet = PreNet(256, 256, 256)
+        self.convs = nn.Sequential(
+            nn.Conv1d(256, 512, 5, 1, 2),
+            nn.ReLU(),
+            nn.InstanceNorm1d(512),
+            nn.ConvTranspose1d(512, 512, 4, 2, 1) if upsample else nn.Identity(),
+            nn.Conv1d(512, 512, 5, 1, 2),
+            nn.ReLU(),
+            nn.InstanceNorm1d(512),
+            nn.Conv1d(512, 512, 5, 1, 2),
+            nn.ReLU(),
+            nn.InstanceNorm1d(512),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.embedding is not None:
+            x = self.embedding(x)
+        x = self.prenet(x)
+        x = self.convs(x.transpose(1, 2))
+        return x.transpose(1, 2)
+
+
+class Decoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.prenet = PreNet(128, 256, 256)
+        self.lstm1 = nn.LSTM(512 + 256, 768, batch_first=True)
+        self.lstm2 = nn.LSTM(768, 768, batch_first=True)
+        self.lstm3 = nn.LSTM(768, 768, batch_first=True)
+        self.proj = nn.Linear(768, 128, bias=False)
+
+    def forward(self, x: torch.Tensor, mels: torch.Tensor) -> torch.Tensor:
+        mels = self.prenet(mels)
+        x, _ = self.lstm1(torch.cat((x, mels), dim=-1))
+        res = x
+        x, _ = self.lstm2(x)
+        x = res + x
+        res = x
+        x, _ = self.lstm3(x)
+        x = res + x
+        return self.proj(x)
+
+    @torch.inference_mode()
+    def generate(self, xs: torch.Tensor) -> torch.Tensor:
+        m = torch.zeros(xs.size(0), 128, device=xs.device)
+        h1 = torch.zeros(1, xs.size(0), 768, device=xs.device)
+        c1 = torch.zeros(1, xs.size(0), 768, device=xs.device)
+        h2 = torch.zeros(1, xs.size(0), 768, device=xs.device)
+        c2 = torch.zeros(1, xs.size(0), 768, device=xs.device)
+        h3 = torch.zeros(1, xs.size(0), 768, device=xs.device)
+        c3 = torch.zeros(1, xs.size(0), 768, device=xs.device)
+
+        mel = []
+        for x in torch.unbind(xs, dim=1):
+            m = self.prenet(m)
+            x = torch.cat((x, m), dim=1).unsqueeze(1)
+            x1, (h1, c1) = self.lstm1(x, (h1, c1))
+            x2, (h2, c2) = self.lstm2(x1, (h2, c2))
+            x = x1 + x2
+            x3, (h3, c3) = self.lstm3(x, (h3, c3))
+            x = x + x3
+            m = self.proj(x).squeeze(1)
+            mel.append(m)
+        return torch.stack(mel, dim=1)
+
+
+class PreNet(nn.Module):
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        output_size: int,
+        dropout: float = 0.5,
+    ):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(input_size, hidden_size),
+            nn.ReLU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_size, output_size),
+            nn.ReLU(),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.net(x)