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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, all_in_mem: bool = False): | |
| 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) | |
| self.all_in_mem = all_in_mem | |
| if self.all_in_mem: | |
| self.cache = [self.get_audio(p[0]) for p in 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") | |
| # Ideally, all data generated after Mar 25 should have .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] | |
| return c, f0, spec, audio_norm, spk, uv | |
| def random_slice(self, c, f0, spec, audio_norm, spk, uv): | |
| # if spec.shape[1] < 30: | |
| # 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): | |
| if self.all_in_mem: | |
| return self.random_slice(*self.cache[index]) | |
| else: | |
| return self.random_slice(*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 | |