Ai Podcast Creation

Ai Podcast Creation is a community skill for automating podcast production workflows, covering script generation from topics, multi-voice synthesis, audio editing, show notes generation, and publishing pipeline integration for consistent episode delivery.

What Is This?

Overview

Ai Podcast Creation provides patterns for building automated podcast production pipelines. It covers episode script generation from topic outlines and research sources, multi-speaker voice synthesis with distinct voice profiles for host and guest personas, audio segment assembly with intro, content, and outro sections, show notes and transcript generation from episode audio, and RSS feed management for podcast distribution. The skill enables creators to produce podcast episodes consistently without manual recording and editing.

Who Should Use This

This skill serves content creators launching podcasts without recording equipment or studio access, marketing teams producing branded audio content at regular intervals, and developers building podcast automation platforms for multiple shows.

Why Use It?

Problems It Solves

Recording podcast episodes requires coordinating schedules between hosts and guests. Audio editing consumes hours per episode for removing filler words and adjusting levels. Writing show notes and transcripts manually doubles the production time per episode. Maintaining a consistent publishing schedule is difficult when production depends on human availability.

Core Highlights

Script generation transforms topic outlines into conversational dialogue between defined speaker roles. Voice synthesis produces distinct audio tracks for each speaker with natural prosody. Audio assembly combines speaker tracks with intro music and transitions into a complete episode. Show notes generation extracts key points and timestamps from the final audio.

How to Use It?

Basic Usage

from dataclasses import dataclass, field

@dataclass
class Speaker:
    name: str
    voice_id: str
    role: str = "host"

@dataclass
class ScriptSegment:
    speaker: str
    text: str
    duration_estimate: float = 0.0

class PodcastScriptWriter:
    def __init__(self, speakers: list[Speaker]):
        self.speakers = {s.name: s for s in speakers}
        self.segments: list[ScriptSegment] = []

    def add_dialogue(self, speaker_name: str,
                     text: str):
        words = len(text.split())
        duration = words / 150 * 60  # ~150 wpm
        self.segments.append(ScriptSegment(
            speaker=speaker_name, text=text,
            duration_estimate=duration))

    def total_duration(self) -> float:
        return sum(s.duration_estimate
                   for s in self.segments)

    def get_script(self) -> list[dict]:
        return [{"speaker": s.speaker, "text": s.text,
                 "duration": round(s.duration_estimate, 1)}
                for s in self.segments]

Real-World Examples

from dataclasses import dataclass, field

@dataclass
class EpisodeConfig:
    title: str
    topic: str
    speakers: list[Speaker] = field(default_factory=list)
    intro_audio: str = ""
    outro_audio: str = ""

class PodcastProducer:
    def __init__(self):
        self.episodes: list[dict] = []

    def produce_episode(self, config: EpisodeConfig,
                        script_writer: PodcastScriptWriter
                        ) -> dict:
        script = script_writer.get_script()
        audio_segments = []
        for seg in script:
            audio_segments.append({
                "speaker": seg["speaker"],
                "audio_file": f"{seg['speaker']}_"
                              f"{len(audio_segments)}.wav",
                "duration": seg["duration"]})
        total = sum(s["duration"] for s in audio_segments)
        episode = {"title": config.title,
                   "segments": len(audio_segments),
                   "duration_minutes": round(total / 60, 1)}
        self.episodes.append(episode)
        return episode

    def generate_show_notes(self,
                            script: list[dict]) -> str:
        notes = []
        timestamp = 0.0
        for seg in script:
            minutes = int(timestamp // 60)
            seconds = int(timestamp % 60)
            notes.append(
                f"[{minutes:02d}:{seconds:02d}] "
                f"{seg['speaker']}: {seg['text'][:60]}")
            timestamp += seg["duration"]
        return "\n".join(notes)

Advanced Tips

Use different voice profiles with distinct pitch and speaking rate to make multi-speaker dialogue sound natural. Generate episode transcripts alongside audio for accessibility and SEO benefits. Implement a template system for recurring episode formats that pre-fills intro and outro segments.

When to Use It?

Use Cases

Produce a daily news digest podcast that generates episodes from curated article summaries. Create an interview-format podcast where AI generates both host questions and guest responses from research material. Build a multilingual podcast that generates the same episode in multiple languages using different voice profiles.

Related Topics

Text-to-speech synthesis, audio production pipelines, RSS feed management, content scheduling automation, and transcript generation.

Important Notes

Requirements

Access to a multi-voice text-to-speech API for speaker synthesis. Audio processing tools for segment assembly and format conversion. A content source or topic pipeline for generating episode scripts.

Usage Recommendations

Do: review generated scripts for factual accuracy before synthesizing audio. Use distinct voice profiles for each speaker to maintain clarity in multi-speaker episodes. Include timestamps in show notes for easy listener navigation.

Don't: publish episodes without reviewing the script for accuracy and appropriateness. Use identical voice profiles for different speakers, making dialogue confusing. Generate episodes longer than the content supports, as padding reduces listener engagement.

Limitations

Synthesized voices may lack the natural expressiveness of human speakers. Script generation quality depends on the source material and topic complexity. Audio assembly adds processing overhead that increases with episode length.