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| import glob | |
| import os | |
| import random | |
| from multiprocessing import Manager | |
| from typing import List, Tuple | |
| import numpy as np | |
| import torch | |
| from torch.utils.data import Dataset | |
| class WaveGradDataset(Dataset): | |
| """ | |
| WaveGrad Dataset searchs for all the wav files under root path | |
| and converts them to acoustic features on the fly and returns | |
| random segments of (audio, feature) couples. | |
| """ | |
| def __init__( | |
| self, | |
| ap, | |
| items, | |
| seq_len, | |
| hop_len, | |
| pad_short, | |
| conv_pad=2, | |
| is_training=True, | |
| return_segments=True, | |
| use_noise_augment=False, | |
| use_cache=False, | |
| ): | |
| super().__init__() | |
| self.ap = ap | |
| self.item_list = items | |
| self.seq_len = seq_len if return_segments else None | |
| self.hop_len = hop_len | |
| self.pad_short = pad_short | |
| self.conv_pad = conv_pad | |
| self.is_training = is_training | |
| self.return_segments = return_segments | |
| self.use_cache = use_cache | |
| self.use_noise_augment = use_noise_augment | |
| if return_segments: | |
| assert seq_len % hop_len == 0, " [!] seq_len has to be a multiple of hop_len." | |
| self.feat_frame_len = seq_len // hop_len + (2 * conv_pad) | |
| # cache acoustic features | |
| if use_cache: | |
| self.create_feature_cache() | |
| def create_feature_cache(self): | |
| self.manager = Manager() | |
| self.cache = self.manager.list() | |
| self.cache += [None for _ in range(len(self.item_list))] | |
| def find_wav_files(path): | |
| return glob.glob(os.path.join(path, "**", "*.wav"), recursive=True) | |
| def __len__(self): | |
| return len(self.item_list) | |
| def __getitem__(self, idx): | |
| item = self.load_item(idx) | |
| return item | |
| def load_test_samples(self, num_samples: int) -> List[Tuple]: | |
| """Return test samples. | |
| Args: | |
| num_samples (int): Number of samples to return. | |
| Returns: | |
| List[Tuple]: melspectorgram and audio. | |
| Shapes: | |
| - melspectrogram (Tensor): :math:`[C, T]` | |
| - audio (Tensor): :math:`[T_audio]` | |
| """ | |
| samples = [] | |
| return_segments = self.return_segments | |
| self.return_segments = False | |
| for idx in range(num_samples): | |
| mel, audio = self.load_item(idx) | |
| samples.append([mel, audio]) | |
| self.return_segments = return_segments | |
| return samples | |
| def load_item(self, idx): | |
| """load (audio, feat) couple""" | |
| # compute features from wav | |
| wavpath = self.item_list[idx] | |
| if self.use_cache and self.cache[idx] is not None: | |
| audio = self.cache[idx] | |
| else: | |
| audio = self.ap.load_wav(wavpath) | |
| if self.return_segments: | |
| # correct audio length wrt segment length | |
| if audio.shape[-1] < self.seq_len + self.pad_short: | |
| audio = np.pad( | |
| audio, (0, self.seq_len + self.pad_short - len(audio)), mode="constant", constant_values=0.0 | |
| ) | |
| assert ( | |
| audio.shape[-1] >= self.seq_len + self.pad_short | |
| ), f"{audio.shape[-1]} vs {self.seq_len + self.pad_short}" | |
| # correct the audio length wrt hop length | |
| p = (audio.shape[-1] // self.hop_len + 1) * self.hop_len - audio.shape[-1] | |
| audio = np.pad(audio, (0, p), mode="constant", constant_values=0.0) | |
| if self.use_cache: | |
| self.cache[idx] = audio | |
| if self.return_segments: | |
| max_start = len(audio) - self.seq_len | |
| start = random.randint(0, max_start) | |
| end = start + self.seq_len | |
| audio = audio[start:end] | |
| if self.use_noise_augment and self.is_training and self.return_segments: | |
| audio = audio + (1 / 32768) * torch.randn_like(audio) | |
| mel = self.ap.melspectrogram(audio) | |
| mel = mel[..., :-1] # ignore the padding | |
| audio = torch.from_numpy(audio).float() | |
| mel = torch.from_numpy(mel).float().squeeze(0) | |
| return (mel, audio) | |
| def collate_full_clips(batch): | |
| """This is used in tune_wavegrad.py. | |
| It pads sequences to the max length.""" | |
| max_mel_length = max([b[0].shape[1] for b in batch]) if len(batch) > 1 else batch[0][0].shape[1] | |
| max_audio_length = max([b[1].shape[0] for b in batch]) if len(batch) > 1 else batch[0][1].shape[0] | |
| mels = torch.zeros([len(batch), batch[0][0].shape[0], max_mel_length]) | |
| audios = torch.zeros([len(batch), max_audio_length]) | |
| for idx, b in enumerate(batch): | |
| mel = b[0] | |
| audio = b[1] | |
| mels[idx, :, : mel.shape[1]] = mel | |
| audios[idx, : audio.shape[0]] = audio | |
| return mels, audios | |