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audio_dataset.py
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import numpy as np
import os
import torch
from torch.utils.data import Dataset, DataLoader
from utils.preprocess import Preprocess, FeatureTypes
import math
from multiprocessing import Pool
from sortedcontainers import SortedList
class AudioDataset(Dataset):
def __init__(self, config, root_dir='/data/music/chord_recognition', dataset_names=('isophonic',),
featuretype=FeatureTypes.cqt, num_workers=20, train=False, preprocessing=False, resize=None, kfold=4):
super(AudioDataset, self).__init__()
self.config = config
self.root_dir = root_dir
self.dataset_names = dataset_names
self.preprocessor = Preprocess(config, featuretype, dataset_names, self.root_dir)
self.resize = resize
self.train = train
self.ratio = config.experiment['data_ratio']
# preprocessing hyperparameters
# song_hz, n_bins, bins_per_octave, hop_length
mp3_config = config.mp3
feature_config = config.feature
self.mp3_string = "%d_%.1f_%.1f" % \
(mp3_config['song_hz'], mp3_config['inst_len'],
mp3_config['skip_interval'])
self.feature_string = "%s_%d_%d_%d" % \
(featuretype.value, feature_config['n_bins'], feature_config['bins_per_octave'], feature_config['hop_length'])
if feature_config['large_voca'] == True:
# store paths if exists
is_preprocessed = True if os.path.exists(os.path.join(root_dir, 'result', dataset_names[0]+'_voca', self.mp3_string, self.feature_string)) else False
if (not is_preprocessed) | preprocessing:
midi_paths = self.preprocessor.get_all_files()
if num_workers > 1:
num_path_per_process = math.ceil(len(midi_paths) / num_workers)
args = [midi_paths[i * num_path_per_process:(i + 1) * num_path_per_process] for i in range(num_workers)]
# start process
p = Pool(processes=num_workers)
p.map(self.preprocessor.generate_labels_features_voca, args)
p.close()
else:
self.preprocessor.generate_labels_features_voca(midi_paths)
# kfold is 5 fold index ( 0, 1, 2, 3, 4 )
self.song_names, self.paths = self.get_paths_voca(kfold=kfold)
else:
# store paths if exists
is_preprocessed = True if os.path.exists(os.path.join(root_dir, 'result', dataset_names[0], self.mp3_string, self.feature_string)) else False
if (not is_preprocessed) | preprocessing:
midi_paths = self.preprocessor.get_all_files()
if num_workers > 1:
num_path_per_process = math.ceil(len(midi_paths) / num_workers)
args = [midi_paths[i * num_path_per_process:(i + 1) * num_path_per_process]
for i in range(num_workers)]
# start process
p = Pool(processes=num_workers)
p.map(self.preprocessor.generate_labels_features_new, args)
p.close()
else:
self.preprocessor.generate_labels_features_new(midi_paths)
# kfold is 5 fold index ( 0, 1, 2, 3, 4 )
self.song_names, self.paths = self.get_paths(kfold=kfold)
def __len__(self):
return len(self.paths)
def __getitem__(self, idx):
instance_path = self.paths[idx]
res = dict()
data = torch.load(instance_path)
res['feature'] = np.log(np.abs(data['feature']) + 1e-6)
res['chord'] = data['chord']
return res
def get_paths(self, kfold=4):
temp = {}
used_song_names = list()
for name in self.dataset_names:
dataset_path = os.path.join(self.root_dir, "result", name, self.mp3_string, self.feature_string)
song_names = os.listdir(dataset_path)
for song_name in song_names:
paths = []
instance_names = os.listdir(os.path.join(dataset_path, song_name))
if len(instance_names) > 0:
used_song_names.append(song_name)
for instance_name in instance_names:
paths.append(os.path.join(dataset_path, song_name, instance_name))
temp[song_name] = paths
# throw away unused song names
song_names = used_song_names
song_names = SortedList(song_names)
print('Total used song length : %d' %len(song_names))
tmp = []
for i in range(len(song_names)):
tmp += temp[song_names[i]]
