/
data_loader.py
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data_loader.py
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import os
import torch
import pickle
import numpy as np
from functools import partial
from numpy.random import uniform
from multiprocessing import Process, Manager
from torch.utils import data
from torch.utils.data.sampler import Sampler
class Utterances(data.Dataset):
"""Dataset class for the Utterances dataset."""
def __init__(self, root_dir, feat_dir, mode):
"""Initialize and preprocess the Utterances dataset."""
self.root_dir = root_dir
self.feat_dir = feat_dir
self.mode = mode
self.step = 20
self.split = 0
metaname = os.path.join(self.root_dir, "train.pkl")
meta = pickle.load(open(metaname, "rb"))
manager = Manager()
meta = manager.list(meta)
dataset = manager.list(len(meta)*[None]) # <-- can be shared between processes.
processes = []
for i in range(0, len(meta), self.step):
p = Process(target=self.load_data,
args=(meta[i:i+self.step],dataset,i,mode))
p.start()
processes.append(p)
for p in processes:
p.join()
# very importtant to do dataset = list(dataset)
if mode == 'train':
self.train_dataset = list(dataset)
self.num_tokens = len(self.train_dataset)
elif mode == 'test':
self.test_dataset = list(dataset)
self.num_tokens = len(self.test_dataset)
else:
raise ValueError
print('Finished loading {} dataset...'.format(mode))
def load_data(self, submeta, dataset, idx_offset, mode):
for k, sbmt in enumerate(submeta):
uttrs = len(sbmt)*[None]
# fill in speaker id and embedding
uttrs[0] = sbmt[0]
uttrs[1] = sbmt[1]
# fill in data
sp_tmp = np.load(os.path.join(self.root_dir, sbmt[2]))
f0_tmp = np.load(os.path.join(self.feat_dir, sbmt[2]))
if self.mode == 'train':
sp_tmp = sp_tmp[self.split:, :]
f0_tmp = f0_tmp[self.split:]
elif self.mode == 'test':
sp_tmp = sp_tmp[:self.split, :]
f0_tmp = f0_tmp[:self.split]
else:
raise ValueError
uttrs[2] = ( sp_tmp, f0_tmp )
dataset[idx_offset+k] = uttrs
def __getitem__(self, index):
dataset = self.train_dataset if self.mode == 'train' else self.test_dataset
list_uttrs = dataset[index]
spk_id_org = list_uttrs[0]
emb_org = list_uttrs[1]
melsp, f0_org = list_uttrs[2]
return melsp, emb_org, f0_org
def __len__(self):
"""Return the number of spkrs."""
return self.num_tokens
class MyCollator(object):
def __init__(self, hparams):
self.min_len_seq = hparams.min_len_seq
self.max_len_seq = hparams.max_len_seq
self.max_len_pad = hparams.max_len_pad
def __call__(self, batch):
# batch[i] is a tuple of __getitem__ outputs
new_batch = []
for token in batch:
aa, b, c = token
len_crop = np.random.randint(self.min_len_seq, self.max_len_seq+1, size=2) # 1.5s ~ 3s
left = np.random.randint(0, len(aa)-len_crop[0], size=2)
pdb.set_trace()
a = aa[left[0]:left[0]+len_crop[0], :]
c = c[left[0]:left[0]+len_crop[0]]
a = np.clip(a, 0, 1)
a_pad = np.pad(a, ((0,self.max_len_pad-a.shape[0]),(0,0)), 'constant')
c_pad = np.pad(c[:,np.newaxis], ((0,self.max_len_pad-c.shape[0]),(0,0)), 'constant', constant_values=-1e10)
new_batch.append( (a_pad, b, c_pad, len_crop[0]) )
batch = new_batch
a, b, c, d = zip(*batch)
melsp = torch.from_numpy(np.stack(a, axis=0))
spk_emb = torch.from_numpy(np.stack(b, axis=0))
pitch = torch.from_numpy(np.stack(c, axis=0))
len_org = torch.from_numpy(np.stack(d, axis=0))
return melsp, spk_emb, pitch, len_org
class MultiSampler(Sampler):
"""Samples elements more than once in a single pass through the data.
"""
def __init__(self, num_samples, n_repeats, shuffle=False):
self.num_samples = num_samples
self.n_repeats = n_repeats
self.shuffle = shuffle
def gen_sample_array(self):
self.sample_idx_array = torch.arange(self.num_samples, dtype=torch.int64).repeat(self.n_repeats)
if self.shuffle:
self.sample_idx_array = self.sample_idx_array[torch.randperm(len(self.sample_idx_array))]
return self.sample_idx_array
def __iter__(self):
return iter(self.gen_sample_array())
def __len__(self):
return len(self.sample_idx_array)
def get_loader(hparams):
"""Build and return a data loader."""
dataset = Utterances(hparams.root_dir, hparams.feat_dir, hparams.mode)
my_collator = MyCollator(hparams)
sampler = MultiSampler(len(dataset), hparams.samplier, shuffle=hparams.shuffle)
worker_init_fn = lambda x: np.random.seed((torch.initial_seed()) % (2**32))
data_loader = data.DataLoader(dataset=dataset,
batch_size=hparams.batch_size,
sampler=sampler,
num_workers=hparams.num_workers,
drop_last=True,
pin_memory=True,
worker_init_fn=worker_init_fn,
collate_fn=my_collator)
return data_loader