How to Create an AI Podcast Transcription and Summary System

An ai podcast transcription and summary system automated end-to-end turns every episode into searchable text, show notes, social posts, and blog content. I build these for podcast producers who record weekly but never have time to repurpose. Drop the audio file in, get a full content package out.

One hour of conversation becomes a week of content.

What You Need Before Starting

• Podcast audio files (MP3, WAV)
• Python 3.8+ with whisper, anthropic, and pydub
• Storage for transcripts and summaries
• A publishing workflow for the generated content

Step 1: Transcribe the Episode

import whisper

model = whisper.load_model("medium")

def transcribe_episode(audio_path):
    result = model.transcribe(audio_path, language="en")
    return {
        "full_text": result["text"],
        "segments": [{"start": s["start"], "end": s["end"], "text": s["text"]} for s in result["segments"]],
        "duration": result["segments"][-1]["end"] if result["segments"] else 0
    }

Step 2: Generate Episode Summary

import anthropic

client = anthropic.Anthropic()

def generate_summary(transcript_text, episode_title):
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=800,
        messages=[{
            "role": "user",
            "content": f"""Create a content package from this podcast transcript.

Episode: {episode_title}

Generate:
1. A 3-sentence episode summary
2. 5-7 key takeaways as bullet points
3. Show notes with timestamps (use the approximate minute marks)
4. 3 social media post ideas (Twitter-length)
5. A blog post outline based on the main theme

Transcript:
{transcript_text[:8000]}"""
        }]
    )
    return response.content[0].text

Step 3: Extract Timestamps for Show Notes

def generate_chapters(segments, num_chapters=5):
    total_duration = segments[-1]["end"]
    chapter_length = total_duration / num_chapters

    chapters = []
    for i in range(num_chapters):
        start_time = i * chapter_length
        relevant_segments = [s for s in segments if s["start"] >= start_time and s["start"] < (i + 1) * chapter_length]

        if relevant_segments:
            chunk_text = " ".join([s["text"] for s in relevant_segments[:10]])
            title = generate_chapter_title(chunk_text)
            chapters.append({
                "timestamp": format_timestamp(start_time),
                "title": title
            })

    return chapters

def format_timestamp(seconds):
    minutes = int(seconds // 60)
    secs = int(seconds % 60)
    return f"{minutes:02d}:{secs:02d}"

def generate_chapter_title(text):
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=20,
        messages=[{"role": "user", "content": f"Write a 3-5 word chapter title for this section:\n{text[:500]}"}]
    )
    return response.content[0].text.strip()

Step 4: Build the Full Pipeline

def process_episode(audio_path, episode_title, output_folder):
    import os
    os.makedirs(output_folder, exist_ok=True)

    # Transcribe
    transcript = transcribe_episode(audio_path)

    # Save transcript
    with open(os.path.join(output_folder, "transcript.txt"), "w") as f:
        f.write(transcript["full_text"])

    # Generate content package
    summary = generate_summary(transcript["full_text"], episode_title)
    with open(os.path.join(output_folder, "content_package.md"), "w") as f:
        f.write(summary)

    # Generate chapters
    chapters = generate_chapters(transcript["segments"])

    return {
        "transcript_words": len(transcript["full_text"].split()),
        "duration_minutes": round(transcript["duration"] / 60, 1),
        "chapters": chapters,
        "content_package": summary
    }

Step 5: Generate Social Clips

Identify the most quotable moments for short-form content:

def find_quotable_moments(transcript_text, count=5):
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=500,
        messages=[{
            "role": "user",
            "content": f"""Find the {count} most quotable/shareable moments from this transcript.
For each, provide the exact quote and a one-line context note.

Transcript:
{transcript_text[:8000]}"""
        }]
    )
    return response.content[0].text

What to Build Next

Add automatic audiogram generation. Take the best quotes, pair them with a waveform animation, and export as video clips for social media. Audiograms get 5x more engagement than text-only podcast promotion.

Related Reading

• AI for Content Creation at Scale - podcast repurposing as a content multiplication strategy
• The One Person Company Is Here - one person running a full podcast operation
• The Data Flywheel Explained - podcast content feeding the content flywheel