153 lines
5.0 KiB
Python
153 lines
5.0 KiB
Python
import glob
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import os
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import random
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from multiprocessing import Manager
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import numpy as np
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import torch
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from torch.utils.data import Dataset
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class GANDataset(Dataset):
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"""
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GAN 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__(
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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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return_pairs=False,
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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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):
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super().__init__()
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self.ap = ap
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self.item_list = items
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self.compute_feat = not isinstance(items[0], (tuple, list))
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self.seq_len = seq_len
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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.return_pairs = return_pairs
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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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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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# map G and D instances
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self.G_to_D_mappings = list(range(len(self.item_list)))
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self.shuffle_mapping()
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# cache acoustic features
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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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"""Return different items for Generator and Discriminator and
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cache acoustic features"""
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# set the seed differently for each worker
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if torch.utils.data.get_worker_info():
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random.seed(torch.utils.data.get_worker_info().seed)
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if self.return_segments:
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item1 = self.load_item(idx)
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if self.return_pairs:
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idx2 = self.G_to_D_mappings[idx]
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item2 = self.load_item(idx2)
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return item1, item2
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return item1
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item1 = self.load_item(idx)
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return item1
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def _pad_short_samples(self, audio, mel=None):
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"""Pad samples shorter than the output sequence length"""
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if len(audio) < self.seq_len:
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audio = np.pad(audio, (0, self.seq_len - len(audio)), mode="constant", constant_values=0.0)
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if mel is not None and mel.shape[1] < self.feat_frame_len:
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pad_value = self.ap.melspectrogram(np.zeros([self.ap.win_length]))[:, 0]
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mel = np.pad(
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mel,
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([0, 0], [0, self.feat_frame_len - mel.shape[1]]),
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mode="constant",
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constant_values=pad_value.mean(),
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)
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return audio, mel
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def shuffle_mapping(self):
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random.shuffle(self.G_to_D_mappings)
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def load_item(self, idx):
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"""load (audio, feat) couple"""
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if self.compute_feat:
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# compute features from wav
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wavpath = self.item_list[idx]
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# print(wavpath)
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if self.use_cache and self.cache[idx] is not None:
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audio, mel = self.cache[idx]
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else:
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audio = self.ap.load_wav(wavpath)
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mel = self.ap.melspectrogram(audio)
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audio, mel = self._pad_short_samples(audio, mel)
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else:
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# load precomputed features
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wavpath, feat_path = self.item_list[idx]
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if self.use_cache and self.cache[idx] is not None:
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audio, mel = self.cache[idx]
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else:
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audio = self.ap.load_wav(wavpath)
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mel = np.load(feat_path)
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audio, mel = self._pad_short_samples(audio, mel)
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# correct the audio length wrt padding applied in stft
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audio = np.pad(audio, (0, self.hop_len), mode="edge")
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audio = audio[: mel.shape[-1] * self.hop_len]
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assert (
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mel.shape[-1] * self.hop_len == audio.shape[-1]
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), f" [!] {mel.shape[-1] * self.hop_len} vs {audio.shape[-1]}"
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audio = torch.from_numpy(audio).float().unsqueeze(0)
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mel = torch.from_numpy(mel).float().squeeze(0)
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if self.return_segments:
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max_mel_start = mel.shape[1] - self.feat_frame_len
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mel_start = random.randint(0, max_mel_start)
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mel_end = mel_start + self.feat_frame_len
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mel = mel[:, mel_start:mel_end]
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audio_start = mel_start * self.hop_len
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audio = audio[:, audio_start : audio_start + self.seq_len]
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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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return (mel, audio)
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