Adding option to do flow matching based duration prediction

matcha/models/components/duration_predictors.py

@@ -0,0 +1,243 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import pack
+
+from matcha.models.components.decoder import SinusoidalPosEmb, TimestepEmbedding
+from matcha.models.components.text_encoder import LayerNorm
+
+# Define available networks
+
+
+class DurationPredictorNetwork(nn.Module):
+    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.p_dropout = p_dropout
+
+        self.drop = torch.nn.Dropout(p_dropout)
+        self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_1 = LayerNorm(filter_channels)
+        self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_2 = LayerNorm(filter_channels)
+        self.proj = torch.nn.Conv1d(filter_channels, 1, 1)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class DurationPredictorNetworkWithTimeStep(nn.Module):
+    """Similar architecture but with a time embedding support"""
+
+    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
+        super().__init__()
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.p_dropout = p_dropout
+
+        self.time_embeddings = SinusoidalPosEmb(filter_channels)
+        self.time_mlp = TimestepEmbedding(
+            in_channels=filter_channels,
+            time_embed_dim=filter_channels,
+            act_fn="silu",
+        )
+
+        self.drop = torch.nn.Dropout(p_dropout)
+        self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_1 = LayerNorm(filter_channels)
+        self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_2 = LayerNorm(filter_channels)
+        self.proj = torch.nn.Conv1d(filter_channels, 1, 1)
+
+    def forward(self, x, x_mask, enc_outputs, t):
+        t = self.time_embeddings(t)
+        t = self.time_mlp(t).unsqueeze(-1)
+
+        x = pack([x, enc_outputs], "b * t")[0]
+
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = x + t
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = x + t
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+# Define available methods to compute loss
+
+# Simple MSE deterministic
+
+
+class DeterministicDurationPredictor(nn.Module):
+    def __init__(self, params):
+        super().__init__()
+        self.estimator = DurationPredictorNetwork(
+            params.n_channels + (params.spk_emb_dim if params.n_spks > 1 else 0),
+            params.filter_channels,
+            params.kernel_size,
+            params.p_dropout,
+        )
+
+    @torch.inference_mode()
+    def forward(self, x, x_mask):
+        return self.estimator(x, x_mask)
+
+    def compute_loss(self, durations, enc_outputs, x_mask):
+        return F.mse_loss(self.estimator(enc_outputs, x_mask), durations, reduction="sum") / torch.sum(x_mask)
+
+
+# Flow Matching duration predictor
+
+
+class FlowMatchingDurationPrediction(nn.Module):
+    def __init__(self, params) -> None:
+        super().__init__()
+
+        self.estimator = DurationPredictorNetworkWithTimeStep(
+            1
+            + params.n_channels
+            + (
+                params.spk_emb_dim if params.n_spks > 1 else 0
+            ),  # 1 for the durations and n_channels for encoder outputs
+            params.filter_channels,
+            params.kernel_size,
+            params.p_dropout,
+        )
+        self.sigma_min = params.sigma_min
+        self.n_steps = params.n_steps
+
+    @torch.inference_mode()
+    def forward(self, enc_outputs, mask, n_timesteps=None, temperature=1):
+        """Forward diffusion
+
+        Args:
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            n_timesteps (int): number of diffusion steps
+            temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+
+        Returns:
+            sample: generated mel-spectrogram
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        if n_timesteps is None:
+            n_timesteps = self.n_steps
+
+        b, _, t = enc_outputs.shape
+        z = torch.randn((b, 1, t), device=enc_outputs.device, dtype=enc_outputs.dtype) * temperature
+        t_span = torch.linspace(0, 1, n_timesteps + 1, device=enc_outputs.device)
+        return self.solve_euler(z, t_span=t_span, enc_outputs=enc_outputs, mask=mask)
+
+    def solve_euler(self, x, t_span, enc_outputs, mask):
+        """
+        Fixed euler solver for ODEs.
+        Args:
+            x (torch.Tensor): random noise
+            t_span (torch.Tensor): n_timesteps interpolated
+                shape: (n_timesteps + 1,)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+        """
+        t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
+
+        # I am storing this because I can later plot it by putting a debugger here and saving it to a file
+        # Or in future might add like a return_all_steps flag
+        sol = []
+
+        for step in range(1, len(t_span)):
+            dphi_dt = self.estimator(x, mask, enc_outputs, t)
+
+            x = x + dt * dphi_dt
+            t = t + dt
+            sol.append(x)
+            if step < len(t_span) - 1:
+                dt = t_span[step + 1] - t
+
+        return sol[-1]
+
+    def compute_loss(self, x1, enc_outputs, mask):
+        """Computes diffusion loss
+
+        Args:
+            x1 (torch.Tensor): Target
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): target mask
+                shape: (batch_size, 1, mel_timesteps)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            spks (torch.Tensor, optional): speaker embedding. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+
+        Returns:
+            loss: conditional flow matching loss
+            y: conditional flow
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        enc_outputs = enc_outputs.detach()  # don't update encoder from the duration predictor
+        b, _, t = enc_outputs.shape
+
+        # random timestep
+        t = torch.rand([b, 1, 1], device=enc_outputs.device, dtype=enc_outputs.dtype)
+        # sample noise p(x_0)
+        z = torch.randn_like(x1)
+
+        y = (1 - (1 - self.sigma_min) * t) * z + t * x1
+        u = x1 - (1 - self.sigma_min) * z
+
+        loss = F.mse_loss(self.estimator(y, mask, enc_outputs, t.squeeze()), u, reduction="sum") / (
+            torch.sum(mask) * u.shape[1]
+        )
+        return loss
+
+
+# Meta class to wrap all duration predictors
+
+
+class DP(nn.Module):
+    def __init__(self, params):
+        super().__init__()
+        self.name = params.name
+
+        if params.name == "deterministic":
+            self.dp = DeterministicDurationPredictor(
+                params,
+            )
+        elif params.name == "flow_matching":
+            self.dp = FlowMatchingDurationPrediction(
+                params,
+            )
+        else:
+            raise ValueError(f"Invalid duration predictor configuration: {params.name}")
+
+    @torch.inference_mode()
+    def forward(self, enc_outputs, mask):
+        return self.dp(enc_outputs, mask)
+
+    def compute_loss(self, durations, enc_outputs, mask):
+        return self.dp.compute_loss(durations, enc_outputs, mask)