File size: 2,734 Bytes
ba733e3 | 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 | import os
import json
from tqdm import tqdm
from sklearn.model_selection import train_test_split
import random
def fix_seed(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
fix_seed(42)
train_data_num = 100
# file_path = './figstep_audio'
safe_file_path = './figstep_audio_safe'
safe_text_file = os.path.join(safe_file_path, 'audio_files.txt')
harm_file_path = './figstep_audio'
harm_text_file = os.path.join(harm_file_path, 'audio_files.txt')
org_data_harm = []
with open(harm_text_file, "r") as f:
for line in f:
audio_file, question = line.strip().split(" ", 1)
name_split = audio_file.split("_")
scenario = "_".join(name_split[:-1])
org_data_harm.append({
"scenario": scenario,
"audio_file": audio_file,
"question": question.replace("-", " "),
})
org_data_safe = []
with open(safe_text_file, "r") as f:
for line in f:
audio_file, question = line.strip().split(" ", 1)
name_split = audio_file.split("_")
scenario = "_".join(name_split[:-1])
org_data_safe.append({
"scenario": scenario,
"audio_file": audio_file,
"question": question.replace("-", " "),
})
data_num_harm = len(org_data_harm)
print(f"Total harm data num: {data_num_harm}")
train_data_harm, test_data_harm = train_test_split(org_data_harm, train_size=train_data_num, random_state=42)
data_num_safe = len(org_data_safe)
print(f"Total safe data num: {data_num_safe}")
index_list = list(range(data_num_harm))
random.shuffle(index_list)
train_index_harm, test_index_harm = train_test_split(index_list, train_size=train_data_num, random_state=42)
# train_index_safe, test_index_safe = train_test_split(index_list, train_size=train_data_num, random_state=42)
train_data_harm = []
test_data_harm = []
train_data_safe = []
test_data_safe = []
for i in train_index_harm:
train_data_harm.append(org_data_harm[i])
train_data_safe.append(org_data_safe[i])
for i in test_index_harm:
test_data_harm.append(org_data_harm[i])
test_data_safe.append(org_data_safe[i])
test_data_num = len(test_index_harm)
def save_json(file_path, train_data, test_data):
out_file_train = os.path.join(file_path, f"train_{train_data_num}.json")
out_file_test = os.path.join(file_path, f"test_{test_data_num}.json")
with open(out_file_test, 'w', encoding='utf-8') as f:
json.dump(test_data, f, ensure_ascii=False, indent=4)
with open(out_file_train, 'w', encoding='utf-8') as f:
json.dump(train_data, f, ensure_ascii=False, indent=4)
save_json(harm_file_path, train_data_harm, test_data_harm)
save_json(safe_file_path, train_data_safe, test_data_safe)
pass |