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, spectrogram_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, vol_aug: bool = True): self.audiopaths = load_filepaths_and_text(audiopaths) self.hparams = hparams 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.unit_interpolate_mode = hparams.data.unit_interpolate_mode 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 self.vol_emb = hparams.model.vol_embedding self.vol_aug = hparams.train.vol_aug and vol_aug 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, uv = np.load(filename + ".f0.npy",allow_pickle=True) f0 = torch.FloatTensor(np.array(f0,dtype=float)) uv = torch.FloatTensor(np.array(uv,dtype=float)) c = torch.load(filename+ ".soft.pt") c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[0], mode=self.unit_interpolate_mode) if self.vol_emb: volume_path = filename + ".vol.npy" volume = np.load(volume_path) volume = torch.from_numpy(volume).float() else: volume = None 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] if volume!= None: volume = volume[:lmin] return c, f0, spec, audio_norm, spk, uv, volume def random_slice(self, c, f0, spec, audio_norm, spk, uv, volume): # if spec.shape[1] < 30: # print("skip too short audio:", filename) # return None if random.choice([True, False]) and self.vol_aug and volume!=None: max_amp = float(torch.max(torch.abs(audio_norm))) + 1e-5 max_shift = min(1, np.log10(1/max_amp)) log10_vol_shift = random.uniform(-1, max_shift) audio_norm = audio_norm * (10 ** log10_vol_shift) volume = volume * (10 ** log10_vol_shift) spec = spectrogram_torch(audio_norm, self.hparams.data.filter_length, self.hparams.data.sampling_rate, self.hparams.data.hop_length, self.hparams.data.win_length, center=False)[0] 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] if volume !=None: volume = volume[start:end] return c, f0, spec, audio_norm, spk, uv,volume 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) volume_padded = torch.FloatTensor(len(batch), max_c_len) c_padded.zero_() spec_padded.zero_() f0_padded.zero_() wav_padded.zero_() uv_padded.zero_() volume_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 volume = row[6] if volume != None: volume_padded[i, :volume.size(0)] = volume else : volume_padded = None return c_padded, f0_padded, spec_padded, wav_padded, spkids, lengths, uv_padded, volume_padded