Update modules/length_regulator.py

modules/length_regulator.py

@@ -1,96 +1,102 @@
-from typing import Tuple
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from modules.commons import sequence_mask
-
-
-class InterpolateRegulator(nn.Module):
-    def __init__(
-            self,
-            channels: int,
-            sampling_ratios: Tuple,
-            is_discrete: bool = False,
-            codebook_size: int = 1024, # for discrete only
-            out_channels: int = None,
-            groups: int = 1,
-            token_dropout_prob: float = 0.5,  # randomly drop out input tokens
-            token_dropout_range: float = 0.5,  # randomly drop out input tokens
-            n_codebooks: int = 1,  # number of codebooks
-            quantizer_dropout: float = 0.0,  # dropout for quantizer
-            f0_condition: bool = False,
-            n_f0_bins: int = 512,
-    ):
-        super().__init__()
-        self.sampling_ratios = sampling_ratios
-        out_channels = out_channels or channels
-        model = nn.ModuleList([])
-        if len(sampling_ratios) > 0:
-            for _ in sampling_ratios:
-                module = nn.Conv1d(channels, channels, 3, 1, 1)
-                norm = nn.GroupNorm(groups, channels)
-                act = nn.Mish()
-                model.extend([module, norm, act])
-        model.append(
-            nn.Conv1d(channels, out_channels, 1, 1)
-        )
-        self.model = nn.Sequential(*model)
-        self.embedding = nn.Embedding(codebook_size, channels)
-        self.is_discrete = is_discrete
-
-        self.mask_token = nn.Parameter(torch.zeros(1, channels))
-
-        self.n_codebooks = n_codebooks
-        if n_codebooks > 1:
-            self.extra_codebooks = nn.ModuleList([
-                nn.Embedding(codebook_size, channels) for _ in range(n_codebooks - 1)
-            ])
-        self.token_dropout_prob = token_dropout_prob
-        self.token_dropout_range = token_dropout_range
-        self.quantizer_dropout = quantizer_dropout
-
-        if f0_condition:
-            self.f0_embedding = nn.Embedding(n_f0_bins, channels)
-            self.f0_condition = f0_condition
-            self.n_f0_bins = n_f0_bins
-            self.f0_bins = torch.arange(2, 1024, 1024 // n_f0_bins)
-            self.f0_mask = nn.Parameter(torch.zeros(1, channels))
-        else:
-            self.f0_condition = False
-
-    def forward(self, x, ylens=None, n_quantizers=None, f0=None):
-        # apply token drop
-        if self.training:
-            n_quantizers = torch.ones((x.shape[0],)) * self.n_codebooks
-            dropout = torch.randint(1, self.n_codebooks + 1, (x.shape[0],))
-            n_dropout = int(x.shape[0] * self.quantizer_dropout)
-            n_quantizers[:n_dropout] = dropout[:n_dropout]
-            n_quantizers = n_quantizers.to(x.device)
-            # decide whether to drop for each sample in batch
-        else:
-            n_quantizers = torch.ones((x.shape[0],), device=x.device) * (self.n_codebooks if n_quantizers is None else n_quantizers)
-        if self.is_discrete:
-            if self.n_codebooks > 1:
-                assert len(x.size()) == 3
-                x_emb = self.embedding(x[:, 0])
-                for i, emb in enumerate(self.extra_codebooks):
-                    x_emb = x_emb + (n_quantizers > i+1)[..., None, None] * emb(x[:, i+1])
-                x = x_emb
-            elif self.n_codebooks == 1:
-                if len(x.size()) == 2:
-                    x = self.embedding(x)
-                else:
-                    x = self.embedding(x[:, 0])
-        # x in (B, T, D)
-        mask = sequence_mask(ylens).unsqueeze(-1)
-        x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
-        if self.f0_condition:
-            quantized_f0 = torch.bucketize(f0, self.f0_bins.to(f0.device))  # (N, T)
-            drop_f0 = torch.rand(quantized_f0.size(0)).to(f0.device) < self.quantizer_dropout
-            f0_emb = self.f0_embedding(quantized_f0)
-            f0_emb[drop_f0] = self.f0_mask
-            f0_emb = F.interpolate(f0_emb.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
-            x = x + f0_emb
-        out = self.model(x).transpose(1, 2).contiguous()
-        olens = ylens
-        return out * mask, olens
+from typing import Tuple
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from modules.commons import sequence_mask
