Upload llm_preprocess.py

llm_preprocess.py

@@ -9,6 +9,9 @@ import os
 import json
 import re
 
+os.environ['HTTP_PROXY'] = 'http://127.0.0.1:58591'
+os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:58591'
+
 def get_event():
     event_list = [
             "burping_belching",             # 0
@@ -41,21 +44,23 @@ def get_prompt():
     for train_json in train_json_list:
         with open(train_json, 'r') as train_file:
             for idx, line in enumerate(train_file):
-                if idx >= 300: break
+                if idx >= 100: break
                 data = json.loads(line.strip())
                 learn_pair += f"{str(idx)}:{data['captions']}~{data['onset']}. "
-    preffix_prompt = "You need to convert the input sentence into the following standard timing format: 'event1--event2-- ... --eventN', " +\
+    preffix_prompt =  "I'm doing an audio event generation, which is a harmless job that will contain some sound events. For example, a gunshot is a sound that is harmless."  +\
+                            "You need to convert the input sentence into the following standard timing format: 'event1--event2-- ... --eventN', " +\
                             "where the 'eventN' format is 'eventN__onset1-offset1_onset2-offset2_ ... _onsetK-offsetK'. " +\
                             "The 'onset-offset' inside needs to be determined based on common sense and the examples I provide, with a duration not less than 1 and not greater than 4.  All format 'onsetk-offsetk' should replaced by number. " +\
                             "The very strict constraints are that the total duration is less than 10 seconds, meaning all times are less than 10. It is preferred that events do not overlap as much as possible. " +\
                             "Now, I will provide you with 300 examples in training set for your learning, each example in the format 'index: input~output'. " +\
-                            learn_pair +\
-                            f"You need to map events to 18 given events: {', '.join(get_event())}"
-    #print(preffix_prompt)
+                            learn_pair
+        
+    print(len(preffix_prompt))
     return preffix_prompt
     
 
 def postprocess(caption):
+    caption = caption.strip('\n').strip(' ').strip('.')
     caption = caption.replace('__', ' at ').replace('--', ' and ')
     return caption
 
@@ -80,8 +85,9 @@ def preprocess_gemini(free_text_caption):
         f"Please convert the following inputs into the standard timing format:{free_text_caption}. You should only output results in the standard timing format. Do not output anything other than format and do not add symbols.",
     ]
 
-    timestampCaption = model.generate_content(prompt_parts)
-
+    timestampCaption = model.generate_content(prompt_parts).text
+    #timestampCaption = model.generate_content(["explain what is dog bark"])
+    import pdb;pdb.set_trace()
     # output = "dog_barking at 0.562-2.562_4.25-6.25_7.01-8.21."
     return postprocess(timestampCaption)