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+{
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "id": "6c33ba3a",
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+ "metadata": {},
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+ "source": [
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+ "# PowerPoint to Narrative-Aware Voiceover Transcript Generator\n",
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+ "\n",
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+ "This notebook demonstrates the complete workflow for converting PowerPoint presentations into AI-generated voiceover transcripts using the unified transcript processor with Llama 4 Maverick through the Llama API.\n",
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+ "\n",
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+ "## Overview\n",
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+ "\n",
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+ "This unified workflow performs the following operations:\n",
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+ "\n",
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+ "1. **Content Extraction**: Pulls speaker notes and visual elements from PowerPoint slides\n",
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+ "2. **Image Conversion**: Transforms slides into high-quality images for AI analysis\n",
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+ "3. **Flexible Processing**: Choose between standard or narrative-aware processing modes\n",
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+ "4. **Transcript Generation**: Creates natural-sounding voiceover content with optional narrative continuity\n",
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+ "5. **Speech Optimization**: Converts numbers, technical terms, and abbreviations to spoken form\n",
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+ "6. **Results Export**: Saves transcripts and context information in multiple formats\n",
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+ "\n",
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+ "## Key Features\n",
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+ "\n",
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+ "- **Unified Processor**: Single class handles both standard and narrative-aware processing\n",
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+ "- **Configurable Context**: Adjustable context window for narrative continuity\n",
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+ "- **Mode Selection**: Toggle between standard and narrative processing with a simple flag\n",
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+ "- **Backward Compatibility**: Maintains compatibility with existing workflows\n",
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+ "\n",
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+ "## Prerequisites\n",
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+ "\n",
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+ "Before running this notebook, ensure you have:\n",
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+ "- Created a `.env` file with your `LLAMA_API_KEY`\n",
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+ "- Updated `config.yaml` with your presentation file path\n",
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+ "---"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "d8965447",
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+ "metadata": {},
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+ "source": [
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+ "## Setup and Configuration\n",
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+ "\n",
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+ "Import required libraries and load environment configuration."
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 29,
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+ "id": "21a962b2",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "SUCCESS: Environment loaded successfully!\n",
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+ "SUCCESS: Llama API key found\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "# Import required libraries\n",
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+ "import pandas as pd\n",
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+ "import os\n",
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+ "from pathlib import Path\n",
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+ "from dotenv import load_dotenv\n",
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+ "import matplotlib.pyplot as plt\n",
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+ "from IPython.display import display\n",
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+ "\n",
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+ "# Load environment variables from .env file\n",
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+ "load_dotenv()\n",
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+ "\n",
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+ "# Verify setup\n",
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+ "if os.getenv('LLAMA_API_KEY'):\n",
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+ " print(\"SUCCESS: Environment loaded successfully!\")\n",
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+ " print(\"SUCCESS: Llama API key found\")\n",
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+ "else:\n",
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+ " print(\"WARNING: LLAMA_API_KEY not found in .env file\")\n",
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+ " print(\"Please check your .env file and add your API key\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 30,
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+ "id": "71c1c8bd",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "SUCCESS: All modules imported successfully!\n",
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+ "- PPTX processor ready\n",
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+ "- Unified transcript generator ready\n",
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+ "- Configuration manager ready\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "# Import custom modules\n",
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+ "try:\n",
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+ " from src.core.pptx_processor import extract_pptx_notes, pptx_to_images_and_notes\n",
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+ " from src.processors.unified_transcript_generator import (\n",
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+ " UnifiedTranscriptProcessor,\n",
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+ " process_slides,\n",
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+ " process_slides_with_narrative\n",
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+ " )\n",
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+ " from src.config.settings import load_config, get_config\n",
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+ "\n",
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+ " print(\"SUCCESS: All modules imported successfully!\")\n",
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+ " print(\"- PPTX processor ready\")\n",
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+ " print(\"- Unified transcript generator ready\")\n",
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+ " print(\"- Configuration manager ready\")\n",
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+ "\n",
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+ "except ImportError as e:\n",
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+ " print(f\"ERROR: Import error: {e}\")\n",
