| 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 |
|
|
| |
|
|
|
|
| """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") |
|
|
| |
| 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] > 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 |
|
|
