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data.py
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data.py
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import torch
from torch.utils.data import Dataset
import numpy as np
import os
max_time_steps = 16000
upsample_conditional_features = True
hop_length = 256
class LJspeechDataset(Dataset):
def __init__(self, data_root, train=True, test_size=0.05):
self.data_root = data_root
self.lengths = []
self.train = train
self.test_size = test_size
self.paths = [self.collect_files(0), self.collect_files(1)]
def __len__(self):
return len(self.paths[0])
def __getitem__(self, idx):
wav = np.load(self.paths[0][idx])
mel = np.load(self.paths[1][idx])
return wav, mel
def interest_indices(self, paths):
test_num_samples = int(self.test_size * len(paths))
train_indices, test_indices = range(0, len(paths) - test_num_samples), \
range(len(paths) - test_num_samples, len(paths))
return train_indices if self.train else test_indices
def collect_files(self, col):
meta = os.path.join(self.data_root, "train.txt")
with open(meta, "rb") as f:
lines = f.readlines()
l = lines[0].decode("utf-8").split("|")
assert len(l) == 4
self.lengths = list(
map(lambda l: int(l.decode("utf-8").split("|")[2]), lines))
paths = list(map(lambda l: l.decode("utf-8").split("|")[col], lines))
paths = list(map(lambda f: os.path.join(self.data_root, f), paths))
# Filter by train/test
indices = self.interest_indices(paths)
paths = list(np.array(paths)[indices])
self.lengths = list(np.array(self.lengths)[indices])
self.lengths = list(map(int, self.lengths))
return paths
def _pad(seq, max_len, constant_values=0):
return np.pad(seq, (0, max_len - len(seq)),
mode='constant', constant_values=constant_values)
def _pad_2d(x, max_len, b_pad=0):
x = np.pad(x, [(b_pad, max_len - len(x) - b_pad), (0, 0)],
mode="constant", constant_values=0)
return x
def collate_fn(batch):
"""
Create batch
Args : batch(tuple) : List of tuples / (x, c) x : list of (T,) c : list of (T, D)
Returns : Tuple of batch / Network inputs x (B, C, T), Network targets (B, T, 1)
"""
local_conditioning = len(batch[0]) >= 2
if local_conditioning:
new_batch = []
for idx in range(len(batch)):
x, c = batch[idx]
if upsample_conditional_features:
assert len(x) % len(c) == 0 and len(x) // len(c) == hop_length
max_steps = max_time_steps - max_time_steps % hop_length # To ensure Divisibility
if len(x) > max_steps:
max_time_frames = max_steps // hop_length
s = np.random.randint(0, len(c) - max_time_frames)
ts = s * hop_length
x = x[ts:ts + hop_length * max_time_frames]
c = c[s:s + max_time_frames]
assert len(x) % len(c) == 0 and len(x) // len(c) == hop_length
else:
pass
new_batch.append((x, c))
batch = new_batch
else:
pass
input_lengths = [len(x[0]) for x in batch]
max_input_len = max(input_lengths)
# x_batch : [B, T, 1]
x_batch = np.array([_pad_2d(x[0].reshape(-1, 1), max_input_len) for x in batch], dtype=np.float32)
assert len(x_batch.shape) == 3
if local_conditioning:
max_len = max([len(x[1]) for x in batch])
c_batch = np.array([_pad_2d(x[1], max_len) for x in batch], dtype=np.float32)
assert len(c_batch.shape) == 3
# (B x C x T')
c_batch = torch.tensor(c_batch).transpose(1, 2).contiguous()
del max_len
else:
c_batch = None
# Convert to channel first i.e., (B, C, T) / C = 1
x_batch = torch.tensor(x_batch).transpose(1, 2).contiguous()
return x_batch, c_batch
def collate_fn_synthesize(batch):
"""
Create batch
Args : batch(tuple) : List of tuples / (x, c) x : list of (T,) c : list of (T, D)
Returns : Tuple of batch / Network inputs x (B, C, T), Network targets (B, T, 1)
"""
local_conditioning = len(batch[0]) >= 2
if local_conditioning:
new_batch = []
for idx in range(len(batch)):
x, c = batch[idx]
if upsample_conditional_features:
assert len(x) % len(c) == 0 and len(x) // len(c) == hop_length
new_batch.append((x, c))
batch = new_batch
else:
pass
input_lengths = [len(x[0]) for x in batch]
max_input_len = max(input_lengths)
x_batch = np.array([_pad_2d(x[0].reshape(-1, 1), max_input_len) for x in batch], dtype=np.float32)
assert len(x_batch.shape) == 3
if local_conditioning:
max_len = max([len(x[1]) for x in batch])
c_batch = np.array([_pad_2d(x[1], max_len) for x in batch], dtype=np.float32)
assert len(c_batch.shape) == 3
# (B x C x T')
c_batch = torch.tensor(c_batch).transpose(1, 2).contiguous()
else:
c_batch = None
# Convert to channel first i.e., (B, C, T) / C = 1
x_batch = torch.tensor(x_batch).transpose(1, 2).contiguous()
return x_batch, c_batch