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+ " print(\"Make sure you're running from the project root directory\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 31,
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+ "id": "53781172",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "SUCCESS: Configuration loaded successfully!\n",
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+ "\n",
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+ "Current Settings:\n",
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+ "- Llama Model: Llama-4-Maverick-17B-128E-Instruct-FP8\n",
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+ "- Image DPI: 200\n",
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+ "- Image Format: png\n",
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+ "- Context Window: 5 previous slides (default)\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "# Load and display configuration\n",
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+ "config = load_config()\n",
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+ "print(\"SUCCESS: Configuration loaded successfully!\")\n",
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+ "print(\"\\nCurrent Settings:\")\n",
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+ "print(f\"- Llama Model: {config['api']['llama_model']}\")\n",
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+ "print(f\"- Image DPI: {config['processing']['default_dpi']}\")\n",
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+ "print(f\"- Image Format: {config['processing']['default_format']}\")\n",
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+ "print(f\"- Context Window: 5 previous slides (default)\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "e11ef993-f0bc-4eba-82cb-e8d4b083196e",
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+ "metadata": {},
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+ "source": [
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+ "#### Don't forget to update the config file with your pptx file name!"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 32,
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+ "id": "9386e035",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "File Configuration:\n",
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+ "- Input File: input/All About Llamas.pptx\n",
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+ "- Output Directory: output/\n",
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+ "- SUCCESS: Input file found (10.8 MB)\n",
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+ "- SUCCESS: Output directory ready\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "# Configure file paths from config.yaml\n",
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+ "pptx_file = config['current_project']['pptx_file'] + config['current_project']['extension']\n",
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+ "output_dir = config['current_project']['output_dir']\n",
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+ "\n",
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+ "print(\"File Configuration:\")\n",
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+ "print(f\"- Input File: {pptx_file}\")\n",
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+ "print(f\"- Output Directory: {output_dir}\")\n",
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+ "\n",
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+ "# Verify input file exists\n",
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+ "if Path(pptx_file).exists():\n",
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+ " file_size = Path(pptx_file).stat().st_size / 1024 / 1024\n",
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+ " print(f\"- SUCCESS: Input file found ({file_size:.1f} MB)\")\n",
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+ "else:\n",
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+ " print(f\"- ERROR: Input file not found: {pptx_file}\")\n",
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+ " print(\" Please update the 'pptx_file' path in config.yaml\")\n",
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+ "\n",
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+ "# Create output directory if needed\n",
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+ "Path(output_dir).mkdir(parents=True, exist_ok=True)\n",
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+ "print(f\"- SUCCESS: Output directory ready\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "35a9e13a-4f85-488e-880b-62c7512d1248",
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+ "metadata": {},
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+ "source": [
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+ "## Processing Mode Configuration\n",
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+ "\n",
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+ "Choose your processing mode and configure the unified processor."
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 33,
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+ "id": "6fbfcb28-2f09-4497-8098-35cf3d62ebf3",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Processing Mode Configuration:\n",
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+ "- Mode: NARRATIVE CONTINUITY\n",
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+ "- Context Window: 5 previous slides\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "# Configure processing mode\n",
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+ "\n",
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+ "USE_NARRATIVE = True # Set to False for standard processing, True for narrative continuity\n",
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+ "CONTEXT_WINDOW_SIZE = 5 # Number of previous slides to use as context (only used when USE_NARRATIVE=True)\n",
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+ "\n",
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+ "print(\"Processing Mode Configuration:\")\n",
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+ "if USE_NARRATIVE:\n",
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+ " print(f\"- Mode: NARRATIVE CONTINUITY\")\n",
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+ " print(f\"- Context Window: {CONTEXT_WINDOW_SIZE} previous slides\")\n",
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+ "else:\n",
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+ " print(f\"- Mode: STANDARD PROCESSING\")\n",
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+ " print(f\"- Features: Independent slide processing, faster execution\")\n",
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+ "\n",
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+ "# Initialize the unified processor\n",
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+ "processor = UnifiedTranscriptProcessor(\n",
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+ " use_narrative=USE_NARRATIVE,\n",
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+ " context_window_size=CONTEXT_WINDOW_SIZE\n",
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+ ")"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "ea4851e6",
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+ "metadata": {},
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+ "source": [
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+ "---\n",
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+ "## Processing Pipeline\n",
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+ "\n",
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+ "Execute the main processing pipeline in three key steps."