print('Total instances (train and valid) : %d' %len(tmp))
# divide train/valid dataset using k fold
result = []
total_fold = 5
quotient = len(song_names) // total_fold
remainder = len(song_names) % total_fold
fold_num = [0]
for i in range(total_fold):
fold_num.append(quotient)
for i in range(remainder):
fold_num[i+1] += 1
for i in range(total_fold):
fold_num[i+1] += fold_num[i]
if self.train:
tmp = []
# get not augmented data
for k in range(total_fold):
if k != kfold:
for i in range(fold_num[k], fold_num[k+1]):
result += temp[song_names[i]]
tmp += song_names[fold_num[k]:fold_num[k + 1]]
song_names = tmp
else:
for i in range(fold_num[kfold], fold_num[kfold+1]):
instances = temp[song_names[i]]
instances = [inst for inst in instances if "1.00_0" in inst]
result += instances
song_names = song_names[fold_num[kfold]:fold_num[kfold+1]]
return song_names, result
def get_paths_voca(self, kfold=4):
temp = {}
used_song_names = list()
for name in self.dataset_names:
dataset_path = os.path.join(self.root_dir, "result", name+'_voca', self.mp3_string, self.feature_string)
song_names = os.listdir(dataset_path)
for song_name in song_names:
paths = []
instance_names = os.listdir(os.path.join(dataset_path, song_name))
if len(instance_names) > 0:
used_song_names.append(song_name)
for instance_name in instance_names:
paths.append(os.path.join(dataset_path, song_name, instance_name))
temp[song_name] = paths
# throw away unused song names
song_names = used_song_names
song_names = SortedList(song_names)
print('Total used song length : %d' %len(song_names))
tmp = []
for i in range(len(song_names)):
tmp += temp[song_names[i]]
print('Total instances (train and valid) : %d' %len(tmp))
# divide train/valid dataset using k fold
result = []
total_fold = 5
quotient = len(song_names) // total_fold
remainder = len(song_names) % total_fold
fold_num = [0]
for i in range(total_fold):
fold_num.append(quotient)
for i in range(remainder):
fold_num[i+1] += 1
for i in range(total_fold):
fold_num[i+1] += fold_num[i]
if self.train:
tmp = []
# get not augmented data
for k in range(total_fold):
if k != kfold:
for i in range(fold_num[k], fold_num[k+1]):
result += temp[song_names[i]]
tmp += song_names[fold_num[k]:fold_num[k + 1]]
song_names = tmp
else:
for i in range(fold_num[kfold], fold_num[kfold+1]):
instances = temp[song_names[i]]
instances = [inst for inst in instances if "1.00_0" in inst]
result += instances
song_names = song_names[fold_num[kfold]:fold_num[kfold+1]]
return song_names, result
def _collate_fn(batch):
batch_size = len(batch)
max_len = batch[0]['feature'].shape[1]
input_percentages = torch.empty(batch_size) # for variable length
chord_lens = torch.empty(batch_size, dtype=torch.int64)
chords = []
collapsed_chords = []
features = []
boundaries = []
for i in range(batch_size):
sample = batch[i]
feature = sample['feature']
chord = sample['chord']
diff = np.diff(chord, axis=0).astype(np.bool)
idx = np.insert(diff, 0, True, axis=0)
chord_lens[i] = np.sum(idx).item(0)
chords.extend(chord)
features.append(feature)
input_percentages[i] = feature.shape[1] / max_len
collapsed_chords.extend(np.array(chord)[idx].tolist())
boundary = np.append([0], diff)
boundaries.extend(boundary.tolist())
features = torch.tensor(features, dtype=torch.float32).unsqueeze(1) # batch_size*1*feature_size*max_len
chords = torch.tensor(chords, dtype=torch.int64) # (batch_size*time_length)
collapsed_chords = torch.tensor(collapsed_chords, dtype=torch.int64) # total_unique_chord_len
boundaries = torch.tensor(boundaries, dtype=torch.uint8) # (batch_size*time_length)
return features, input_percentages, chords, collapsed_chords, chord_lens, boundaries
class AudioDataLoader(DataLoader):
def __init__(self, *args, **kwargs):
super(AudioDataLoader, self).__init__(*args, **kwargs)
self.collate_fn = _collate_fn