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import pandas as pd
import requests
import argparse
import os
from openai import OpenAI
openai_key = 'sk-cEOsqGI9gAOrxgqQ6D1B9U3nvZrpzsy98m03RDfrNf9XOYwP'
# def get_struct(caption):
# headers = {
# 'Content-Type': 'application/json',
# 'Authorization': f'{openai_key}',
# }
# json_data = {
# 'model': 'gpt-3.5-turbo',
# 'messages': [
# {
# 'role': 'user',
# 'content':f'I want to know what sound might be in the given scene and you need to give me the results in the following format:\
# Question: A bird sings on the river in the morning, a cow passes by and scares away the bird.\
# Answer: <running water& all>@<birds chriping& start>@<cow footsteps& mid>@<birds flying away& end>.\
# Question: cellphone ringing a variety of tones followed by a loud explosion and fire crackling as a truck engine runs idle\
# Answer: <variety cellphone ringing tones& start>@<loud explosion& end>@<fire crackling& end>@<truck engine idle& end>\
# Question: Train passing followed by short honks three times \
# Answer: <train passing& all>@<short honks three times& end>\
# All indicates the sound exists in the whole scene \
# Start, mid, end indicates the time period the sound appear.\
# Question: {caption} \
# Answer:',
# },
# ],
# 'temperature': 0.0,
# }
# response = requests.post('https://api.openai.com/v1/chat/completions', headers=headers, json=json_data)
# return eval(response.content)['choices'][0]["message"]["content"]
def get_struct(caption):
client = OpenAI(
base_url='https://api.openai-proxy.org/v1',
api_key='sk-cEOsqGI9gAOrxgqQ6D1B9U3nvZrpzsy98m03RDfrNf9XOYwP',
)
chat_completion = client.chat.completions.create(
messages=[
{
'role': 'user',
'content':f'I want to know what sound might be in the given scene and you need to give me the results in the following format:\
Question: A bird sings on the river in the morning, a cow passes by and scares away the bird.\
Answer: <running water& all>@<birds chriping& start>@<cow footsteps& mid>@<birds flying away& end>.\
Question: cellphone ringing a variety of tones followed by a loud explosion and fire crackling as a truck engine runs idle\
Answer: <variety cellphone ringing tones& start>@<loud explosion& end>@<fire crackling& end>@<truck engine idle& end>\
Question: Train passing followed by short honks three times \
Answer: <train passing& all>@<short honks three times& end>\
All indicates the sound exists in the whole scene \
Start, mid, end indicates the time period the sound appear.\
Question: {caption} \
Answer:',
},
],
model="gpt-3.5-turbo",
)
print(chat_completion)
print(chat_completion.choices[0].message.content)
return chat_completion.choices[0].message.content
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument( "--tsv_path",type=str)
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
tsv_path = args.tsv_path
ori_df = pd.read_csv(tsv_path,sep='\t')
index = 0
end = len(ori_df)
name = os.path.basename(tsv_path)[:-4]
f = open(f'{name}.txt','w')
newcap_list = []
while index < end - 1:
try:
df = ori_df.iloc[index:end]
for t in df.itertuples():
index = int(t[0])
ori_caption = getattr(t,'caption')
strcut_cap = get_struct(ori_caption)
if 'sorry' in strcut_cap.lower():
strcut_cap = f'<{ori_caption.lower()}, all>'
newcap_list.append(strcut_cap)
f.write(f'{index}\t{strcut_cap}\n')
f.flush()
except:
print("error")
f.flush()
f.close()
with open(f'{name}.txt') as f:
lines = f.readlines()
id2cap = {}
for line in lines:
# print(line)
index,caption = line.strip().split('\t')
id2cap[int(index)] = caption
df = pd.read_csv(f'{name}.tsv',sep='\t')
df['struct_cap'] = df.index.map(id2cap)
df.to_csv(f'{name}_struct.tsv',sep='\t',index=False)
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