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# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************
from typing import Callable, List, Tuple
import torch
from torch import Tensor
from torch.utils.data.dataset import random_split
from torchaudio.datasets import LJSPEECH
class SpectralNormalization(torch.nn.Module):
def forward(self, input):
return torch.log(torch.clamp(input, min=1e-5))
class InverseSpectralNormalization(torch.nn.Module):
def forward(self, input):
return torch.exp(input)
class MapMemoryCache(torch.utils.data.Dataset):
r"""Wrap a dataset so that, whenever a new item is returned, it is saved to memory."""
def __init__(self, dataset):
self.dataset = dataset
self._cache = [None] * len(dataset)
def __getitem__(self, n):
if self._cache[n] is not None:
return self._cache[n]
item = self.dataset[n]
self._cache[n] = item
return item
def __len__(self):
return len(self.dataset)
class Processed(torch.utils.data.Dataset):
def __init__(self, dataset, transforms, text_preprocessor):
self.dataset = dataset
self.transforms = transforms
self.text_preprocessor = text_preprocessor
def __getitem__(self, key):
item = self.dataset[key]
return self.process_datapoint(item)
def __len__(self):
return len(self.dataset)
def process_datapoint(self, item):
melspec = self.transforms(item[0])
text_norm = torch.IntTensor(self.text_preprocessor(item[2]))
return text_norm, torch.squeeze(melspec, 0)
def split_process_dataset(
dataset: str,
file_path: str,
val_ratio: float,
transforms: Callable,
text_preprocessor: Callable[[str], List[int]],
) -> Tuple[torch.utils.data.Dataset, torch.utils.data.Dataset]:
"""Returns the Training and validation datasets.
Args:
dataset (str): The dataset to use. Available options: [`'ljspeech'`]
file_path (str): Path to the data.
val_ratio (float): Path to the data.
transforms (callable): A function/transform that takes in a waveform and
returns a transformed waveform (mel spectrogram in this example).
text_preprocess (callable): A function that takes in a string and
returns a list of integers representing each of the symbol in the string.
Returns:
train_dataset (`torch.utils.data.Dataset`): The training set.
val_dataset (`torch.utils.data.Dataset`): The validation set.
"""
if dataset == "ljspeech":
data = LJSPEECH(root=file_path, download=False)
val_length = int(len(data) * val_ratio)
lengths = [len(data) - val_length, val_length]
train_dataset, val_dataset = random_split(data, lengths)
else:
raise ValueError(f"Expected datasets: `ljspeech`, but found {dataset}")
train_dataset = Processed(train_dataset, transforms, text_preprocessor)
val_dataset = Processed(val_dataset, transforms, text_preprocessor)
train_dataset = MapMemoryCache(train_dataset)
val_dataset = MapMemoryCache(val_dataset)
return train_dataset, val_dataset
def text_mel_collate_fn(
batch: Tuple[Tensor, Tensor], n_frames_per_step: int = 1
) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
"""The collate function padding and adjusting the data based on `n_frames_per_step`.
Modified from https://github.com/NVIDIA/DeepLearningExamples
Args:
batch (tuple of two tensors): the first tensor is the mel spectrogram with shape
(n_batch, n_mels, n_frames), the second tensor is the text with shape (n_batch, ).
n_frames_per_step (int, optional): The number of frames to advance every step.
Returns:
text_padded (Tensor): The input text to Tacotron2 with shape (n_batch, max of ``text_lengths``).
text_lengths (Tensor): The length of each text with shape (n_batch).
mel_specgram_padded (Tensor): The target mel spectrogram
with shape (n_batch, n_mels, max of ``mel_specgram_lengths``)
mel_specgram_lengths (Tensor): The length of each mel spectrogram with shape (n_batch).
gate_padded (Tensor): The ground truth gate output
with shape (n_batch, max of ``mel_specgram_lengths``)
"""
text_lengths, ids_sorted_decreasing = torch.sort(
torch.LongTensor([len(x[0]) for x in batch]), dim=0, descending=True
)
max_input_len = text_lengths[0]
text_padded = torch.zeros((len(batch), max_input_len), dtype=torch.int64)
for i in range(len(ids_sorted_decreasing)):
text = batch[ids_sorted_decreasing[i]][0]
text_padded[i, : text.size(0)] = text
# Right zero-pad mel-spec
num_mels = batch[0][1].size(0)
max_target_len = max([x[1].size(1) for x in batch])
if max_target_len % n_frames_per_step != 0:
max_target_len += n_frames_per_step - max_target_len % n_frames_per_step
assert max_target_len % n_frames_per_step == 0
# include mel padded and gate padded
mel_specgram_padded = torch.zeros((len(batch), num_mels, max_target_len), dtype=torch.float32)
gate_padded = torch.zeros((len(batch), max_target_len), dtype=torch.float32)
mel_specgram_lengths = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
mel = batch[ids_sorted_decreasing[i]][1]
mel_specgram_padded[i, :, : mel.size(1)] = mel
mel_specgram_lengths[i] = mel.size(1)
gate_padded[i, mel.size(1) - 1 :] = 1
return text_padded, text_lengths, mel_specgram_padded, mel_specgram_lengths, gate_padded
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