Spaces:
Runtime error
Runtime error
File size: 5,463 Bytes
d051d21 |
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 |
import time
import os
import random
import numpy as np
import torch
import torch.utils.data
import modules.commons as commons
import utils
from modules.mel_processing import spectrogram_torch, spec_to_mel_torch
from utils import load_wav_to_torch, load_filepaths_and_text
# import h5py
"""Multi speaker version"""
class TextAudioSpeakerLoader(torch.utils.data.Dataset):
"""
1) loads audio, speaker_id, text pairs
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
def __init__(self, audiopaths, hparams, all_in_mem: bool = False):
self.audiopaths = load_filepaths_and_text(audiopaths)
self.max_wav_value = hparams.data.max_wav_value
self.sampling_rate = hparams.data.sampling_rate
self.filter_length = hparams.data.filter_length
self.hop_length = hparams.data.hop_length
self.win_length = hparams.data.win_length
self.sampling_rate = hparams.data.sampling_rate
self.use_sr = hparams.train.use_sr
self.spec_len = hparams.train.max_speclen
self.spk_map = hparams.spk
random.seed(1234)
random.shuffle(self.audiopaths)
self.all_in_mem = all_in_mem
if self.all_in_mem:
self.cache = [self.get_audio(p[0]) for p in self.audiopaths]
def get_audio(self, filename):
filename = filename.replace("\\", "/")
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.sampling_rate:
raise ValueError("{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate))
audio_norm = audio / self.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
# Ideally, all data generated after Mar 25 should have .spec.pt
if os.path.exists(spec_filename):
spec = torch.load(spec_filename)
else:
spec = spectrogram_torch(audio_norm, self.filter_length,
self.sampling_rate, self.hop_length, self.win_length,
center=False)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename)
spk = filename.split("/")[-2]
spk = torch.LongTensor([self.spk_map[spk]])
f0 = np.load(filename + ".f0.npy")
f0, uv = utils.interpolate_f0(f0)
f0 = torch.FloatTensor(f0)
uv = torch.FloatTensor(uv)
c = torch.load(filename+ ".soft.pt")
c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[0])
lmin = min(c.size(-1), spec.size(-1))
assert abs(c.size(-1) - spec.size(-1)) < 3, (c.size(-1), spec.size(-1), f0.shape, filename)
assert abs(audio_norm.shape[1]-lmin * self.hop_length) < 3 * self.hop_length
spec, c, f0, uv = spec[:, :lmin], c[:, :lmin], f0[:lmin], uv[:lmin]
audio_norm = audio_norm[:, :lmin * self.hop_length]
return c, f0, spec, audio_norm, spk, uv
def random_slice(self, c, f0, spec, audio_norm, spk, uv):
# if spec.shape[1] < 30:
# print("skip too short audio:", filename)
# return None
if spec.shape[1] > 800:
start = random.randint(0, spec.shape[1]-800)
end = start + 790
spec, c, f0, uv = spec[:, start:end], c[:, start:end], f0[start:end], uv[start:end]
audio_norm = audio_norm[:, start * self.hop_length : end * self.hop_length]
return c, f0, spec, audio_norm, spk, uv
def __getitem__(self, index):
if self.all_in_mem:
return self.random_slice(*self.cache[index])
else:
return self.random_slice(*self.get_audio(self.audiopaths[index][0]))
def __len__(self):
return len(self.audiopaths)
class TextAudioCollate:
def __call__(self, batch):
batch = [b for b in batch if b is not None]
input_lengths, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[0].shape[1] for x in batch]),
dim=0, descending=True)
max_c_len = max([x[0].size(1) for x in batch])
max_wav_len = max([x[3].size(1) for x in batch])
lengths = torch.LongTensor(len(batch))
c_padded = torch.FloatTensor(len(batch), batch[0][0].shape[0], max_c_len)
f0_padded = torch.FloatTensor(len(batch), max_c_len)
spec_padded = torch.FloatTensor(len(batch), batch[0][2].shape[0], max_c_len)
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
spkids = torch.LongTensor(len(batch), 1)
uv_padded = torch.FloatTensor(len(batch), max_c_len)
c_padded.zero_()
spec_padded.zero_()
f0_padded.zero_()
wav_padded.zero_()
uv_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
c = row[0]
c_padded[i, :, :c.size(1)] = c
lengths[i] = c.size(1)
f0 = row[1]
f0_padded[i, :f0.size(0)] = f0
spec = row[2]
spec_padded[i, :, :spec.size(1)] = spec
wav = row[3]
wav_padded[i, :, :wav.size(1)] = wav
spkids[i, 0] = row[4]
uv = row[5]
uv_padded[i, :uv.size(0)] = uv
return c_padded, f0_padded, spec_padded, wav_padded, spkids, lengths, uv_padded
|