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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):
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)
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")
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]
if spec.shape[1] < 60:
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):
return 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
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