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
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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,
verbose=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
self.verbose = verbose
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))]
@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):
item = self.load_item(idx)
return item
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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
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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)
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@staticmethod
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