Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,565 Bytes
a84a65c |
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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
import csv
import os
import pickle
import sys
import numpy as np
import torch
import random
import math
import librosa
import pandas as pd
from pathlib import Path
class audio_spec_join_Dataset(torch.utils.data.Dataset):
# Only Load audio dataset: for training Stage1: Audio Npy Dataset
def __init__(self, split, dataset_name, spec_crop_len, drop=0.0):
super().__init__()
if split == "train":
self.split = "Train"
elif split == "valid" or split == 'test':
self.split = "Test"
# Default params:
self.min_duration = 2
self.spec_crop_len = spec_crop_len
self.drop = drop
print("Use Drop: {}".format(self.drop))
self.init_text2audio(dataset_name)
print('Split: {} Total Sample Num: {}'.format(split, len(self.dataset)))
if os.path.exists('/apdcephfs_intern/share_1316500/nlphuang/data/video_to_audio/vggsound/cavp/empty_vid.npz'):
self.root = '/apdcephfs_intern'
else:
self.root = '/apdcephfs'
def init_text2audio(self, dataset):
with open(dataset) as f:
reader = csv.DictReader(
f,
delimiter="\t",
quotechar=None,
doublequote=False,
lineterminator="\n",
quoting=csv.QUOTE_NONE,
)
samples = [dict(e) for e in reader]
if self.split == 'Test':
samples = samples[:100]
self.dataset = samples
print('text2audio dataset len:', len(self.dataset))
def __len__(self):
return len(self.dataset)
def load_feat(self, spec_path):
try:
spec_raw = np.load(spec_path) # mel spec [80, T]
except:
print(f'corrupted mel:{spec_path}', flush=True)
spec_raw = np.zeros((80, self.spec_crop_len), dtype=np.float32) # [C, T]
spec_len = self.spec_crop_len
if spec_raw.shape[1] < spec_len:
spec_raw = np.tile(spec_raw, math.ceil(spec_len / spec_raw.shape[1]))
spec_raw = spec_raw[:, :int(spec_len)]
return spec_raw
def __getitem__(self, idx):
data_dict = {}
data = self.dataset[idx]
p = np.random.uniform(0, 1)
if p > self.drop:
caption = {"ori_caption": data['ori_cap'], "struct_caption": data['caption']}
else:
caption = {"ori_caption": "", "struct_caption": ""}
mel_path = data['mel_path'].replace('/apdcephfs', '/apdcephfs_intern') if self.root == '/apdcephfs_intern' else data['mel_path']
spec = self.load_feat(mel_path)
data_dict['caption'] = caption
data_dict['image'] = spec # (80, 624)
return data_dict
class spec_join_Dataset_Train(audio_spec_join_Dataset):
def __init__(self, dataset_cfg):
super().__init__(split='train', **dataset_cfg)
class spec_join_Dataset_Valid(audio_spec_join_Dataset):
def __init__(self, dataset_cfg):
super().__init__(split='valid', **dataset_cfg)
class spec_join_Dataset_Test(audio_spec_join_Dataset):
def __init__(self, dataset_cfg):
super().__init__(split='test', **dataset_cfg)
class audio_spec_join_audioset_Dataset(audio_spec_join_Dataset):
# def __init__(self, split, dataset_name, root, spec_crop_len, drop=0.0):
# super().__init__(split, dataset_name, spec_crop_len, drop)
#
# self.data_dir = root
# MANIFEST_COLUMNS = ["name", "dataset", "ori_cap", "audio_path", "mel_path", "duration"]
# manifest = {c: [] for c in MANIFEST_COLUMNS}
# skip = 0
# if self.split != 'Train': return
# from preprocess.generate_manifest import save_df_to_tsv
# from tqdm import tqdm
# for idx in tqdm(range(len(self.dataset))):
# item = self.dataset[idx]
# mel_path = f'{self.data_dir}/{Path(item["name"])}_mel.npy'
# try:
# _ = np.load(mel_path)
# except:
# skip += 1
# continue
#
# manifest["name"].append(item['name'])
# manifest["dataset"].append("audioset")
# manifest["ori_cap"].append(item['ori_cap'])
# manifest["duration"].append(item['audio_path'])
# manifest["audio_path"].append(item['duration'])
# manifest["mel_path"].append(mel_path)
#
# print(f"Writing manifest to {dataset_name.replace('audioset.tsv', 'audioset_new.tsv')}..., skip: {skip}")
# save_df_to_tsv(pd.DataFrame.from_dict(manifest), f"{dataset_name.replace('audioset.tsv', 'audioset_new.tsv')}")
def __getitem__(self, idx):
data_dict = {}
data = self.dataset[idx]
p = np.random.uniform(0, 1)
if p > self.drop:
caption = data['ori_cap']
else:
caption = ""
spec = self.load_feat(data['mel_path'])
data_dict['caption'] = caption
data_dict['image'] = spec # (80, 624)
return data_dict
class spec_join_Dataset_audioset_Train(audio_spec_join_audioset_Dataset):
def __init__(self, dataset_cfg):
super().__init__(split='train', **dataset_cfg)
class spec_join_Dataset_audioset_Valid(audio_spec_join_audioset_Dataset):
def __init__(self, dataset_cfg):
super().__init__(split='valid', **dataset_cfg)
class spec_join_Dataset_audioset_Test(audio_spec_join_audioset_Dataset):
def __init__(self, dataset_cfg):
super().__init__(split='test', **dataset_cfg)
|