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data.py
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# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# MIT License for more details.
import random
import numpy as np
import torch
import torchaudio as ta
from text import text_to_sequence, cmudict
from text.symbols import symbols
from utils import parse_filelist, intersperse
from model.utils import fix_len_compatibility
from params import seed as random_seed
import sys
sys.path.insert(0, 'hifi-gan')
from meldataset import mel_spectrogram
class TextMelDataset(torch.utils.data.Dataset):
def __init__(self, filelist_path, cmudict_path, add_blank=True,
n_fft=1024, n_mels=80, sample_rate=22050,
hop_length=256, win_length=1024, f_min=0., f_max=8000):
self.filepaths_and_text = parse_filelist(filelist_path)
self.cmudict = cmudict.CMUDict(cmudict_path)
self.add_blank = add_blank
self.n_fft = n_fft
self.n_mels = n_mels
self.sample_rate = sample_rate
self.hop_length = hop_length
self.win_length = win_length
self.f_min = f_min
self.f_max = f_max
random.seed(random_seed)
random.shuffle(self.filepaths_and_text)
def get_pair(self, filepath_and_text):
filepath, text = filepath_and_text[0], filepath_and_text[1]
text = self.get_text(text, add_blank=self.add_blank) #텍스트를 전처리하여 텐서로 변환
mel = self.get_mel(filepath) #멜 스펙트로그램으로 변환
return (text, mel)
def get_mel(self, filepath):
audio, sr = ta.load(filepath)
assert sr == self.sample_rate
mel = mel_spectrogram(audio, self.n_fft, self.n_mels, self.sample_rate, self.hop_length,
self.win_length, self.f_min, self.f_max, center=False).squeeze()
return mel
def get_text(self, text, add_blank=True):
text_norm = text_to_sequence(text, dictionary=self.cmudict)
if self.add_blank:
text_norm = intersperse(text_norm, len(symbols)) # add a blank token, whose id number is len(symbols)
text_norm = torch.IntTensor(text_norm)
return text_norm
def __getitem__(self, index):
text, mel = self.get_pair(self.filepaths_and_text[index])
item = {'y': mel, 'x': text}
return item
def __len__(self):
return len(self.filepaths_and_text)
def sample_test_batch(self, size):
idx = np.random.choice(range(len(self)), size=size, replace=False)
test_batch = []
for index in idx:
test_batch.append(self.__getitem__(index))
return test_batch
#배치 데이터를 정렬하고 패딩(padding)하여 동일한 크기의 텐서로 만들어 모델에 입력할
class TextMelBatchCollate(object):
def __call__(self, batch):
B = len(batch)
y_max_length = max([item['y'].shape[-1] for item in batch])
y_max_length = fix_len_compatibility(y_max_length)
x_max_length = max([item['x'].shape[-1] for item in batch])
n_feats = batch[0]['y'].shape[-2]
y = torch.zeros((B, n_feats, y_max_length), dtype=torch.float32)
x = torch.zeros((B, x_max_length), dtype=torch.long)
y_lengths, x_lengths = [], []
for i, item in enumerate(batch):
y_, x_ = item['y'], item['x']
y_lengths.append(y_.shape[-1])
x_lengths.append(x_.shape[-1])
y[i, :, :y_.shape[-1]] = y_
x[i, :x_.shape[-1]] = x_
y_lengths = torch.LongTensor(y_lengths)
x_lengths = torch.LongTensor(x_lengths)
return {'x': x, 'x_lengths': x_lengths, 'y': y, 'y_lengths': y_lengths}
class TextMelSpeakerDataset(torch.utils.data.Dataset):
def __init__(self, filelist_path, cmudict_path, add_blank=True,
n_fft=1024, n_mels=80, sample_rate=22050,
hop_length=256, win_length=1024, f_min=0., f_max=8000):
super().__init__()
self.filelist = parse_filelist(filelist_path, split_char='|')
self.cmudict = cmudict.CMUDict(cmudict_path)
self.n_fft = n_fft
self.n_mels = n_mels
self.sample_rate = sample_rate
self.hop_length = hop_length
self.win_length = win_length
self.f_min = f_min
self.f_max = f_max
self.add_blank = add_blank
random.seed(random_seed)
random.shuffle(self.filelist)
def get_triplet(self, line):
filepath, text, speaker = line[0], line[1], line[2]
text = self.get_text(text, add_blank=self.add_blank)
mel = self.get_mel(filepath)
speaker = self.get_speaker(speaker)
return (text, mel, speaker)
def get_mel(self, filepath):
audio, sr = ta.load(filepath)
assert sr == self.sample_rate
mel = mel_spectrogram(audio, self.n_fft, self.n_mels, self.sample_rate, self.hop_length,
self.win_length, self.f_min, self.f_max, center=False).squeeze()
return mel
def get_text(self, text, add_blank=True):
text_norm = text_to_sequence(text, dictionary=self.cmudict)
if self.add_blank:
text_norm = intersperse(text_norm, len(symbols)) # add a blank token, whose id number is len(symbols)
text_norm = torch.LongTensor(text_norm)
return text_norm
def get_speaker(self, speaker):
speaker = torch.LongTensor([int(speaker)])
return speaker
def __getitem__(self, index):
text, mel, speaker = self.get_triplet(self.filelist[index])
item = {'y': mel, 'x': text, 'spk': speaker}
return item
def __len__(self):
return len(self.filelist)
def sample_test_batch(self, size):
idx = np.random.choice(range(len(self)), size=size, replace=False)
test_batch = []
for index in idx:
test_batch.append(self.__getitem__(index))
return test_batch
class TextMelSpeakerBatchCollate(object):
def __call__(self, batch):
B = len(batch)
y_max_length = max([item['y'].shape[-1] for item in batch])
y_max_length = fix_len_compatibility(y_max_length)
x_max_length = max([item['x'].shape[-1] for item in batch])
n_feats = batch[0]['y'].shape[-2]
y = torch.zeros((B, n_feats, y_max_length), dtype=torch.float32)
x = torch.zeros((B, x_max_length), dtype=torch.long)
y_lengths, x_lengths = [], []
spk = []
for i, item in enumerate(batch):
y_, x_, spk_ = item['y'], item['x'], item['spk']
y_lengths.append(y_.shape[-1])
x_lengths.append(x_.shape[-1])
y[i, :, :y_.shape[-1]] = y_
x[i, :x_.shape[-1]] = x_
spk.append(spk_)
y_lengths = torch.LongTensor(y_lengths)
x_lengths = torch.LongTensor(x_lengths)
spk = torch.cat(spk, dim=0)
return {'x': x, 'x_lengths': x_lengths, 'y': y, 'y_lengths': y_lengths, 'spk': spk}