import glob
import os
import random
from multiprocessing import Manager
import numpy as np
import torch
from torch.utils.data import Dataset
[docs]
class GANDataset(Dataset):
"""
GAN 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,
return_pairs=False,
is_training=True,
return_segments=True,
use_noise_augment=False,
use_cache=False,
verbose=False,
):
super().__init__()
self.ap = ap
self.item_list = items
self.compute_feat = not isinstance(items[0], (tuple, list))
self.seq_len = seq_len
self.hop_len = hop_len
self.pad_short = pad_short
self.conv_pad = conv_pad
self.return_pairs = return_pairs
self.is_training = is_training
self.return_segments = return_segments
self.use_cache = use_cache
self.use_noise_augment = use_noise_augment
self.verbose = verbose
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)
# map G and D instances
self.G_to_D_mappings = list(range(len(self.item_list)))
self.shuffle_mapping()
# 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))]
@staticmethod
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):
"""Return different items for Generator and Discriminator and
cache acoustic features"""
# set the seed differently for each worker
if torch.utils.data.get_worker_info():
random.seed(torch.utils.data.get_worker_info().seed)
if self.return_segments:
item1 = self.load_item(idx)
if self.return_pairs:
idx2 = self.G_to_D_mappings[idx]
item2 = self.load_item(idx2)
return item1, item2
return item1
item1 = self.load_item(idx)
return item1
def _pad_short_samples(self, audio, mel=None):
"""Pad samples shorter than the output sequence length"""
if len(audio) < self.seq_len:
audio = np.pad(audio, (0, self.seq_len - len(audio)), mode="constant", constant_values=0.0)
if mel is not None and mel.shape[1] < self.feat_frame_len:
pad_value = self.ap.melspectrogram(np.zeros([self.ap.win_length]))[:, 0]
mel = np.pad(
mel,
([0, 0], [0, self.feat_frame_len - mel.shape[1]]),
mode="constant",
constant_values=pad_value.mean(),
)
return audio, mel
def shuffle_mapping(self):
random.shuffle(self.G_to_D_mappings)
[docs]
def load_item(self, idx):
"""load (audio, feat) couple"""
if self.compute_feat:
# compute features from wav
wavpath = self.item_list[idx]
# print(wavpath)
if self.use_cache and self.cache[idx] is not None:
audio, mel = self.cache[idx]
else:
audio = self.ap.load_wav(wavpath)
mel = self.ap.melspectrogram(audio)
audio, mel = self._pad_short_samples(audio, mel)
else:
# load precomputed features
wavpath, feat_path = self.item_list[idx]
if self.use_cache and self.cache[idx] is not None:
audio, mel = self.cache[idx]
else:
audio = self.ap.load_wav(wavpath)
mel = np.load(feat_path)
audio, mel = self._pad_short_samples(audio, mel)
# correct the audio length wrt padding applied in stft
audio = np.pad(audio, (0, self.hop_len), mode="edge")
audio = audio[: mel.shape[-1] * self.hop_len]
assert (
mel.shape[-1] * self.hop_len == audio.shape[-1]
), f" [!] {mel.shape[-1] * self.hop_len} vs {audio.shape[-1]}"
audio = torch.from_numpy(audio).float().unsqueeze(0)
mel = torch.from_numpy(mel).float().squeeze(0)
if self.return_segments:
max_mel_start = mel.shape[1] - self.feat_frame_len
mel_start = random.randint(0, max_mel_start)
mel_end = mel_start + self.feat_frame_len
mel = mel[:, mel_start:mel_end]
audio_start = mel_start * self.hop_len
audio = audio[:, audio_start : audio_start + self.seq_len]
if self.use_noise_augment and self.is_training and self.return_segments:
audio = audio + (1 / 32768) * torch.randn_like(audio)
return (mel, audio)