File size: 7,548 Bytes
2e62044 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 |
import pickle
import random
import torch
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import Sampler
from utils.dsp import *
from utils import hparams as hp
from utils.text import text_to_sequence
from utils.paths import Paths
from pathlib import Path
from functools import partial
###################################################################################
# WaveRNN/Vocoder Dataset #########################################################
###################################################################################
class VocoderDataset(Dataset):
def __init__(self, path: Path, dataset_ids, train_gta=False):
self.metadata = dataset_ids
self.mel_path = path/'gta' if train_gta else path/'mel'
self.quant_path = path/'quant'
def __getitem__(self, index):
item_id = self.metadata[index]
m = np.load(self.mel_path/f'{item_id}.npy')
x = np.load(self.quant_path/f'{item_id}.npy')
return m, x
def __len__(self):
return len(self.metadata)
def get_vocoder_datasets(path: Path, batch_size, train_gta):
with open(path/'dataset.pkl', 'rb') as f:
dataset = pickle.load(f)
dataset_ids = [x[0] for x in dataset]
random.seed(1234)
random.shuffle(dataset_ids)
test_ids = dataset_ids[-hp.voc_test_samples:]
train_ids = dataset_ids[:-hp.voc_test_samples]
train_dataset = VocoderDataset(path, train_ids, train_gta)
test_dataset = VocoderDataset(path, test_ids, train_gta)
train_set = DataLoader(train_dataset,
collate_fn=collate_vocoder,
batch_size=batch_size,
num_workers=2,
shuffle=True,
pin_memory=True)
test_set = DataLoader(test_dataset,
batch_size=1,
num_workers=1,
shuffle=False,
pin_memory=True)
return train_set, test_set
def collate_vocoder(batch):
if not hp.is_configured():
print("未配置参数")
hp.configure("E:\\智能语音处理系统\\Noise-suppression-and-speech-recognition-systems-master\\WaveRNNModel\\hparams.py")
mel_win = hp.voc_seq_len // hp.hop_length + 2 * hp.voc_pad
max_offsets = [x[0].shape[-1] -2 - (mel_win + 2 * hp.voc_pad) for x in batch]
mel_offsets = [np.random.randint(0, offset) for offset in max_offsets]
sig_offsets = [(offset + hp.voc_pad) * hp.hop_length for offset in mel_offsets]
mels = [x[0][:, mel_offsets[i]:mel_offsets[i] + mel_win] for i, x in enumerate(batch)]
labels = [x[1][sig_offsets[i]:sig_offsets[i] + hp.voc_seq_len + 1] for i, x in enumerate(batch)]
mels = np.stack(mels).astype(np.float32)
labels = np.stack(labels).astype(np.int64)
mels = torch.tensor(mels)
labels = torch.tensor(labels).long()
x = labels[:, :hp.voc_seq_len]
y = labels[:, 1:]
bits = 16 if hp.voc_mode == 'MOL' else hp.bits
x = label_2_float(x.float(), bits)
if hp.voc_mode == 'MOL':
y = label_2_float(y.float(), bits)
return x, y, mels
###################################################################################
# Tacotron/TTS Dataset ############################################################
###################################################################################
def get_tts_datasets(path: Path, batch_size, r):
print("path",path)
with open(path/'dataset.pkl', 'rb') as f:
dataset = pickle.load(f)
dataset_ids = []
mel_lengths = []
print("hp.tts_max_mel_len",hp.tts_max_mel_len)
for (item_id, len) in dataset:
if len <= hp.tts_max_mel_len:
dataset_ids += [item_id]
mel_lengths += [len]
with open(path/'text_dict.pkl', 'rb') as f:
text_dict = pickle.load(f)
train_dataset = TTSDataset(path, dataset_ids, text_dict)
sampler = None
if hp.tts_bin_lengths:
sampler = BinnedLengthSampler(mel_lengths, batch_size, batch_size * 3)
train_set = DataLoader(train_dataset,
collate_fn=partial(collate_tts, r=r),
batch_size=batch_size,
sampler=sampler,
num_workers=1,
pin_memory=True)
longest = mel_lengths.index(max(mel_lengths))
# Used to evaluate attention during training process
attn_example = dataset_ids[longest]
# print(attn_example)
return train_set, attn_example
class TTSDataset(Dataset):
def __init__(self, path: Path, dataset_ids, text_dict):
self.path = path
self.metadata = dataset_ids
self.text_dict = text_dict
def __getitem__(self, index):
item_id = self.metadata[index]
#print("path555555",self.path)
if not hp.is_configured():
print("未配置参数")
hp.configure("E:\\智能语音处理系统\\Noise-suppression-and-speech-recognition-systems-master\\WaveRNNModel\\hparams.py")
#print("test666666",hp.tts_cleaner_names)
x = text_to_sequence(self.text_dict[item_id], hp.tts_cleaner_names)
mel = np.load(self.path/'mel'/f'{item_id}.npy')
mel_len = mel.shape[-1]
return x, mel, item_id, mel_len
def __len__(self):
return len(self.metadata)
def pad1d(x, max_len):
return np.pad(x, (0, max_len - len(x)), mode='constant')
def pad2d(x, max_len):
return np.pad(x, ((0, 0), (0, max_len - x.shape[-1])), mode='constant')
def collate_tts(batch, r):
x_lens = [len(x[0]) for x in batch]
max_x_len = max(x_lens)
chars = [pad1d(x[0], max_x_len) for x in batch]
chars = np.stack(chars)
spec_lens = [x[1].shape[-1] for x in batch]
max_spec_len = max(spec_lens) + 1
if max_spec_len % r != 0:
max_spec_len += r - max_spec_len % r
mel = [pad2d(x[1], max_spec_len) for x in batch]
mel = np.stack(mel)
ids = [x[2] for x in batch]
mel_lens = [x[3] for x in batch]
chars = torch.tensor(chars).long()
mel = torch.tensor(mel)
# scale spectrograms to -4 <--> 4
mel = (mel * 8.) - 4.
return chars, mel, ids, mel_lens
class BinnedLengthSampler(Sampler):
def __init__(self, lengths, batch_size, bin_size):
_, self.idx = torch.sort(torch.tensor(lengths).long())
self.batch_size = batch_size
self.bin_size = bin_size
assert self.bin_size % self.batch_size == 0
def __iter__(self):
# Need to change to numpy since there's a bug in random.shuffle(tensor)
# TODO: Post an issue on pytorch repo
idx = self.idx.numpy()
bins = []
for i in range(len(idx) // self.bin_size):
this_bin = idx[i * self.bin_size:(i + 1) * self.bin_size]
random.shuffle(this_bin)
bins += [this_bin]
random.shuffle(bins)
binned_idx = np.stack(bins).reshape(-1)
if len(binned_idx) < len(idx):
last_bin = idx[len(binned_idx):]
random.shuffle(last_bin)
binned_idx = np.concatenate([binned_idx, last_bin])
return iter(torch.tensor(binned_idx).long())
def __len__(self):
return len(self.idx)
|