Source code for espnet2.gan_tts.jets.length_regulator

# Copyright 2022 Dan Lim
#  Apache 2.0  (

import logging

import torch

[docs]class GaussianUpsampling(torch.nn.Module): """Gaussian upsampling with fixed temperature as in: """ def __init__(self, delta=0.1): super().__init__() = delta
[docs] def forward(self, hs, ds, h_masks=None, d_masks=None): """Upsample hidden states according to durations. Args: hs (Tensor): Batched hidden state to be expanded (B, T_text, adim). ds (Tensor): Batched token duration (B, T_text). h_masks (Tensor): Mask tensor (B, T_feats). d_masks (Tensor): Mask tensor (B, T_text). Returns: Tensor: Expanded hidden state (B, T_feat, adim). """ B = ds.size(0) device = ds.device if ds.sum() == 0: logging.warning( "predicted durations includes all 0 sequences. " "fill the first element with 1." ) # NOTE(kan-bayashi): This case must not be happened in teacher forcing. # It will be happened in inference with a bad duration predictor. # So we do not need to care the padded sequence case here. ds[ds.sum(dim=1).eq(0)] = 1 if h_masks is None: T_feats = ds.sum().int() else: T_feats = h_masks.size(-1) t = torch.arange(0, T_feats).unsqueeze(0).repeat(B, 1).to(device).float() if h_masks is not None: t = t * h_masks.float() c = ds.cumsum(dim=-1) - ds / 2 energy = -1 * * (t.unsqueeze(-1) - c.unsqueeze(1)) ** 2 if d_masks is not None: energy = energy.masked_fill( ~(d_masks.unsqueeze(1).repeat(1, T_feats, 1)), -float("inf") ) p_attn = torch.softmax(energy, dim=2) # (B, T_feats, T_text) hs = torch.matmul(p_attn, hs) return hs