+
+
+class InterpolateRegulator(nn.Module):
+    def __init__(
+            self,
+            channels: int,
+            sampling_ratios: Tuple,
+            is_discrete: bool = False,
+            codebook_size: int = 1024, # for discrete only
+            out_channels: int = None,
+            groups: int = 1,
+            token_dropout_prob: float = 0.5,  # randomly drop out input tokens
+            token_dropout_range: float = 0.5,  # randomly drop out input tokens
+            n_codebooks: int = 1,  # number of codebooks
+            quantizer_dropout: float = 0.0,  # dropout for quantizer
+            f0_condition: bool = False,
+            n_f0_bins: int = 512,
+    ):
+        super().__init__()
+        self.sampling_ratios = sampling_ratios
+        out_channels = out_channels or channels
+        model = nn.ModuleList([])
+        if len(sampling_ratios) > 0:
+            for _ in sampling_ratios:
+                module = nn.Conv1d(channels, channels, 3, 1, 1)
+                norm = nn.GroupNorm(groups, channels)
+                act = nn.Mish()
+                model.extend([module, norm, act])
+        model.append(
+            nn.Conv1d(channels, out_channels, 1, 1)
+        )
+        self.model = nn.Sequential(*model)
+        self.embedding = nn.Embedding(codebook_size, channels)
+        self.is_discrete = is_discrete
+
+        self.mask_token = nn.Parameter(torch.zeros(1, channels))
+
+        self.n_codebooks = n_codebooks
+        if n_codebooks > 1:
+            self.extra_codebooks = nn.ModuleList([
+                nn.Embedding(codebook_size, channels) for _ in range(n_codebooks - 1)
+            ])
+        self.token_dropout_prob = token_dropout_prob
+        self.token_dropout_range = token_dropout_range
+        self.quantizer_dropout = quantizer_dropout
+
+        if f0_condition:
+            self.f0_embedding = nn.Embedding(n_f0_bins, channels)
+            self.f0_condition = f0_condition
+            self.n_f0_bins = n_f0_bins
+            self.f0_bins = torch.arange(2, 1024, 1024 // n_f0_bins)
+            self.f0_mask = nn.Parameter(torch.zeros(1, channels))
+        else:
+            self.f0_condition = False
+
+    def forward(self, x, ylens=None, n_quantizers=None, f0=None):
+        # apply token drop
+        if self.training:
+            n_quantizers = torch.ones((x.shape[0],)) * self.n_codebooks
+            dropout = torch.randint(1, self.n_codebooks + 1, (x.shape[0],))
+            n_dropout = int(x.shape[0] * self.quantizer_dropout)
+            n_quantizers[:n_dropout] = dropout[:n_dropout]
+            n_quantizers = n_quantizers.to(x.device)
+            # decide whether to drop for each sample in batch
+        else:
+            n_quantizers = torch.ones((x.shape[0],), device=x.device) * (self.n_codebooks if n_quantizers is None else n_quantizers)
+        if self.is_discrete:
+            if self.n_codebooks > 1:
+                assert len(x.size()) == 3
+                x_emb = self.embedding(x[:, 0])
+                for i, emb in enumerate(self.extra_codebooks):
+                    x_emb = x_emb + (n_quantizers > i+1)[..., None, None] * emb(x[:, i+1])
+                x = x_emb
+            elif self.n_codebooks == 1:
+                if len(x.size()) == 2:
+                    x = self.embedding(x)
+                else:
+                    x = self.embedding(x[:, 0])
+        # x in (B, T, D)
+        mask = sequence_mask(ylens).unsqueeze(-1)
+        x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
+        if self.f0_condition:
+            if f0 is None:
+                x = x + self.f0_mask.unsqueeze(-1)
+            else:
+                quantized_f0 = torch.bucketize(f0, self.f0_bins.to(f0.device))  # (N, T)
+                if self.training:
+                    drop_f0 = torch.rand(quantized_f0.size(0)).to(f0.device) < self.quantizer_dropout
+                else:
+                    drop_f0 = torch.zeros(quantized_f0.size(0)).to(f0.device).bool()
+                f0_emb = self.f0_embedding(quantized_f0)
+                f0_emb[drop_f0] = self.f0_mask
+                f0_emb = F.interpolate(f0_emb.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
+                x = x + f0_emb
+        out = self.model(x).transpose(1, 2).contiguous()
+        olens = ylens
+        return out * mask, olens