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Ryan Banze
Ryan Banze

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From Podcast to AI Summary: How I Built a Podcast Summarizer in Colab

#webdev #ai #programming #javascript

🌍 Why Podcast Summarization Matters

Podcasts are one of the fastest-growing media formats, but their long-form nature makes them hard to consume for busy listeners.

A 2-hour conversation can hide 10 minutes of golden insights that most people never hear.

That raised a question for me:

“What if podcasts could summarize themselves?”

Instead of manually listening, transcribing, and editing, I wanted a one-click, zero-setup pipeline:

  • Pull a podcast 🎧
  • Transcribe it 🗣️
  • Chunk intelligently ✂️
  • Summarize with layered AI 🧠
  • Turn into visuals 🎨
  • Narrate + polish into a short video 🎞️

✅ No APIs required

✅ No paid GPUs required (Colab handles it)

✅ All in one notebook, free to run

🚀 Who Is This For?

This Colab-based pipeline is useful for:

  • 🎧 Podcast junkies → Quick takeaways without full episodes
  • 🎥 Content creators → Repurpose audio into Shorts, TikToks, Reels
  • 🧠 AI enthusiasts → Real-world NLP + generative workflows
  • 🛠️ Developers → Build and extend a working summarizer pipeline

🛠️ Step-by-Step Breakdown

🎥 1. Pulling Audio from YouTube

We use yt-dlp (an improved youtube-dl fork) to grab audio streams directly.

def download_youtube_audio(video_url, output_basename="podcast"):
    ydl_opts = {
        'format': 'bestaudio/best',
        'outtmpl': output_basename + ".%(ext)s",
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'mp3',
            'preferredquality': '192',
        }],
    }
    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        ydl.download([video_url])
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✅ Simple, reliable, and avoids copyright issues.

  • Transcribing with Whisper Whisper by OpenAI is a high-quality speech-to-text model. You don’t need an API key — it runs right in Colab! 

whisper_model = whisper.load_model("base", device="cuda")
result = whisper_model.transcribe("converted.wav")
transcript = result["text"]
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⚡ No waiting, no cost, just real-time transcription.

✂️ . Chunking the Transcript (Smartly)
To keep summaries relevant and within model limits, we chunk the text by tokens.


def chunk_by_tokens(text, max_tokens=1000, overlap=100):
    tokens = tokenizer.encode(text)
    chunks = []
    start = 0
    while start < len(tokens):
        end = min(start + max_tokens, len(tokens))
        chunk = tokens[start:end]
        chunks.append(tokenizer.decode(chunk))
        start += max_tokens - overlap
    return chunks
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Overlapping helps preserve context across chunk boundaries.

🧠 Summarize Each Chunk with BART (Facebook)
To efficiently handle long transcripts, we first summarize chunks using Facebook’s BART-Large-CNN, a powerful abstractive summarizer available via Hugging Face.


from transformers import pipeline

summarizer_fb_bart = pipeline("summarization", model="facebook/bart-large-cnn")
summarizer_fb_bart(["chunk of text"])
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Why BART?

  • Abstractive summarization (not just cut-paste sentences)
  • Optimized for chunked podcast transcripts
  • Outputs clear, readable summaries

✅ Why BART first? It’s fast, clean, and fine-tuned for summarization.

Summarize with Mistral (and Gemini)

  • Mistral 7B refines chunk summaries
  • Gemini 1.5 Flash generates final narration This layered approach balances speed, cost, and narrative polish. Example visual prompt:
"A tense boardroom with glowing monitors, modern executives debating AI ethics"

🎨 Create AI Images
With Stable Diffusion, we turn each prompt into an image.


import google.generativeai as genai

model = genai.GenerativeModel(model_name="gemini-1.5-flash")
response = model.generate_content(final_prompt)
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  • Noise reduced early
  • Tone aligned midstream
  • Gemini delivers a publish-worthy final narrative

🎙️ Turn Text into Voice — Pick Your AI Narrator
🔹 Option 1: Google Text-to-Speech (gTTS)
Free, fast, and easy for English voiceovers.


from gtts import gTTS

tts = gTTS(text=final_summary, lang='en')
tts.save("generated_speech.mp3")
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✅ Pros: Free, simple
❌ Cons: Only one default voice

🔹 Option 2: Microsoft Edge TTS
Dozens of high-quality voices with expressive tone.


import ipywidgets as widgets
from IPython.display import display

available_voices = ["en-US-GuyNeural", "en-US-JennyNeural", "en-GB-RyanNeural", "en-IN-NeerjaNeural"]
voice_dropdown = widgets.Dropdown(
    options=available_voices,
    description="🎙️ Pick Voice:",
    style={'description_width': 'initial'},
    layout=widgets.Layout(width='50%')
)
display(voice_dropdown)
Generate narration:
import edge_tts
import asyncio

async def generate_voice(text, voice="en-US-GuyNeural"):
    communicate = edge_tts.Communicate(text, voice)
    await communicate.save("generated_speech.mp3")

await generate_voice(final_summary, voice=voice_dropdown.value)
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✅ Pros: Natural, expressive voices
❌ Cons: Requires internet + installation

Voice Style Summary

  • Use gTTS for quick + simple narration
  • Use Edge TTS for professional-grade voices
  • Let users pick interactively with UI dropdowns

🎶 Add Background Music for Emotion & Flow
Background music makes your video engaging by:

  • Setting tone (calm, energetic, dramatic)
  • Filling silent gaps
  • Making content feel polished 

import requests
from moviepy.editor import AudioFileClip

music_url = "https://www.soundhelix.com/examples/mp3/SoundHelix-Song-1.mp3"
music_path = "music.mp3"

response = requests.get(music_url)
with open(music_path, 'wb') as f:
    f.write(response.content)

voice = AudioFileClip("generated_speech.mp3")
music = AudioFileClip("music.mp3").subclip(0, voice.duration).volumex(0.1)
Combine:
from moviepy.editor import CompositeAudioClip
final_audio = CompositeAudioClip([music, voice.set_start(0)])
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🖼️ Generate Images with Diffusers
We use Hugging Face’s 🧨 Diffusers for text-to-image.


from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16
).to("cuda")

images = [pipe(prompt).images[0] for prompt in script_scenes]
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🎞️ Final Video Assembly (MoviePy)
We now combine:

  • AI images
  • Voice narration
  • Background music 

final_audio = CompositeAudioClip([music, voice])
video = concatenate_videoclips(image_clips).set_audio(final_audio)
video.write_videofile("final_video.mp4", fps=24)
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🎁 Bonus: Why I Made This
I love podcasts, but I don’t always have time to listen.
So I asked myself: Can I turn a podcast into a 1-minute video?
This project proved the answer is yes.

🏁 Final Thoughts
This is just the beginning. You can remix this workflow to:

  • Generate thumbnails
  • Translate into other languages
  • Create TikToks or Shorts from long content

💻 Code on GitHub
📂 Source Code & Notebook: GitHub Repo

💬 Want to Support My Work?

If you enjoyed this project, consider buying me a coffee to support more free AI tutorials and tools:

👉 Buy Me a Coffee ☕

📱 Follow Me

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