AI Podcast Production Pipeline

Overview

An automated workflow for producing full podcast episodes from topic ideas or articles. This pipeline handles script writing, voice generation, intro/outro music, episode assembly, and publishing — enabling creators to produce professional podcasts without recording equipment or editing skills.

The workflow is optimized for solo podcasters, content repurposing, and high-frequency publishing. It can transform blog posts, articles, or raw ideas into complete podcast episodes in under an hour.

Difficulty

Medium — Requires coordination of several AI tools but each step is well-documented.

Tools Required

Tool	Purpose
Claude / GPT-4	Script writing and show notes
ElevenLabs	Natural voice generation
Suno / Udio	AI music generation for intro/outro
FFmpeg	Audio assembly and mixing
Spotify for Podcasters API	Publishing to podcast platforms
Whisper	Optional: transcribe existing audio

Workflow Steps

Step 1: Script Generation

import anthropic

client = anthropic.Anthropic()

def generate_podcast_script(topic: str, style: str = "conversational") -> dict:
    """Generate a complete podcast script with intro, segments, and outro."""
    response = client.messages.create(
        model="claude-3-5-sonnet-latest",
        max_tokens=4000,
        messages=[
            {"role": "user", "content": f"""Create a podcast script about: {topic}

Style: {style}
Duration: ~15-20 minutes

Structure:
1. INTRO (30 seconds) - Hook the listener, introduce the topic
2. SEGMENT 1 (5 min) - Deep dive into the first aspect
3. SEGMENT 2 (5 min) - Second aspect with examples
4. SEGMENT 3 (5 min) - Practical takeaways and tips
5. OUTRO (30 seconds) - Summary, call to action, teaser for next episode

Include:
- Natural conversational dialogue (as if two hosts are chatting)
- Smooth transitions between segments
- Engaging hooks and cliffhangers
- Clear call to action at the end
- Timestamp markers for each segment

Format each section as:
[SEGMENT: name]
[HOST_A]: dialogue
[HOST_B]: dialogue
[TRANSITION]: brief bridge text"""}
        ]
    )
    
    # Parse into structured format
    script = parse_script(response.content[0].text)
    return script

def parse_script(raw_script: str) -> dict:
    """Parse raw script into structured segments."""
    import re
    
    segments = {}
    current_segment = None
    
    for line in raw_script.split("\n"):
        segment_match = re.match(r'\[SEGMENT: (.+?)\]', line)
        if segment_match:
            current_segment = segment_match.group(1)
            segments[current_segment] = []
        elif current_segment:
            segments[current_segment].append(line)
    
    return segments

Step 2: Voice Generation

def generate_podcast_audio(script: dict, host_a_voice: str, host_b_voice: str) -> list[dict]:
    """Generate audio for each segment with different voices."""
    import requests
    
    audio_segments = []
    
    for segment_name, lines in script.items():
        for line in lines:
            if line.startswith("[HOST_A]:"):
                text = line.replace("[HOST_A]:", "").strip()
                audio = generate_voice(text, host_a_voice)
                audio_segments.append({
                    "segment": segment_name,
                    "speaker": "HOST_A",
                    "audio": audio,
                    "duration": estimate_duration(text)
                })
            elif line.startswith("[HOST_B]:"):
                text = line.replace("[HOST_B]:", "").strip()
                audio = generate_voice(text, host_b_voice)
                audio_segments.append({
                    "segment": segment_name,
                    "speaker": "HOST_B",
                    "audio": audio,
                    "duration": estimate_duration(text)
                })
    
    return audio_segments

def generate_voice(text: str, voice_id: str) -> bytes:
    """Generate voice audio using ElevenLabs."""
    url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
    
    response = requests.post(url, json={
        "text": text,
        "model_id": "eleven_multilingual_v2",
        "voice_settings": {
            "stability": 0.4,
            "similarity_boost": 0.8,
            "style_exaggeration": 0.3
        }
    }, headers={
        "xi-api-key": "YOUR_ELEVENLABS_API_KEY"
    })
    
    return response.content

def estimate_duration(text: str) -> float:
    """Estimate audio duration based on word count."""
    words = len(text.split())
    return words / 2.5  # ~150 words per minute

Step 3: Music Generation

def generate_podcast_music(style: str = "upbeat", duration: int = 30) -> bytes:
    """Generate intro/outro music using Suno or Udio."""
    
    # Using Suno API
    response = requests.post(
        "https://api.suno.ai/v1/generate",
        headers={"Authorization": f"Bearer {SUNO_API_KEY}"},
        json={
            "prompt": f"Upbeat podcast intro music, professional, {duration} seconds, no vocals",
            "style": style,
            "duration": duration
        }
    )
    
    music_url = response.json()["audio_url"]
    return requests.get(music_url).content

Step 4: Audio Assembly

import subprocess

def assemble_podcast(audio_segments: list, intro_music: bytes, outro_music: bytes, output_path: str):
    """Assemble the complete podcast episode."""
    
