160 lines
6.9 KiB
Python
160 lines
6.9 KiB
Python
# *****************************************************************************
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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# * Neither the name of the NVIDIA CORPORATION nor the
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# names of its contributors may be used to endorse or promote products
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# derived from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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# *****************************************************************************
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import random
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import numpy as np
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import torch
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import torch.utils.data
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import common.layers as layers
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from common.utils import load_wav_to_torch, load_filepaths_and_text, to_gpu
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from common.text import text_to_sequence
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class TextMelLoader(torch.utils.data.Dataset):
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"""
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1) loads audio,text pairs
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2) normalizes text and converts them to sequences of one-hot vectors
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3) computes mel-spectrograms from audio files.
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"""
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def __init__(self, dataset_path, audiopaths_and_text, args, load_mel_from_disk=True):
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self.audiopaths_and_text = load_filepaths_and_text(dataset_path, audiopaths_and_text)
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self.text_cleaners = args.text_cleaners
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self.load_mel_from_disk = load_mel_from_disk
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if not load_mel_from_disk:
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self.max_wav_value = args.max_wav_value
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self.sampling_rate = args.sampling_rate
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self.stft = layers.TacotronSTFT(
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args.filter_length, args.hop_length, args.win_length,
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args.n_mel_channels, args.sampling_rate, args.mel_fmin,
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args.mel_fmax)
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def get_mel(self, filename):
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if not self.load_mel_from_disk:
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audio, sampling_rate = load_wav_to_torch(filename)
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if sampling_rate != self.stft.sampling_rate:
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raise ValueError("{} {} SR doesn't match target {} SR".format(
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sampling_rate, self.stft.sampling_rate))
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audio_norm = audio / self.max_wav_value
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audio_norm = audio_norm.unsqueeze(0)
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audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
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melspec = self.stft.mel_spectrogram(audio_norm)
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melspec = torch.squeeze(melspec, 0)
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else:
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melspec = torch.load(filename)
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# assert melspec.size(0) == self.stft.n_mel_channels, (
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# 'Mel dimension mismatch: given {}, expected {}'.format(
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# melspec.size(0), self.stft.n_mel_channels))
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return melspec
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def get_text(self, text):
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text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners))
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return text_norm
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def __getitem__(self, index):
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# separate filename and text
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audiopath, text = self.audiopaths_and_text[index]
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len_text = len(text)
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text = self.get_text(text)
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mel = self.get_mel(audiopath)
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return (text, mel, len_text)
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def __len__(self):
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return len(self.audiopaths_and_text)
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class TextMelCollate():
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""" Zero-pads model inputs and targets based on number of frames per step
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"""
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def __init__(self, n_frames_per_step):
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self.n_frames_per_step = n_frames_per_step
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def __call__(self, batch):
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"""Collate's training batch from normalized text and mel-spectrogram
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PARAMS
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------
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batch: [text_normalized, mel_normalized]
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"""
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# Right zero-pad all one-hot text sequences to max input length
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input_lengths, ids_sorted_decreasing = torch.sort(
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torch.LongTensor([len(x[0]) for x in batch]),
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dim=0, descending=True)
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max_input_len = input_lengths[0]
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text_padded = torch.LongTensor(len(batch), max_input_len)
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text_padded.zero_()
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for i in range(len(ids_sorted_decreasing)):
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text = batch[ids_sorted_decreasing[i]][0]
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text_padded[i, :text.size(0)] = text
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# Right zero-pad mel-spec
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num_mels = batch[0][1].size(0)
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max_target_len = max([x[1].size(1) for x in batch])
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if max_target_len % self.n_frames_per_step != 0:
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max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
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assert max_target_len % self.n_frames_per_step == 0
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# include mel padded and gate padded
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mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
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mel_padded.zero_()
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gate_padded = torch.FloatTensor(len(batch), max_target_len)
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gate_padded.zero_()
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output_lengths = torch.LongTensor(len(batch))
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for i in range(len(ids_sorted_decreasing)):
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mel = batch[ids_sorted_decreasing[i]][1]
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mel_padded[i, :, :mel.size(1)] = mel
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gate_padded[i, mel.size(1)-1:] = 1
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output_lengths[i] = mel.size(1)
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# count number of items - characters in text
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len_x = [x[2] for x in batch]
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len_x = torch.Tensor(len_x)
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# Return any extra fields as sorted lists
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num_fields = len(batch[0])
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extra_fields = tuple([batch[i][f] for i in ids_sorted_decreasing]
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for f in range(3, num_fields))
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return (text_padded, input_lengths, mel_padded, gate_padded, \
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output_lengths, len_x) + extra_fields
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def batch_to_gpu(batch):
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text_padded, input_lengths, mel_padded, gate_padded, \
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output_lengths, len_x = batch
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text_padded = to_gpu(text_padded).long()
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input_lengths = to_gpu(input_lengths).long()
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max_len = torch.max(input_lengths.data).item()
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mel_padded = to_gpu(mel_padded).float()
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gate_padded = to_gpu(gate_padded).float()
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output_lengths = to_gpu(output_lengths).long()
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x = (text_padded, input_lengths, mel_padded, max_len, output_lengths)
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y = (mel_padded, gate_padded)
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len_x = torch.sum(output_lengths)
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return (x, y, len_x)
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