Fix summarization issues and improve GPU handling. Update .gitignore for venv

utils/transcription.py

@@ -25,7 +25,6 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 WHISPER_MODEL = "base"
-SUMMARIZATION_MODEL = "t5-base"
 
 def transcribe_audio(audio_path: Path, model=WHISPER_MODEL, use_cache=True, cache_max_age=None, 
                      use_gpu=True, memory_fraction=0.8):
@@ -83,107 +82,4 @@ def transcribe_audio(audio_path: Path, model=WHISPER_MODEL, use_cache=True, cach
         }
         save_to_cache(audio_path, cache_data, model, "transcribe")
     
-    return segments, transcript
-
-
-def summarize_text(text, model=SUMMARIZATION_MODEL, use_gpu=True, memory_fraction=0.8):
-    """
-    Summarize text using a pre-trained transformer model with chunking.
-    
-    Args:
-        text (str): Text to summarize
-        model (str): Model to use for summarization
-        use_gpu (bool): Whether to use GPU acceleration if available
-        memory_fraction (float): Fraction of GPU memory to use (0.0 to 1.0)
-        
-    Returns:
-        str: Summarized text
-    """
-    # Configure device
-    device = torch.device("cpu")
-    if use_gpu and GPU_UTILS_AVAILABLE:
-        device = get_optimal_device()
-        logger.info(f"Using device: {device} for summarization")
-    
-    # Initialize the pipeline with the specified device
-    device_arg = -1 if device.type == "cpu" else 0  # -1 for CPU, 0 for GPU
-    summarization_pipeline = pipeline("summarization", model=model, device=device_arg)
-    tokenizer = AutoTokenizer.from_pretrained(model)
-    
-    max_tokens = 512
-    
-    tokens = tokenizer(text, return_tensors='pt')
-    num_tokens = len(tokens['input_ids'][0])
-    
-    if num_tokens > max_tokens:
-        chunks = chunk_text(text, max_tokens, tokenizer)
-        summaries = []
-        
-        for i, chunk in enumerate(chunks):
-            logger.info(f"Summarizing chunk {i+1}/{len(chunks)}")
-            summary_output = summarization_pipeline(
-                "summarize: " + chunk, 
-                max_length=150, 
-                min_length=30, 
-                do_sample=False
-            )
-            summaries.append(summary_output[0]['summary_text'])
-        
-        overall_summary = " ".join(summaries)
-        
-        # If the combined summary is still long, summarize it again
-        if len(summaries) > 1:
-            logger.info("Generating final summary from chunk summaries")
-            combined_text = " ".join(summaries)
-            overall_summary = summarization_pipeline(
-                "summarize: " + combined_text, 
-                max_length=150, 
-                min_length=30, 
-                do_sample=False
-            )[0]['summary_text']
-    else:
-        overall_summary = summarization_pipeline(
-            "summarize: " + text, 
-            max_length=150, 
-            min_length=30, 
-            do_sample=False
-        )[0]['summary_text']
-    
-    return overall_summary
-
-
-def chunk_text(text, max_tokens, tokenizer=None):
-    """
-    Splits the text into a list of chunks based on token limits.
-    
-    Args:
-        text (str): Text to chunk
-        max_tokens (int): Maximum tokens per chunk
-        tokenizer (AutoTokenizer, optional): Tokenizer to use
-        
-    Returns:
-        list: List of text chunks
-    """
-    if tokenizer is None:
-        tokenizer = AutoTokenizer.from_pretrained(SUMMARIZATION_MODEL)
-    
-    words = text.split()
-    
-    chunks = []
-    current_chunk = []
-    current_length = 0
-    
-    for word in words:
-        hypothetical_length = current_length + len(tokenizer(word, return_tensors='pt')['input_ids'][0]) - 2
-        if hypothetical_length <= max_tokens:
-            current_chunk.append(word)
-            current_length = hypothetical_length
-        else:
-            chunks.append(' '.join(current_chunk))
-            current_chunk = [word]
-            current_length = len(tokenizer(word, return_tensors='pt')['input_ids'][0]) - 2
-    
-    if current_chunk:
-        chunks.append(' '.join(current_chunk))
-    
-    return chunks
+    return segments, transcript