| 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 |
|
|
| |
|
|
|
|
| """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) |
|
|
|
|
