styletts2 / meldataset.py
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# coding: utf-8
import os
import os.path as osp
import time
import random
import numpy as np
import random
import soundfile as sf
import librosa
import torch
from torch import nn
import torch.nn.functional as F
import torchaudio
from torch.utils.data import DataLoader
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
import pandas as pd
_pad = "$"
_punctuation = ';:,.!?¡¿—…"«»“” '
_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
# Export all symbols:
symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
dicts = {}
for i in range(len((symbols))):
dicts[symbols[i]] = i
class TextCleaner:
def __init__(self, dummy=None):
self.word_index_dictionary = dicts
def __call__(self, text):
indexes = []
for char in text:
try:
indexes.append(self.word_index_dictionary[char])
except KeyError:
print(text)
return indexes
np.random.seed(1)
random.seed(1)
SPECT_PARAMS = {"n_fft": 2048, "win_length": 1200, "hop_length": 300}
MEL_PARAMS = {
"n_mels": 80,
}
to_mel = torchaudio.transforms.MelSpectrogram(
n_mels=80, n_fft=2048, win_length=1200, hop_length=300
)
mean, std = -4, 4
def preprocess(wave):
wave_tensor = torch.from_numpy(wave).float()
mel_tensor = to_mel(wave_tensor)
mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
return mel_tensor
class FilePathDataset(torch.utils.data.Dataset):
def __init__(
self,
data_list,
root_path,
sr=24000,
data_augmentation=False,
validation=False,
OOD_data="Data/OOD_texts.txt",
min_length=50,
):
spect_params = SPECT_PARAMS
mel_params = MEL_PARAMS
_data_list = [l[:-1].split("|") for l in data_list]
self.data_list = [data if len(data) == 3 else (*data, 0) for data in _data_list]
self.text_cleaner = TextCleaner()
self.sr = sr
self.df = pd.DataFrame(self.data_list)
self.to_melspec = torchaudio.transforms.MelSpectrogram(**MEL_PARAMS)
self.mean, self.std = -4, 4
self.data_augmentation = data_augmentation and (not validation)
self.max_mel_length = 192
self.min_length = min_length
with open(OOD_data, "r") as f:
tl = f.readlines()
idx = 1 if ".wav" in tl[0].split("|")[0] else 0
self.ptexts = [t.split("|")[idx] for t in tl]
self.root_path = root_path
def __len__(self):
return len(self.data_list)
def __getitem__(self, idx):
data = self.data_list[idx]
path = data[0]
wave, text_tensor, speaker_id = self._load_tensor(data)
mel_tensor = preprocess(wave).squeeze()
acoustic_feature = mel_tensor.squeeze()
length_feature = acoustic_feature.size(1)
acoustic_feature = acoustic_feature[:, : (length_feature - length_feature % 2)]
# get reference sample
ref_data = (self.df[self.df[2] == str(speaker_id)]).sample(n=1).iloc[0].tolist()
ref_mel_tensor, ref_label = self._load_data(ref_data[:3])
# get OOD text
ps = ""
while len(ps) < self.min_length:
rand_idx = np.random.randint(0, len(self.ptexts) - 1)
ps = self.ptexts[rand_idx]
text = self.text_cleaner(ps)
text.insert(0, 0)
text.append(0)
ref_text = torch.LongTensor(text)
return (
speaker_id,
acoustic_feature,
text_tensor,
ref_text,
ref_mel_tensor,
ref_label,
path,
wave,
)
def _load_tensor(self, data):
wave_path, text, speaker_id = data
speaker_id = int(speaker_id)
wave, sr = sf.read(osp.join(self.root_path, wave_path))
if wave.shape[-1] == 2:
wave = wave[:, 0].squeeze()
if sr != 24000:
wave = librosa.resample(wave, orig_sr=sr, target_sr=24000)
print(wave_path, sr)
wave = np.concatenate([np.zeros([5000]), wave, np.zeros([5000])], axis=0)
text = self.text_cleaner(text)
text.insert(0, 0)
text.append(0)
text = torch.LongTensor(text)
return wave, text, speaker_id
def _load_data(self, data):
wave, text_tensor, speaker_id = self._load_tensor(data)
mel_tensor = preprocess(wave).squeeze()
mel_length = mel_tensor.size(1)
if mel_length > self.max_mel_length:
random_start = np.random.randint(0, mel_length - self.max_mel_length)
mel_tensor = mel_tensor[
:, random_start : random_start + self.max_mel_length
]
return mel_tensor, speaker_id
class Collater(object):
"""
Args:
adaptive_batch_size (bool): if true, decrease batch size when long data comes.
"""
def __init__(self, return_wave=False):
self.text_pad_index = 0
self.min_mel_length = 192
self.max_mel_length = 192
self.return_wave = return_wave
def __call__(self, batch):
# batch[0] = wave, mel, text, f0, speakerid
batch_size = len(batch)
# sort by mel length
lengths = [b[1].shape[1] for b in batch]
batch_indexes = np.argsort(lengths)[::-1]
batch = [batch[bid] for bid in batch_indexes]
nmels = batch[0][1].size(0)
max_mel_length = max([b[1].shape[1] for b in batch])
max_text_length = max([b[2].shape[0] for b in batch])
max_rtext_length = max([b[3].shape[0] for b in batch])
labels = torch.zeros((batch_size)).long()
mels = torch.zeros((batch_size, nmels, max_mel_length)).float()
texts = torch.zeros((batch_size, max_text_length)).long()
ref_texts = torch.zeros((batch_size, max_rtext_length)).long()
input_lengths = torch.zeros(batch_size).long()
ref_lengths = torch.zeros(batch_size).long()
output_lengths = torch.zeros(batch_size).long()
ref_mels = torch.zeros((batch_size, nmels, self.max_mel_length)).float()
ref_labels = torch.zeros((batch_size)).long()
paths = ["" for _ in range(batch_size)]
waves = [None for _ in range(batch_size)]
for bid, (
label,
mel,
text,
ref_text,
ref_mel,
ref_label,
path,
wave,
) in enumerate(batch):
mel_size = mel.size(1)
text_size = text.size(0)
rtext_size = ref_text.size(0)
labels[bid] = label
mels[bid, :, :mel_size] = mel
texts[bid, :text_size] = text
ref_texts[bid, :rtext_size] = ref_text
input_lengths[bid] = text_size
ref_lengths[bid] = rtext_size
output_lengths[bid] = mel_size
paths[bid] = path
ref_mel_size = ref_mel.size(1)
ref_mels[bid, :, :ref_mel_size] = ref_mel
ref_labels[bid] = ref_label
waves[bid] = wave
return (
waves,
texts,
input_lengths,
ref_texts,
ref_lengths,
mels,
output_lengths,
ref_mels,
)
def build_dataloader(
path_list,
root_path,
validation=False,
OOD_data="Data/OOD_texts.txt",
min_length=50,
batch_size=4,
num_workers=1,
device="cpu",
collate_config={},
dataset_config={},
):
dataset = FilePathDataset(
path_list,
root_path,
OOD_data=OOD_data,
min_length=min_length,
validation=validation,
**dataset_config
)
collate_fn = Collater(**collate_config)
data_loader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=(not validation),
num_workers=num_workers,
drop_last=(not validation),
collate_fn=collate_fn,
pin_memory=(device != "cpu"),
)
return data_loader