| import math | |
| import os | |
| import random | |
| import torch | |
| import torch.utils.data | |
| import numpy as np | |
| from librosa.util import normalize | |
| from scipy.io.wavfile import read | |
| from librosa.filters import mel as librosa_mel_fn | |
| MAX_WAV_VALUE = 32768.0 | |
| def load_wav(full_path): | |
| sampling_rate, data = read(full_path) | |
| return data, sampling_rate | |
| def dynamic_range_compression(x, C=1, clip_val=1e-5): | |
| return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) | |
| def dynamic_range_decompression(x, C=1): | |
| return np.exp(x) / C | |
| def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): | |
| return torch.log(torch.clamp(x, min=clip_val) * C) | |
| def dynamic_range_decompression_torch(x, C=1): | |
| return torch.exp(x) / C | |
| def spectral_normalize_torch(magnitudes): | |
| output = dynamic_range_compression_torch(magnitudes) | |
| return output | |
| def spectral_de_normalize_torch(magnitudes): | |
| output = dynamic_range_decompression_torch(magnitudes) | |
| return output | |
| mel_basis = {} | |
| hann_window = {} | |
| def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): | |
| if torch.min(y) < -1.: | |
| print('min value is ', torch.min(y)) | |
| if torch.max(y) > 1.: | |
| print('max value is ', torch.max(y)) | |
| global mel_basis, hann_window | |
| if fmax not in mel_basis: | |
| mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) | |
| mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device) | |
| hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) | |
| y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') | |
| y = y.squeeze(1) | |
| spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)], | |
| center=center, pad_mode='reflect', normalized=False, onesided=True) | |
| spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9)) | |
| spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec) | |
| spec = spectral_normalize_torch(spec) | |
| return spec | |
| def get_dataset_filelist(a): | |
| with open(a.input_training_file, 'r', encoding='utf-8') as fi: | |
| training_files = [os.path.join(a.input_wavs_dir, 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_files = [os.path.join(a.input_wavs_dir, x.split('|')[0]) | |
| for x in fi.read().split('\n') if len(x) > 0] | |
| return training_files, validation_files | |
| class MelDataset(torch.utils.data.Dataset): | |
| def __init__(self, training_files, segment_size, n_fft, num_mels, | |
| hop_size, win_size, sampling_rate, fmin, fmax, split=True, shuffle=True, n_cache_reuse=1, | |
| device=None, fmax_loss=None, fine_tuning=False, base_mels_path=None): | |
| self.audio_files = training_files | |
| random.seed(1234) | |
| if shuffle: | |
| random.shuffle(self.audio_files) | |
| self.segment_size = segment_size | |
| self.sampling_rate = sampling_rate | |
| self.split = split | |
| self.n_fft = n_fft | |
| self.num_mels = num_mels | |
| self.hop_size = hop_size | |
| self.win_size = win_size | |
| self.fmin = fmin | |
| self.fmax = fmax | |
| self.fmax_loss = fmax_loss | |
| self.cached_wav = None | |
| self.n_cache_reuse = n_cache_reuse | |
| self._cache_ref_count = 0 | |
| self.device = device | |
| self.fine_tuning = fine_tuning | |
| self.base_mels_path = base_mels_path | |
| def __getitem__(self, index): | |
| filename = self.audio_files[index] | |
| if self._cache_ref_count == 0: | |
| audio, sampling_rate = load_wav(filename) | |
| audio = audio / MAX_WAV_VALUE | |
| if not self.fine_tuning: | |
| audio = normalize(audio) * 0.95 | |
| self.cached_wav = audio | |
| if sampling_rate != self.sampling_rate: | |
| raise ValueError("{} SR doesn't match target {} SR".format( | |
| sampling_rate, self.sampling_rate)) | |
| self._cache_ref_count = self.n_cache_reuse | |
| else: | |
| audio = self.cached_wav | |
| self._cache_ref_count -= 1 | |
| audio = torch.FloatTensor(audio) | |
| audio = audio.unsqueeze(0) | |
| if not self.fine_tuning: | |
| if self.split: | |
| if audio.size(1) >= self.segment_size: | |
| max_audio_start = audio.size(1) - self.segment_size | |
| audio_start = random.randint(0, max_audio_start) | |
| audio = audio[:, audio_start:audio_start+self.segment_size] | |
| else: | |
| audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant') | |
| mel = mel_spectrogram(audio, self.n_fft, self.num_mels, | |
| self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax, | |
| center=False) | |
| else: | |
| mel = np.load( | |
| os.path.join(self.base_mels_path, os.path.splitext(filename)[0] + '.npy')) | |
| mel = torch.from_numpy(mel) | |
| if len(mel.shape) < 3: | |
| mel = mel.unsqueeze(0) | |
| if self.split: | |
| frames_per_seg = math.ceil(self.segment_size / self.hop_size) | |
| if audio.size(1) >= self.segment_size: | |
| mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1) | |
| mel = mel[:, :, mel_start:mel_start + frames_per_seg] | |
| audio = audio[:, mel_start * self.hop_size:(mel_start + frames_per_seg) * self.hop_size] | |
| else: | |
| mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), 'constant') | |
| audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant') | |
| mel_loss = mel_spectrogram(audio, self.n_fft, self.num_mels, | |
| self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax_loss, | |
| center=False) | |
| return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze()) | |
| def __len__(self): | |
| return len(self.audio_files) | |