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "0f098fdf",
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+ "metadata": {},
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+ "source": [
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+ "### Step 1: Extract Content and Convert to Images\n",
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+ "\n",
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+ "Extract speaker notes and slide text, then convert the presentation to high-quality images for AI analysis."
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 34,
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+ "id": "644ee94c",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "PROCESSING: Converting PPTX to images and extracting notes...\n",
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+ "Processing: All About Llamas.pptx\n",
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+ "Extracting speaker notes...\n",
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+ "Found notes on 10 of 10 slides\n",
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+ "Notes df saved to: /Users/yucedincer/Desktop/Projects/llama-cookbook/end-to-end-use-cases/powerpoint-to-voiceover-transcript/output/All About Llamas_notes.csv\n",
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+ "Converting to PDF...\n",
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+ "Converting to PNG images at 200 DPI...\n",
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+ "\n",
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+ "Successfully processed 10 slides\n",
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+ "Images saved to: /Users/yucedincer/Desktop/Projects/llama-cookbook/end-to-end-use-cases/powerpoint-to-voiceover-transcript/output\n",
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+ "\n",
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+ "SUCCESS: Processing completed successfully!\n",
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+ "- Processed 10 slides\n",
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+ "- Images saved to: /Users/yucedincer/Desktop/Projects/llama-cookbook/end-to-end-use-cases/powerpoint-to-voiceover-transcript/output\n",
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+ "- Found notes on 10 slides\n",
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+ "- DataFrame shape: (10, 8)\n",
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+ "\n",
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+ "Sample Data (First 5 slides):\n"
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+ ]
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+ },
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+ {
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+ "data": {
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+ "text/html": [
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+ "<div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>slide_number</th>\n",
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+ " <th>slide_title</th>\n",
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+ " <th>has_notes</th>\n",
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+ " <th>notes_word_count</th>\n",
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+ " <th>slide_text_word_count</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>1</td>\n",
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+ " <td>Llamas: Fascinating Animals of the Andes</td>\n",
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+ " <td>True</td>\n",
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+ " <td>34</td>\n",
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+ " <td>14</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>2</td>\n",
