Update audio_agent.py

audio_agent.py

@@ -17,92 +17,99 @@ class AudioAgent(BaseAgent):
     def __call__(self, question: str, file_name: str, **kwargs) -> str:
         print(f"AudioAgent: Transcribing file: {file_name}")
 
-        search_query = self.extract_search_query(question)
-
         try:
-            # Set the model ID based on size
-            model_id = "openai/whisper-tiny"
-            
-            # Load model with optimizations for CPU
-            model = AutoModelForSpeechSeq2Seq.from_pretrained(
-                model_id,
-                torch_dtype=torch.float32,  # Use float32 for CPU
-                low_cpu_mem_usage=True,
-                use_safetensors=True
-            )
-            
-            # Load processor
-            processor = AutoProcessor.from_pretrained(model_id)
-            
-            # Create pipeline with small batch size for CPU
-            pipe = pipeline(
-                "automatic-speech-recognition",
-                model=model,
-                tokenizer=processor.tokenizer,
-                feature_extractor=processor.feature_extractor,
-                max_new_tokens=128,
-                chunk_length_s=30,
-                batch_size=1,  # Small batch size for CPU
-                return_timestamps=True,
-                device="cpu",
-            )
-            
-            print("Model loaded successfully.")
-
-            print(f"Starting transcription of {file_name}")
+            if file_name:
 
-            try:
-                print("Running transcription (this may take some time on CPU)...")
-                
-                # Set task and language options
-                generate_kwargs = {}
-                generate_kwargs["language"] = "auto"
+                search_query = self.extract_search_query(question)
+        
+                try:
+                    # Set the model ID based on size
+                    model_id = "openai/whisper-tiny"
                     
-                # Run the pipeline directly on the file
-                result = pipe(
-                    file_name,
-                    generate_kwargs=generate_kwargs,
-                    return_timestamps=True
-                )
-                
-                # Process results based on output format
-
-                transcript = result["text"]
-
-                system_prompt = """
-                You are a research expert tasked with answering factual questions based on a text transcript extracted from an audio file.
-                Analyze the transcript carefully and extract the information needed to answer the question accurately.
-                Provide a concise, accurate answer based only on the provided information.
-                You must provide only the exact answer requested by the user, in the correct type and minimal format, based purely on the provided information.
-                YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
-                If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. 
-                If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. 
-                If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
-                If you cannot find the answer based on the provided information, reply only with "Unknown".
-                """
-                
-                user_prompt = f"""
-                Question: {question}
-                
-                Transcript:
-                {transcript}
+                    # Load model with optimizations for CPU
+                    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+                        model_id,
+                        torch_dtype=torch.float32,  # Use float32 for CPU
+                        low_cpu_mem_usage=True,
+                        use_safetensors=True
+                    )
+                    
+                    # Load processor
+                    processor = AutoProcessor.from_pretrained(model_id)
+                    
+                    # Create pipeline with small batch size for CPU
+                    pipe = pipeline(
+                        "automatic-speech-recognition",
+                        model=model,
+                        tokenizer=processor.tokenizer,
+                        feature_extractor=processor.feature_extractor,
+                        max_new_tokens=128,
+                        chunk_length_s=30,
+                        batch_size=1,  # Small batch size for CPU
+                        return_timestamps=True,
+                        device="cpu",
+                    )
+                    
+                    print("Model loaded successfully.")
+        
+                    print(f"Starting transcription of {file_name}")
+        
+                    try:
+                        print("Running transcription (this may take some time on CPU)...")
+                        
+                        # Set task and language options
+                        generate_kwargs = {}
+                        generate_kwargs["language"] = "auto"
+                            
+                        # Run the pipeline directly on the file
+                        result = pipe(
+                            file_name,
+                            generate_kwargs=generate_kwargs,
+                            return_timestamps=True
+                        )
+                        
+                        # Process results based on output format
+        
+                        transcript = result["text"]
+        
+                        system_prompt = """
+                        You are a research expert tasked with answering factual questions based on a text transcript extracted from an audio file.
+                        Analyze the transcript carefully and extract the information needed to answer the question accurately.
+                        Provide a concise, accurate answer based only on the provided information.
+                        You must provide only the exact answer requested by the user, in the correct type and minimal format, based purely on the provided information.
+                        YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
+                        If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. 
+                        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. 
+                        If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+                        If you cannot find the answer based on the provided information, reply only with "Unknown".
+                        """
+                        
+                        user_prompt = f"""
+                        Question: {question}
+                        
+                        Transcript:
+                        {transcript}
+                        
+                        Based on these search results, provide a concise and accurate answer to the question.
+                        """
+        
+                        final_answer = self.llm_call(system_prompt, user_prompt)
+        
+                        print(f"Final answer: {final_answer}")
                 
-                Based on these search results, provide a concise and accurate answer to the question.
-                """
-
-                final_answer = self.llm_call(system_prompt, user_prompt)
-
-                print(f"Final answer: {final_answer}")
+                        if final_answer.lower() == "unknown":
+                            final_answer = self.ask_james(question)                
         
-                if final_answer.lower() == "unknown":
-                    final_answer = self.ask_james(question)                
+                    except Exception as e:
+                        print(f"Transcription failed: {str(e)}")
+                        raise
+                    
+                except Exception as e:
+                    print(f"Error loading model: {str(e)}")
+                    raise
 
-            except Exception as e:
-                print(f"Transcription failed: {str(e)}")
-                raise
-            
         except Exception as e:
-            print(f"Error loading model: {str(e)}")
+            print(f"Transcription failed: {str(e)}")
             raise
 
         return final_answer