| 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) | |