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import time
import os
import random
import numpy as np
import torch
import torch.utils.data
import commons
from mel_processing import spectrogram_torch, spec_to_mel_torch
from utils import load_wav_to_torch, load_filepaths_and_text, transform
# 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):
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(os.sep)[-2]
spk = torch.LongTensor([self.spk_map[spk]])
c = torch.load(filename + ".soft.pt").squeeze(0)
c = torch.repeat_interleave(c, repeats=2, dim=1)
f0 = np.load(filename + ".f0.npy")
f0 = torch.FloatTensor(f0)
lmin = min(c.size(-1), spec.size(-1), f0.shape[0])
assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape, filename)
assert abs(lmin - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
assert abs(lmin - c.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin]
audio_norm = audio_norm[:, :lmin * self.hop_length]
_spec, _c, _audio_norm, _f0 = spec, c, audio_norm, f0
while spec.size(-1) < self.spec_len:
spec = torch.cat((spec, _spec), -1)
c = torch.cat((c, _c), -1)
f0 = torch.cat((f0, _f0), -1)
audio_norm = torch.cat((audio_norm, _audio_norm), -1)
start = random.randint(0, spec.size(-1) - self.spec_len)
end = start + self.spec_len
spec = spec[:, start:end]
c = c[:, start:end]
f0 = f0[start:end]
audio_norm = audio_norm[:, start * self.hop_length:end * self.hop_length]
return c, f0, spec, audio_norm, spk
def __getitem__(self, index):
return self.get_audio(self.audiopaths[index][0])
def __len__(self):
return len(self.audiopaths)
class EvalDataLoader(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.audiopaths = self.audiopaths[:5]
self.spk_map = hparams.spk
def get_audio(self, filename):
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(os.sep)[-2]
spk = torch.LongTensor([self.spk_map[spk]])
c = torch.load(filename + ".soft.pt").squeeze(0)
c = torch.repeat_interleave(c, repeats=2, dim=1)
f0 = np.load(filename + ".f0.npy")
f0 = torch.FloatTensor(f0)
lmin = min(c.size(-1), spec.size(-1), f0.shape[0])
assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
assert abs(f0.shape[0] - spec.shape[-1]) < 4, (c.size(-1), spec.size(-1), f0.shape)
spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin]
audio_norm = audio_norm[:, :lmin * self.hop_length]
return c, f0, spec, audio_norm, spk
def __getitem__(self, index):
return self.get_audio(self.audiopaths[index][0])
def __len__(self):
return len(self.audiopaths)
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