    # Save all audio segments
    temp_files = []
    
    # Intro music
    with open("intro.mp3", "wb") as f:
        f.write(intro_music)
    temp_files.append("intro.mp3")
    
    # Silence after intro (2 seconds)
    subprocess.run(["ffmpeg", "-y", "-f", "lavfi", "-i", "anullsrc=r=48000:cl=stereo",
                    "-t", "2", "-c:a", "libmp3lame", "silence.mp3"])
    temp_files.append("silence.mp3")
    
    # Audio segments
    for i, seg in enumerate(audio_segments):
        path = f"seg_{i}.mp3"
        with open(path, "wb") as f:
            f.write(seg["audio"])
        temp_files.append(path)
        
        # Add brief pause between speakers
        if i < len(audio_segments) - 1:
            subprocess.run(["ffmpeg", "-y", "-f", "lavfi", "-i", "anullsrc=r=48000:cl=stereo",
                            "-t", "0.5", "-c:a", "libmp3lame", f"pause_{i}.mp3"])
            temp_files.append(f"pause_{i}.mp3")
    
    # Outro music
    with open("outro.mp3", "wb") as f:
        f.write(outro_music)
    temp_files.append("outro.mp3")
    
    # Create concat list
    with open("concat.txt", "w") as f:
        for tf in temp_files:
            f.write(f"file '{tf}'\n")
    
    # Assemble
    subprocess.run([
        "ffmpeg", "-y", "-f", "concat", "-safe", "0",
        "-i", "concat.txt",
        "-c:a", "libmp3lame", "-b:a", "192k",
        output_path
    ], check=True)
    
    return output_path

Step 5: Generate Show Notes

def generate_show_notes(script: dict, topic: str) -> str:
    """Generate podcast show notes with timestamps and links."""
    import anthropic
    
    client = anthropic.Anthropic()
    
    response = client.messages.create(
        model="claude-3-5-sonnet-latest",
        max_tokens=2000,
        messages=[{
            "role": "user",
            "content": f"""Generate podcast show notes for episode about: {topic}

Script segments:
{script}

Include:
- Episode title (catchy, under 60 characters)
- Brief description (2-3 sentences)
- Timestamped chapter markers
- Key takeaways (bullet points)
- Links to resources mentioned
- Call to action for listeners
- Tags for SEO

Format for podcast hosting platforms."""}
        ]
    )
    
    return response.content[0].text

Step 6: Publish

def publish_podcast(audio_path: str, show_notes: str, title: str):
    """Publish to Spotify for Podcasters (formerly Anchor)."""
    
    # Upload episode
    response = requests.post(
        "https://api.spotify.com/v1/podcast-episodes",
        headers={
            "Authorization": f"Bearer {SPOTIFY_ACCESS_TOKEN}",
            "Content-Type": "multipart/form-data"
        },
        files={"audio": open(audio_path, "rb")},
        data={
            "title": title,
            "description": show_notes,
            "language": "en",
            "is_explicit": "false"
        }
    )
    
    episode_url = response.json()["external_urls"]["spotify"]
    return episode_url

Example Usage

def create_podcast_episode(topic: str, output_file: str = "episode.mp3"):
    # Step 1: Generate script
    script = generate_podcast_script(topic, style="conversational")
    print(f"Generated script for: {topic}")
    
    # Step 2: Generate audio
    audio_segments = generate_podcast_audio(
        script,
        host_a_voice="Rachel",  # Female voice
        host_b_voice="Adam"     # Male voice
    )
    print(f"Generated {len(audio_segments)} audio segments")
    
    # Step 3: Generate music
    intro_music = generate_podcast_music(style="upbeat", duration=15)
    outro_music = generate_podcast_music(style="upbeat", duration=10)
    
    # Step 4: Assemble
    assemble_podcast(audio_segments, intro_music, outro_music, output_file)
    print(f"Podcast saved to {output_file}")
    
    # Step 5: Generate show notes
    show_notes = generate_show_notes(script, topic)
    print(f"Show notes:\n{show_notes}")
    
    # Step 6: Publish (optional)
    # episode_url = publish_podcast(output_file, show_notes, f"AI Podcast: {topic}")
    
    return output_file

# Run it
create_podcast_episode("The Rise of AI Agents in 2026")

Pros

• ✅ Produces professional-quality podcasts without recording
• ✅ Consistent voice and quality across episodes
• ✅ Fast production (under 1 hour per episode)
• ✅ Supports multiple languages and voices
• ✅ Easy to scale for high-frequency publishing
• ✅ Repurposes written content into audio

Cons

• ❌ AI voices still lack true human nuance
• ❌ Music generation quality varies
• ❌ Multiple API costs
• ❌ Limited emotional range in voices
• ❌ No spontaneous conversation feel
• ❌ Requires good script writing

When to Use

• Content repurposing — Turn blog posts into podcasts
• High-frequency publishing — Daily or weekly episodes
• Multilingual podcasts — Same content in multiple languages
• Educational content — Course lectures, tutorials
• News podcasts — Daily briefings, industry updates
• Accessibility — Audio version of written content

Resources

• ElevenLabs Podcast Guide
• Suno AI API
• Spotify for Podcasters
• FFmpeg Audio Guide