Spaces:
Runtime error
Runtime error
File size: 8,291 Bytes
7ce5feb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 |
import os
import pickle
import torch
import numpy as np
from numpy.random import uniform
from torch.utils import data
from torch.utils.data.sampler import Sampler
from multiprocessing import Process, Manager
class Utterances(data.Dataset):
"""Dataset class for the Utterances dataset."""
def __init__(self, hparams):
"""Initialize and preprocess the Utterances dataset."""
self.meta_file = hparams.meta_file
self.feat_dir_1 = hparams.feat_dir_1
self.feat_dir_2 = hparams.feat_dir_2
self.feat_dir_3 = hparams.feat_dir_3
self.step = 4
self.split = 0
self.max_len_pad = hparams.max_len_pad
meta = pickle.load(open(self.meta_file, "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))
p.start()
processes.append(p)
for p in processes:
p.join()
# very importtant to do dataset = list(dataset)
self.train_dataset = list(dataset)
self.num_tokens = len(self.train_dataset)
print('Finished loading the {} Utterances training dataset...'.format(self.num_tokens))
def load_data(self, submeta, dataset, idx_offset):
for k, sbmt in enumerate(submeta):
uttrs = len(sbmt)*[None]
for j, tmp in enumerate(sbmt):
if j < 2:
# fill in speaker name and embedding
uttrs[j] = tmp
else:
# fill in data
sp_tmp = np.load(os.path.join(self.feat_dir_1, tmp))
cep_tmp = np.load(os.path.join(self.feat_dir_2, tmp))[:, 0:14]
cd_tmp = np.load(os.path.join(self.feat_dir_3, tmp))
assert len(sp_tmp) == len(cep_tmp) == len(cd_tmp)
uttrs[j] = ( np.clip(sp_tmp, 0, 1), cep_tmp, cd_tmp )
dataset[idx_offset+k] = uttrs
def segment_np(self, cd_long, tau=2):
cd_norm = np.sqrt((cd_long ** 2).sum(axis=-1, keepdims=True))
G = (cd_long @ cd_long.T) / (cd_norm @ cd_norm.T)
L = G.shape[0]
num_rep = []
num_rep_sync = []
prev_boundary = 0
rate = np.random.uniform(0.8, 1.3)
for t in range(1, L+1):
if t==L:
num_rep.append(t - prev_boundary)
num_rep_sync.append(t - prev_boundary)
prev_boundary = t
if t < L:
q = np.random.uniform(rate-0.1, rate)
tmp = G[prev_boundary, max(prev_boundary-20, 0):min(prev_boundary+20, L)]
if q <= 1:
epsilon = np.quantile(tmp, q)
if np.all(G[prev_boundary, t:min(t+tau, L)] < epsilon):
num_rep.append(t - prev_boundary)
num_rep_sync.append(t - prev_boundary)
prev_boundary = t
else:
epsilon = np.quantile(tmp, 2-q)
if np.all(G[prev_boundary, t:min(t+tau, L)] < epsilon):
num_rep.append(t - prev_boundary)
else:
num_rep.extend([t-prev_boundary-0.5, 0.5])
num_rep_sync.append(t - prev_boundary)
prev_boundary = t
num_rep = np.array(num_rep)
num_rep_sync = np.array(num_rep_sync)
return num_rep, num_rep_sync
def __getitem__(self, index):
"""Return M uttrs for one spkr."""
dataset = self.train_dataset
list_uttrs = dataset[index]
emb_org = list_uttrs[1]
uttr = np.random.randint(2, len(list_uttrs))
melsp, melcep, cd_real = list_uttrs[uttr]
num_rep, num_rep_sync = self.segment_np(cd_real)
return melsp, melcep, cd_real, num_rep, num_rep_sync, len(melsp), len(num_rep), len(num_rep_sync), emb_org
def __len__(self):
"""Return the number of spkrs."""
return self.num_tokens
class MyCollator(object):
def __init__(self, hparams):
self.max_len_pad = hparams.max_len_pad
def __call__(self, batch):
new_batch = []
l_short_max = 0
l_short_sync_max = 0
l_real_max = 0
for token in batch:
sp_real, cep_real, cd_real, rep, rep_sync, l_real, l_short, l_short_sync, emb = token
if l_short > l_short_max:
l_short_max = l_short
if l_short_sync > l_short_sync_max:
l_short_sync_max = l_short_sync
if l_real > l_real_max:
l_real_max = l_real
sp_real_pad = np.pad(sp_real, ((0,self.max_len_pad-l_real),(0,0)), 'constant')
cep_real_pad = np.pad(cep_real, ((0,self.max_len_pad-l_real),(0,0)), 'constant')
cd_real_pad = np.pad(cd_real, ((0,self.max_len_pad-l_real),(0,0)), 'constant')
rep_pad = np.pad(rep, (0,self.max_len_pad-l_short), 'constant')
rep_sync_pad = np.pad(rep_sync, (0,self.max_len_pad-l_short_sync), 'constant')
new_batch.append( (sp_real_pad, cep_real_pad, cd_real_pad, rep_pad, rep_sync_pad, l_real, l_short, l_short_sync, emb) )
batch = new_batch
a, b, c, d, e, f, g, h, i = zip(*batch)
sp_real = torch.from_numpy(np.stack(a, axis=0))[:,:l_real_max+1,:]
cep_real = torch.from_numpy(np.stack(b, axis=0))[:,:l_real_max+1,:]
cd_real = torch.from_numpy(np.stack(c, axis=0))[:,:l_real_max+1,:]
num_rep = torch.from_numpy(np.stack(d, axis=0))[:,:l_short_max+1]
num_rep_sync = torch.from_numpy(np.stack(e, axis=0))[:,:l_short_sync_max+1]
len_real = torch.from_numpy(np.stack(f, axis=0))
len_short = torch.from_numpy(np.stack(g, axis=0))
len_short_sync = torch.from_numpy(np.stack(h, axis=0))
spk_emb = torch.from_numpy(np.stack(i, axis=0))
return sp_real, cep_real, cd_real, num_rep, num_rep_sync, len_real, len_short, len_short_sync, spk_emb
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 worker_init_fn(x):
return np.random.seed((torch.initial_seed()) % (2**32))
def get_loader(hparams):
"""Build and return a data loader."""
dataset = Utterances(hparams)
my_collator = MyCollator(hparams)
sampler = MultiSampler(len(dataset), hparams.samplier, shuffle=hparams.shuffle)
data_loader = data.DataLoader(dataset=dataset,
batch_size=hparams.batch_size,
sampler=sampler,
num_workers=hparams.num_workers,
drop_last=True,
pin_memory=False,
worker_init_fn=worker_init_fn,
collate_fn=my_collator)
return data_loader |