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)