File size: 2,824 Bytes
e857f97 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | import os.path
import torch
from data.build_datasets import DataInfo
from data.process_audio import get_audio_transform, torchaudio_loader
from torchvision import datasets
# -*- coding: utf-8 -*-
# @Time : 6/19/21 12:23 AM
# @Author : Yuan Gong
# @Affiliation : Massachusetts Institute of Technology
# @Email : yuangong@mit.edu
# @File : dataloader.py
# modified from:
# Author: David Harwath
# with some functions borrowed from https://github.com/SeanNaren/deepspeech.pytorch
import csv
import json
import logging
import torchaudio
import numpy as np
import torch
import torch.nn.functional
from torch.utils.data import Dataset
import random
def make_index_dict(label_csv):
index_lookup = {}
with open(label_csv, 'r') as f:
csv_reader = csv.DictReader(f)
line_count = 0
for row in csv_reader:
index_lookup[row['mid']] = row['index']
line_count += 1
return index_lookup
class AudiosetDataset(Dataset):
def __init__(self, args, transform, loader):
self.audio_root = '/apdcephfs_cq3/share_1311970/downstream_datasets/Audio/audioset/eval_segments'
dataset_json_file = '/apdcephfs_cq3/share_1311970/downstream_datasets/Audio/audioset/filter_eval.json'
label_csv = '/apdcephfs_cq3/share_1311970/downstream_datasets/Audio/audioset/class_labels_indices.csv'
with open(dataset_json_file, 'r') as fp:
data_json = json.load(fp)
self.data = data_json['data']
self.index_dict = make_index_dict(label_csv)
self.label_num = len(self.index_dict)
self.args = args
self.transform = transform
self.loader = loader
def __getitem__(self, index):
datum = self.data[index]
label_indices = np.zeros(self.label_num)
for label_str in datum['labels'].split(','):
label_indices[int(self.index_dict[label_str])] = 1.0
label_indices = torch.FloatTensor(label_indices)
audio = self.loader(os.path.join(self.audio_root, datum['wav']))
audio_data = self.transform(audio)
return audio_data, label_indices
def __len__(self):
return len(self.data)
def is_valid_file(path):
return True
def get_audio_dataset(args):
data_path = args.audio_data_path
transform = get_audio_transform(args)
if args.val_a_cls_data.lower() == 'audioset':
dataset = AudiosetDataset(args, transform=transform, loader=torchaudio_loader)
else:
dataset = datasets.ImageFolder(data_path, transform=transform, loader=torchaudio_loader, is_valid_file=is_valid_file)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
num_workers=args.workers,
sampler=None,
)
return DataInfo(dataloader=dataloader, sampler=None)
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