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+ " <td>Introduction to Llamas</td>\n",
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+ " <td>True</td>\n",
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+ " <td>28</td>\n",
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+ " <td>25</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2</th>\n",
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+ " <td>3</td>\n",
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+ " <td>Physical Characteristics</td>\n",
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+ " <td>True</td>\n",
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+ " <td>28</td>\n",
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+ " <td>33</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>3</th>\n",
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+ " <td>4</td>\n",
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+ " <td>Diet & Habitat</td>\n",
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+ " <td>True</td>\n",
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+ " <td>24</td>\n",
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+ " <td>23</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>4</th>\n",
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+ " <td>5</td>\n",
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+ " <td>Behavior & Social Structure</td>\n",
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+ " <td>True</td>\n",
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+ " <td>31</td>\n",
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+ " <td>30</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "</div>"
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+ ],
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+ "text/plain": [
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+ " slide_number slide_title has_notes \\\n",
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+ "0 1 Llamas: Fascinating Animals of the Andes True \n",
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+ "1 2 Introduction to Llamas True \n",
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+ "2 3 Physical Characteristics True \n",
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+ "3 4 Diet & Habitat True \n",
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+ "4 5 Behavior & Social Structure True \n",
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+ "\n",
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+ " notes_word_count slide_text_word_count \n",
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+ "0 34 14 \n",
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+ "1 28 25 \n",
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+ "2 28 33 \n",
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+ "3 24 23 \n",
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+ "4 31 30 "
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
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+ ],
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+ "source": [
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+ "print(\"PROCESSING: Converting PPTX to images and extracting notes...\")\n",
|
|
|
+ "\n",
|
|
|
+ "result = pptx_to_images_and_notes(\n",
|
|
|
+ " pptx_path=pptx_file,\n",
|
|
|
+ " output_dir=output_dir,\n",
|
|
|
+ " extract_notes=True\n",
|
|
|
+ ")\n",
|
|
|
+ "\n",
|
|
|
+ "notes_df = result['notes_df']\n",
|
|
|
+ "image_files = result['image_files']\n",
|
|
|
+ "\n",
|
|
|
+ "print(f\"\\nSUCCESS: Processing completed successfully!\")\n",
|
|
|
+ "print(f\"- Processed {len(image_files)} slides\")\n",
|
|
|
+ "print(f\"- Images saved to: {result['output_dir']}\")\n",
|
|
|
+ "print(f\"- Found notes on {notes_df['has_notes'].sum()} slides\")\n",
|
|
|
+ "print(f\"- DataFrame shape: {notes_df.shape}\")\n",
