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meldataset.py
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meldataset.py
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#coding: utf-8
"""
TODO:
- make TestDataset
- separate transforms
"""
import os
import os.path as osp
import time
import random
import numpy as np
import random
import soundfile as sf
import torch
from torch import nn
import torch.nn.functional as F
import torchaudio
from torch.utils.data import DataLoader
import pyworld as pw
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
np.random.seed(1)
random.seed(1)
SPECT_PARAMS = {
"n_fft": 2048,
"win_length": 1200,
"hop_length": 300
}
MEL_PARAMS = {
"n_mels": 80,
"n_fft": 2048,
"win_length": 1200,
"hop_length": 300
}
class MelDataset(torch.utils.data.Dataset):
def __init__(self,
data_list,
sr=24000,
data_augmentation=False,
validation=False,
verbose=True
):
_data_list = [l[:-1].split('|') for l in data_list]
self.data_list = [d[0] for d in _data_list]
self.sr = sr
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.mean, self.std = -4, 4
self.verbose = verbose
# for silence detection
self.zero_value = -10 # what the zero value is
self.bad_F0 = 5 # if less than 5 frames are non-zero, it's a bad F0, try another algorithm
def __len__(self):
return len(self.data_list)
def path_to_mel_and_label(self, path):
wave_tensor = self._load_tensor(path)
# use pyworld to get F0
output_file = path + "_f0.npy"
# check if the file exists
if os.path.isfile(output_file): # if exists, load it directly
f0 = np.load(output_file)
else: # if not exist, create F0 file
if self.verbose:
print('Computing F0 for ' + path + '...')
x = wave_tensor.numpy().astype("double")
frame_period = MEL_PARAMS['hop_length'] * 1000 / self.sr
_f0, t = pw.harvest(x, self.sr, frame_period=frame_period)
if sum(_f0 != 0) < self.bad_F0: # this happens when the algorithm fails
_f0, t = pw.dio(x, self.sr, frame_period=frame_period) # if harvest fails, try dio
f0 = pw.stonemask(x, _f0, t, self.sr)
# save the f0 info for later use
np.save(output_file, f0)
f0 = torch.from_numpy(f0).float()
if self.data_augmentation:
random_scale = 0.5 + 0.5 * np.random.random()
wave_tensor = random_scale * wave_tensor
mel_tensor = self.to_melspec(wave_tensor)
mel_tensor = (torch.log(1e-5 + mel_tensor) - self.mean) / self.std
mel_length = mel_tensor.size(1)
f0_zero = (f0 == 0)
#######################################
# You may want your own silence labels here
# The more accurate the label, the better the resultss
is_silence = torch.zeros(f0.shape)
is_silence[f0_zero] = 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]
f0 = f0[random_start:random_start + self.max_mel_length]
is_silence = is_silence[random_start:random_start + self.max_mel_length]
if torch.any(torch.isnan(f0)): # failed
f0[torch.isnan(f0)] = self.zero_value # replace nan value with 0
return mel_tensor, f0, is_silence
def __getitem__(self, idx):
data = self.data_list[idx]
mel_tensor, f0, is_silence = self.path_to_mel_and_label(data)
return mel_tensor, f0, is_silence
def _load_tensor(self, data):
wave_path = data
wave, sr = sf.read(wave_path)
wave_tensor = torch.from_numpy(wave).float()
return wave_tensor
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.return_wave = return_wave
self.min_mel_length = 192
self.max_mel_length = 192
self.mel_length_step = 16
self.latent_dim = 16
def __call__(self, batch):
# batch[0] = wave, mel, text, f0, speakerid
batch_size = len(batch)
nmels = batch[0][0].size(0)
mels = torch.zeros((batch_size, nmels, self.max_mel_length)).float()
f0s = torch.zeros((batch_size, self.max_mel_length)).float()
is_silences = torch.zeros((batch_size, self.max_mel_length)).float()
for bid, (mel, f0, is_silence) in enumerate(batch):
mel_size = mel.size(1)
mels[bid, :, :mel_size] = mel
f0s[bid, :mel_size] = f0
is_silences[bid, :mel_size] = is_silence
if self.max_mel_length > self.min_mel_length:
random_slice = np.random.randint(
self.min_mel_length//self.mel_length_step,
1+self.max_mel_length//self.mel_length_step) * self.mel_length_step + self.min_mel_length
mels = mels[:, :, :random_slice]
f0 = f0[:, :random_slice]
mels = mels.unsqueeze(1)
return mels, f0s, is_silences
def build_dataloader(path_list,
validation=False,
batch_size=4,
num_workers=1,
device='cpu',
collate_config={},
dataset_config={}):
dataset = MelDataset(path_list, 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