Source code for espnet.nets.pytorch_backend.fastspeech.length_regulator

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright 2019 Tomoki Hayashi
#  Apache 2.0  (

"""Length regulator related modules."""

import logging

import torch

from espnet.nets.pytorch_backend.nets_utils import pad_list

[docs]class LengthRegulator(torch.nn.Module): """Length regulator module for feed-forward Transformer. This is a module of length regulator described in `FastSpeech: Fast, Robust and Controllable Text to Speech`_. The length regulator expands char or phoneme-level embedding features to frame-level by repeating each feature based on the corresponding predicted durations. .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`: """ def __init__(self, pad_value=0.0): """Initilize length regulator module. Args: pad_value (float, optional): Value used for padding. """ super().__init__() self.pad_value = pad_value
[docs] def forward(self, xs, ds, alpha=1.0): """Calculate forward propagation. Args: xs (Tensor): Batch of sequences of char or phoneme embeddings (B, Tmax, D). ds (LongTensor): Batch of durations of each frame (B, T). alpha (float, optional): Alpha value to control speed of speech. Returns: Tensor: replicated input tensor based on durations (B, T*, D). """ if alpha != 1.0: assert alpha > 0 ds = torch.round(ds.float() * alpha).long() 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 repeat = [torch.repeat_interleave(x, d, dim=0) for x, d in zip(xs, ds)] return pad_list(repeat, self.pad_value)