|
|
|
+ "\n",
|
|
|
+ "# Show sample data\n",
|
|
|
+ "print(\"\\nSample Data (First 5 slides):\")\n",
|
|
|
+ "display(notes_df[['slide_number', 'slide_title', 'has_notes', 'notes_word_count', 'slide_text_word_count']].head())"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "markdown",
|
|
|
+ "id": "1f95749d",
|
|
|
+ "metadata": {},
|
|
|
+ "source": [
|
|
|
+ "### Step 2: Generate Narrative-Aware AI Transcripts\n",
|
|
|
+ "\n",
|
|
|
+ "Use the Llama vision model to analyze each slide image and generate natural-sounding voiceover transcripts with narrative continuity.\n",
|
|
|
+ "\n",
|
|
|
+ "This process:\n",
|
|
|
+ "- Analyzes slide visual content using AI vision\n",
|
|
|
+ "- Uses transcripts from previous slides as context\n",
|
|
|
+ "- Combines slide content with speaker notes\n",
|
|
|
+ "- Generates speech-optimized transcripts with smooth transitions\n",
|
|
|
+ "- Maintains consistent terminology throughout the presentation\n",
|
|
|
+ "- Converts numbers and technical terms to spoken form"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 35,
|
|
|
+ "id": "fe564b99",
|
|
|
+ "metadata": {},
|
|
|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "name": "stdout",
|
|
|
+ "output_type": "stream",
|
|
|
+ "text": [
|
|
|
+ "PROCESSING: Starting AI transcript generation with unified processor...\n",
|
|
|
+ "- Processing 10 slides\n",
|
|
|
+ "- Using model: Llama-4-Maverick-17B-128E-Instruct-FP8\n",
|
|
|
+ "- Mode: Narrative Continuity\n",
|
|
|
+ "- Context window: 5 previous slides\n",
|
|
|
+ "- Using previous transcripts as context for narrative continuity\n",
|
|
|
+ "- This may take several minutes...\n",
|
|
|
+ "Processing 10 slides with narrative continuity...\n",
|
|
|
+ "Using context window of 5 previous slides\n"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "name": "stderr",
|
|
|
+ "output_type": "stream",
|
|
|
+ "text": [
|
|
|
+ "Processing slides: 100%|████████████████████████| 10/10 [00:28<00:00, 2.88s/it]"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "name": "stdout",
|
|
|
+ "output_type": "stream",
|
|
|
+ "text": [
|
|
|
+ "Context information saved to: output/narrative_context\n",
|
|
|
+ "\n",
|
|
|
+ "SUCCESS: Transcript generation completed!\n",
|
|
|
+ "- Generated 10 transcripts\n",
|
|
|
+ "- Average length: 703 characters\n",
|
|
|
+ "- Total words: 1,019\n",
|
|
|
+ "- Context information saved to: output/narrative_context/\n",
|
|
|
+ "- Average context slides used: 3.5\n"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "name": "stderr",
|
|
|
+ "output_type": "stream",
|
|
|
+ "text": [
|
|
|
+ "\n"
|
|
|
+ ]
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "print(\"PROCESSING: Starting AI transcript generation with unified processor...\")\n",
|
|
|
+ "print(f\"- Processing {len(notes_df)} slides\")\n",
|
|
|
+ "print(f\"- Using model: {config['api']['llama_model']}\")\n",
|
|
|
+ "print(f\"- Mode: {'Narrative Continuity' if USE_NARRATIVE else 'Standard Processing'}\")\n",
|
|
|
+ "if USE_NARRATIVE:\n",
|
|
|
+ " print(f\"- Context window: {CONTEXT_WINDOW_SIZE} previous slides\")\n",
|
|
|
+ " print(f\"- Using previous transcripts as context for narrative continuity\")\n",
|
|
|
+ "print(\"- This may take several minutes...\")\n",
|
|
|
+ "\n",
|
|
|
+ "# Generate transcripts using the unified processor\n",
|
|
|
+ "processed_df = processor.process_slides_dataframe(\n",
|
|
|
+ " df=notes_df,\n",
|
|
|
+ " output_dir=output_dir,\n",
|
|
|
+ " save_context=True # Only saves context if USE_NARRATIVE=True\n",
|
|
|
+ ")\n",
|
|
|
+ "\n",
|
|
|
+ "print(f\"\\nSUCCESS: Transcript generation completed!\")\n",
|
|
|
+ "print(f\"- Generated {len(processed_df)} transcripts\")\n",
|
|
|
+ "print(f\"- Average length: {processed_df['ai_transcript'].str.len().mean():.0f} characters\")\n",
