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