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from typing import Callable, Tuple
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import torch
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import torch.nn as nn
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from TTS.tts.layers.delightful_tts.variance_predictor import VariancePredictor
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from TTS.tts.utils.helpers import average_over_durations
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class EnergyAdaptor(nn.Module):
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"""Variance Adaptor with an added 1D conv layer. Used to
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get energy embeddings.
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Args:
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channels_in (int): Number of in channels for conv layers.
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channels_out (int): Number of out channels.
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kernel_size (int): Size the kernel for the conv layers.
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dropout (float): Probability of dropout.
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lrelu_slope (float): Slope for the leaky relu.
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emb_kernel_size (int): Size the kernel for the pitch embedding.
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Inputs: inputs, mask
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- **inputs** (batch, time1, dim): Tensor containing input vector
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- **target** (batch, 1, time2): Tensor containing the energy target
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- **dr** (batch, time1): Tensor containing aligner durations vector
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- **mask** (batch, time1): Tensor containing indices to be masked
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Returns:
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- **energy prediction** (batch, 1, time1): Tensor produced by energy predictor
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- **energy embedding** (batch, channels, time1): Tensor produced energy adaptor
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- **average energy target(train only)** (batch, 1, time1): Tensor produced after averaging over durations
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"""
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def __init__(
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self,
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channels_in: int,
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channels_hidden: int,
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channels_out: int,
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kernel_size: int,
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dropout: float,
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lrelu_slope: float,
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emb_kernel_size: int,
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):
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super().__init__()
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self.energy_predictor = VariancePredictor(
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channels_in=channels_in,
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channels=channels_hidden,
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channels_out=channels_out,
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kernel_size=kernel_size,
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p_dropout=dropout,
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lrelu_slope=lrelu_slope,
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)
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self.energy_emb = nn.Conv1d(
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1,
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channels_hidden,
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kernel_size=emb_kernel_size,
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padding=int((emb_kernel_size - 1) / 2),
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)
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def get_energy_embedding_train(
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self, x: torch.Tensor, target: torch.Tensor, dr: torch.IntTensor, mask: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Shapes:
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x: :math: `[B, T_src, C]`
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target: :math: `[B, 1, T_max2]`
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dr: :math: `[B, T_src]`
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mask: :math: `[B, T_src]`
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"""
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energy_pred = self.energy_predictor(x, mask)
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energy_pred.unsqueeze_(1)
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avg_energy_target = average_over_durations(target, dr)
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energy_emb = self.energy_emb(avg_energy_target)
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return energy_pred, avg_energy_target, energy_emb
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def get_energy_embedding(self, x: torch.Tensor, mask: torch.Tensor, energy_transform: Callable) -> torch.Tensor:
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energy_pred = self.energy_predictor(x, mask)
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energy_pred.unsqueeze_(1)
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if energy_transform is not None:
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energy_pred = energy_transform(energy_pred, (~mask).sum(dim=(1, 2)), self.pitch_mean, self.pitch_std)
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energy_emb_pred = self.energy_emb(energy_pred)
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return energy_emb_pred, energy_pred
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