|
|
|
+ "print(f\"- Total words: {processed_df['ai_transcript'].str.split().str.len().sum():,}\")\n",
|
|
|
+ "\n",
|
|
|
+ "if USE_NARRATIVE:\n",
|
|
|
+ " print(f\"- Context information saved to: {output_dir}narrative_context/\")\n",
|
|
|
+ " print(f\"- Average context slides used: {processed_df['context_slides_used'].mean():.1f}\")"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "markdown",
|
|
|
+ "id": "5cff4b70",
|
|
|
+ "metadata": {},
|
|
|
+ "source": [
|
|
|
+ "### Step 3: Save Results\n",
|
|
|
+ "\n",
|
|
|
+ "Save results in multiple formats for different use cases."
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 37,
|
|
|
+ "id": "8463ac3a",
|
|
|
+ "metadata": {},
|
|
|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "name": "stdout",
|
|
|
+ "output_type": "stream",
|
|
|
+ "text": [
|
|
|
+ "PROCESSING: Saving results in multiple formats...\n",
|
|
|
+ "- SUCCESS: Complete results saved to output/narrative_transcripts.csv\n",
|
|
|
+ "- SUCCESS: Clean transcripts saved to output/narrative_transcripts_clean.csv\n",
|
|
|
+ "- SUCCESS: JSON format saved to output/narrative_transcripts.json\n",
|
|
|
+ "\n",
|
|
|
+ "Export Summary:\n",
|
|
|
+ "- Processing mode: Narrative Continuity\n",
|
|
|
+ "- Total slides processed: 10\n",
|
|
|
+ "- Slides with speaker notes: 10\n",
|
|
|
+ "- Total transcript words: 1,019\n",
|
|
|
+ "- Average transcript length: 703 characters\n",
|
|
|
+ "- Estimated reading time: 6.8 minutes\n",
|
|
|
+ "- Average context slides per slide: 3.5\n"
|
|
|
+ ]
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "print(\"PROCESSING: Saving results in multiple formats...\")\n",
|
|
|
+ "\n",
|
|
|
+ "# Create output directory\n",
|
|
|
+ "os.makedirs(output_dir, exist_ok=True)\n",
|
|
|
+ "\n",
|
|
|
+ "# Determine file prefix based on processing mode\n",
|
|
|
+ "file_prefix = \"narrative\" if USE_NARRATIVE else \"standard\"\n",
|
|
|
+ "\n",
|
|
|
+ "# Save complete results with all metadata\n",
|
|
|
+ "output_file = f\"{output_dir}{file_prefix}_transcripts.csv\"\n",
|
|
|
+ "processed_df.to_csv(output_file, index=False)\n",
|
|
|
+ "print(f\"- SUCCESS: Complete results saved to {output_file}\")\n",
|
|
|
+ "\n",
|
|
|
+ "# Save transcript-only version for voiceover work\n",
|
|
|
+ "if USE_NARRATIVE:\n",
|
|
|
+ " transcript_only = processed_df[['slide_number', 'slide_title', 'ai_transcript', 'context_slides_used']]\n",
|
|
|
+ "else:\n",
|
|
|
+ " transcript_only = processed_df[['slide_number', 'slide_title', 'ai_transcript']]\n",
|
|
|
+ "\n",
|
|
|
+ "transcript_file = f\"{output_dir}{file_prefix}_transcripts_clean.csv\"\n",
|
|
|
+ "transcript_only.to_csv(transcript_file, index=False)\n",
|
|
|
+ "print(f\"- SUCCESS: Clean transcripts saved to {transcript_file}\")\n",
|
|
|
+ "\n",
|
|
|
+ "# Save as JSON for API integration\n",
|
|
|
+ "json_file = f\"{output_dir}{file_prefix}_transcripts.json\"\n",
|
|
|
+ "processed_df.to_json(json_file, orient='records', indent=2)\n",
|
|
|
+ "print(f\"- SUCCESS: JSON format saved to {json_file}\")\n",
|
|
|
+ "\n",
|
|
|
+ "# Summary statistics\n",
|
|
|
+ "total_words = processed_df['ai_transcript'].str.split().str.len().sum()\n",
|
|
|
+ "reading_time = total_words / 150 # Assuming 150 words per minute\n",
|
|
|
+ "\n",
|
|
|
+ "print(f\"\\nExport Summary:\")\n",
|
|
|
+ "print(f\"- Processing mode: {'Narrative Continuity' if USE_NARRATIVE else 'Standard Processing'}\")\n",
|
|
|
+ "print(f\"- Total slides processed: {len(processed_df)}\")\n",
|
|
|
+ "print(f\"- Slides with speaker notes: {processed_df['has_notes'].sum()}\")\n",
|
|
|
+ "print(f\"- Total transcript words: {total_words:,}\")\n",
|
|
|
+ "print(f\"- Average transcript length: {processed_df['ai_transcript'].str.len().mean():.0f} characters\")\n",
|
|
|
