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import os
import random
import torch
import torchaudio
import torch.utils.data
import torchaudio.functional as aF
def amp_pha_stft(audio, n_fft, hop_size, win_size, center=True):
hann_window = torch.hann_window(win_size).to(audio.device)
stft_spec = torch.stft(audio, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window,
center=center, pad_mode='reflect', normalized=False, return_complex=True)
log_amp = torch.log(torch.abs(stft_spec)+1e-4)
pha = torch.angle(stft_spec)
com = torch.stack((torch.exp(log_amp)*torch.cos(pha),
torch.exp(log_amp)*torch.sin(pha)), dim=-1)
return log_amp, pha, com
def amp_pha_istft(log_amp, pha, n_fft, hop_size, win_size, center=True):
amp = torch.exp(log_amp)
com = torch.complex(amp*torch.cos(pha), amp*torch.sin(pha))
hann_window = torch.hann_window(win_size).to(com.device)
audio = torch.istft(com, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window, center=center)
return audio
def get_dataset_filelist(a):
with open(a.input_training_file, 'r', encoding='utf-8') as fi:
training_indexes = [x.split('|')[0] for x in fi.read().split('\n') if len(x) > 0]
with open(a.input_validation_file, 'r', encoding='utf-8') as fi:
validation_indexes = [x.split('|')[0] for x in fi.read().split('\n') if len(x) > 0]
return training_indexes, validation_indexes
class Dataset(torch.utils.data.Dataset):
def __init__(self, training_indexes, wavs_dir, segment_size, hr_sampling_rate, lr_sampling_rate,
split=True, shuffle=True, n_cache_reuse=1, device=None):
self.audio_indexes = training_indexes
random.seed(1234)
if shuffle:
random.shuffle(self.audio_indexes)
self.wavs_dir = wavs_dir
self.segment_size = segment_size
self.hr_sampling_rate = hr_sampling_rate
self.lr_sampling_rate = lr_sampling_rate
self.split = split
self.cached_wav = None
self.n_cache_reuse = n_cache_reuse
self._cache_ref_count = 0
self.device = device
def __getitem__(self, index):
filename = self.audio_indexes[index]
if self._cache_ref_count == 0:
audio, orig_sampling_rate = torchaudio.load(os.path.join(self.wavs_dir, filename + '.wav'))
self.cached_wav = audio
self._cache_ref_count = self.n_cache_reuse
else:
audio = self.cached_wav
self._cache_ref_count -= 1
if orig_sampling_rate == self.hr_sampling_rate:
audio_hr = audio
else:
audio_hr = aF.resample(audio, orig_freq=orig_sampling_rate, new_freq=self.hr_sampling_rate)
audio_lr = aF.resample(audio, orig_freq=orig_sampling_rate, new_freq=self.lr_sampling_rate)
audio_lr = aF.resample(audio_lr, orig_freq=self.lr_sampling_rate, new_freq=self.hr_sampling_rate)
audio_lr = audio_lr[:, : audio_hr.size(1)]
if self.split:
if audio_hr.size(1) >= self.segment_size:
max_audio_start = audio_hr.size(1) - self.segment_size
audio_start = random.randint(0, max_audio_start)
audio_hr = audio_hr[:, audio_start: audio_start+self.segment_size]
audio_lr = audio_lr[:, audio_start: audio_start+self.segment_size]
else:
audio_hr = torch.nn.functional.pad(audio_hr, (0, self.segment_size - audio_hr.size(1)), 'constant')
audio_lr = torch.nn.functional.pad(audio_lr, (0, self.segment_size - audio_lr.size(1)), 'constant')
return (audio_hr.squeeze(), audio_lr.squeeze())
def __len__(self):
return len(self.audio_indexes)
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