Welcome to your first multimodal AI project!
Most online videos are long, but users often want the key insights quickly. In this project, you'll build an AI system that watches a video, converts speech to text, and generates a concise summary.
This project combines:
✅ Speech Recognition
✅ Natural Language Processing NLP
✅ Generative AI
✅ Transformers
🎯 Project Goal
Build an AI application that can:
✅ Upload a video
✅ Extract audio
✅ Convert speech to text
✅ Generate AI summaries
✅ Highlight key points
✅ Export notes
🧠 Skills You'll Learn
AI & NLP
Speech-to-Text
Text Summarization
Transformers
Generative AI
Python
File Processing
APIs
Data Handling
Libraries
OpenAI Whisper
Transformers
FFmpeg
Streamlit
📌 How the System Works
Video File
Audio Extraction
Speech-to-Text
Transcript
LLM / Transformer
Summary
📂 Step 1: Install Required Libraries
pip install openai-whisper
pip install transformers
pip install streamlit
pip install moviepy
🎬 Step 2: Upload Video
import streamlit as st
video = st.file_uploader(
"Upload Video",
type=["mp4"]
)
🔊 Step 3: Extract Audio
Using MoviePy:
from moviepy.editor import VideoFileClip
video_clip = VideoFileClip("video.mp4")
audio_clip = video_clip.audio
audio_clip.write_audiofile("audio.wav")
🎙️ Step 4: Convert Speech to Text
Using Whisper:
import whisper
model = whisper.load_model("base")
result = model.transcribe("audio.wav")
transcript = result["text"]
print(transcript)
📄 Example Transcript
Welcome everyone to today's Data Analytics workshop...
The AI now understands everything spoken in the video.
🧠 Step 5: Generate Summary
Using Transformers:
from transformers import pipeline
summarizer = pipeline("summarization")
summary = summarizer(transcript, max_length=150, min_length=50)
📋 Example Output
Original Transcript 5000 words
Summary Today's workshop covered SQL, Power BI, and Python fundamentals. Participants learned dashboard development and data visualization.
✨ Step 6: Create Multiple Summary Types
Short Summary 5 bullet points
Detailed Summary 300-word explanation
Executive Summary Key decisions and action items
Users can choose the format they prefer.
🎯 Step 7: Extract Key Topics
Prompt AI: Identify the main topics discussed.
Output:
1. SQL Basics
2. Power BI
3. Data Visualization
4. Dashboard Design
⏱️ Step 8: Generate Timestamps
Example:
00:00 Introduction
05:30 SQL Basics
18:10 Power BI
35:45 Dashboard Demo
This helps users jump directly to important sections.
🎨 Step 9: Build Streamlit Interface
st.title("AI Video Summarizer")
uploaded_video = st.file_uploader("Upload Video")
if uploaded_video:
st.video(uploaded_video)
if st.button("Summarize"):
summary = generate_summary()
st.write(summary)📊 Step 10: Add Export Options
Allow users to download:
✅ Summary
✅ Transcript
✅ Notes
✅ PDF Report
🚀 Step 11: Deploy Online
Deployment Options:
Render
Railway
Hugging Face Spaces
⭐ Features to Add
Beginner
✅ Video Upload
✅ Transcript Generation
✅ Summary Creation
Intermediate
✅ Topic Extraction
✅ Timestamp Generation
✅ Multi-Language Support
Advanced
✅ YouTube URL Summarization
✅ Meeting Notes Generator
✅ Action Item Detection
✅ Speaker Identification
📂 Project Structure
ai-video-summarizer/
videos/
audio/
transcripts/
summaries/
app.py
summarizer.py
requirements.txt
README.md
screenshots/