+ "print(f\"- Estimated reading time: {reading_time:.1f} minutes\")\n",
|
|
|
+ "\n",
|
|
|
+ "if USE_NARRATIVE and 'context_slides_used' in processed_df.columns:\n",
|
|
|
+ " print(f\"- Average context slides per slide: {processed_df['context_slides_used'].mean():.1f}\")"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "markdown",
|
|
|
+ "id": "8728d2ac",
|
|
|
+ "metadata": {},
|
|
|
+ "source": [
|
|
|
+ "---\n",
|
|
|
+ "# Completion Summary\n",
|
|
|
+ "\n",
|
|
|
+ "## Successfully Generated:\n",
|
|
|
+ "- **Unified Processing**: Single processor handles both standard and narrative modes\n",
|
|
|
+ "- **Flexible Configuration**: Easy switching between processing modes\n",
|
|
|
+ "- **Speech-Optimized Transcripts**: Natural-sounding voiceover content\n",
|
|
|
+ "- **Multiple Formats**: CSV, JSON exports for different use cases\n",
|
|
|
+ "- **Context Analysis**: Detailed information about narrative flow (when enabled)\n",
|
|
|
+ "\n",
|
|
|
+ "## Output Files:\n",
|
|
|
+ "- `[mode]_transcripts.csv` - Complete dataset with metadata\n",
|
|
|
+ "- `[mode]_transcripts_clean.csv` - Clean transcripts for voiceover work\n",
|
|
|
+ "- `[mode]_transcripts.json` - JSON format for API integration\n",
|
|
|
+ "- `narrative_context/` - Context analysis files (narrative mode only)\n",
|
|
|
+ "- Individual slide images in PNG/JPEG format\n",
|
|
|
+ "\n",
|
|
|
+ "## Processing Modes:\n",
|
|
|
+ "\n",
|
|
|
+ "### Standard Mode (`USE_NARRATIVE = False`)\n",
|
|
|
+ "- **Best for**: Simple presentations, quick processing, independent slides\n",
|
|
|
+ "- **Features**: Fast execution, no context dependencies\n",
|
|
|
+ "- **Use cases**: Training materials, product demos, standalone slides\n",
|
|
|
+ "\n",
|
|
|
+ "### Narrative Mode (`USE_NARRATIVE = True`)\n",
|
|
|
+ "- **Best for**: Story-driven presentations, complex topics, educational content\n",
|
|
|
+ "- **Features**: Context awareness, smooth transitions, terminology consistency\n",
|
|
|
+ "- **Use cases**: Conference talks, educational courses, marketing presentations\n",
|
|
|
+ "\n",
|
|
|
+ "## Next Steps:\n",
|
|
|
+ "1. **Review** generated transcripts for accuracy and flow\n",
|
|
|
+ "2. **Edit** any content that needs refinement\n",
|
|
|
+ "3. **Create** voiceover recordings or use TTS systems\n",
|
|
|
+ "4. **Integrate** JSON data into your video production workflow\n",
|
|
|
+ "5. **Experiment** with different processing modes for optimal results\n",
|
|
|
+ "\n",
|
|
|
+ "## Tips for Better Results:\n",
|
|
|
+ "- **Rich Speaker Notes**: Detailed notes improve transcript quality in both modes\n",
|
|
|
+ "- **Clear Visuals**: High-contrast slides with readable text work best\n",
|
|
|
+ "- **Mode Selection**: Use narrative mode for complex presentations, standard for simple ones\n",
|
|
|
+ "- **Context Window**: Adjust context window size (3-7 slides) based on presentation complexity\n",
|
|
|
+ "- **Consistent Style**: Maintain consistent formatting across your presentation\n",
|
|
|
+ "\n",
|
|
|
+ "---"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": null,
|
|
|
+ "id": "7122cdf6-667e-4ae4-8ce7-67cfc32577c8",
|
|
|
+ "metadata": {},
|
|
|
+ "outputs": [],
|
|
|
+ "source": []
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "metadata": {
|
|
|
+ "kernelspec": {
|
|
|
+ "display_name": "promptTesting",
|
|
|
+ "language": "python",
|
|
|
+ "name": "prompttesting"
|
|
|
+ },
|
|
|
+ "language_info": {
|
|
|
+ "codemirror_mode": {
|
|
|
+ "name": "ipython",
|
|
|
+ "version": 3
|
|
|
+ },
|
|
|
+ "file_extension": ".py",
|
|
|
+ "mimetype": "text/x-python",
|
|
|
+ "name": "python",
|
|
|
+ "nbconvert_exporter": "python",
|
|
|
+ "pygments_lexer": "ipython3",
|
|
|
+ "version": "3.13.2"
|
|
|
+ }
|
|
|
+ },
|
|
|
+ "nbformat": 4,
|
|
|
+ "nbformat_minor": 5
|
|
|
+}
|
Yuce Dincer