『AI-Automated Podcasts: Trends and New Tools』のカバーアート

AI-Automated Podcasts: Trends and New Tools

AI-Automated Podcasts: Trends and New Tools

著者： Andres Diaz
無料で聴く

このコンテンツについて

 Is the definitive podcast for creators who want to revolutionize their production through automation and artificial intelligence. In each episode, we explore the latest tools, techniques, and trends that are transforming the podcasting industry. Specifically designed for content creators looking to optimize their workflow, this show guides you through the fascinating world of automation applied to podcasting: from AI-generated scripts and voiceovers, to automatic editing, scheduled distribution, and algorithm-driven audience analysis. Whether you're an experienced podcaster looking to scale your production or a beginner interested in launching your first project with cutting-edge technology, "AI at the Microphone" offers practical insights, step-by-step tutorials, and interviews with pioneers who are defining the future of automated audio content creation. Join us weekly to discover how AI is redefining what's possible in the world of podcasting and how you can leverage these powerful tools to create quality content with less effort and greater consistency.Copyright 2025 Andres Diaz
エピソード
  • n8n and AI: Publish your podcast while you sleep

    n8n and AI: Publish your podcast while you sleep
    2025/12/14
    Summary - The episode explains how to automate podcast publishing with n8n and AI so episodes can be published automatically across platforms (Spotify for Podcasters, iVoox, YouTube) with AI-generated titles, cover art, chapters, transcripts, keywords, and scheduled social posts, effectively letting you “publish while you sleep.” - It frames n8n as the orchestrator and AI as the editor, with the audio file as the raw material, to build a repeatable, scalable workflow that boosts consistency and audience growth. - Recent improvements to n8n (language-model integrations, content templates, a better visual editor, easier encrypted credentials) enable cloud or self-hosted runs, and scaling via queues/workers. - A practical base flow (nine steps): 1) set up an audio inbox and trigger on schedule; 2) quick validation of completeness/format; 3) optional automated postproduction (volume normalization/noise reduction); 4) transcription; 5) metadata creation (titles, description, SEO keywords, chapters); 6) AI-generated cover art; 7) publishing to each platform; 8) multi-format promotion (social posts, newsletter, blog, video script, audiogram); 9) tracking/improvement with logging and retry on failure. - Tips include clear file naming, specifying tone and audience for AI prompts, avoiding odd characters, and summarizing transcripts for clips. - Security and costs: use credential management, enforce usage quotas, and note transcription costs; automation can be optimized by reusing transcript content for social media. - Common mistakes: avoid fully autonomous publishing without human checks, give AI precise tasks, keep descriptions concise with CTAs, and don’t neglect SEO. - A short four-step starter plan for a week to implement automation, with the promise of easier publishing once set up. - The message ends with encouragement to try, subscribe, and contact the author for feedback. Remeber you can contact me at andresdiaz@bestmanagement.org
    続きを読む 一部表示
    7 分
  • Speaker Diarization with AI: Who Is Speaking and When?

    Speaker Diarization with AI: Who Is Speaking and When?
    2025/11/30
    Summary: - Topic: AI Speaker Diarization explains how to determine who spoke when in a recording, labeling speakers as Speaker A, B, C rather than identifying real names, which supports privacy and accurate transcripts. - Why it matters: Diarization underpins reliable transcripts, meeting analysis, and labeled summaries; it’s foundational for privacy and regulatory considerations. - Practical uses: Enhances podcast/video editing, automatic subtitling with voice separation, call analysis in contact centers, meeting minutes, online classes with participation metrics, and analyzing dialogue flow (interruptions, leadership, dynamics). - How it works (high level): 1) voice activity detection, 2) segmentation, 3) extracting speaker embeddings, 4) clustering, 5) refinement and overlap detection; results are labeled with timestamps. - Tools and choices: Open-source options (e.g., pyannote), embedding models (ECAPA, x-vector), pipelines (Whisper with diarization), end-to-end libraries, and cloud services. Strategic decision: on-premises for privacy vs. cloud for speed. - Actionable plan (this week): 1) Prepare audio (single track, 16 kHz, stable volume, reduce echo). 2) Choose tool (local open-source for control vs. cloud for speed/cost). 3) Tune parameters (segment length, detection thresholds, overlap sensitivity). 4) Validate and correct (watch for label jumps; refine with resegmentation or different clustering). 5) Integrate (export with timestamps, chapters, participation stats, or labeled subtitles). - Performance and evaluation: Use diarization error rate (DER) as the main metric; if no references, perform quick label-coherence checks. - What’s new: End-to-end diarization models, better overlap detection, hybrid deep representations with Bayesian clustering, and real-time latency suitable for live subtitling and moderating. - Practical tips to boost results: use individual mics, gentle denoising, trim long silences, normalize levels, and create a small “voice bank” to map known labels post-diarization (not biometric identification). - Ethics and compliance: obtain consent, inform users of automated analysis, store only necessary data; transparency improves fairness and effectiveness. - Extra benefit: diarization makes audio searchable by queries (e.g., “show me the part where the finance person discussed the budget”). - Roadmap for different use cases: podcasts/videos to speed editing and subtitles; sales/support to measure participation; teaching to create speaker-based chapters. - Closing visual: diarization maps conversations, helping you navigate conversations faster and more efficiently. - Contact: If you’d like to promote your brand on this podcast, email andresdiaz@bestmanagement.org Remeber you can contact me at andresdiaz@bestmanagement.org
    続きを読む 一部表示
    8 分
  • AI-Powered Automatic Chapters: More Retention in 5 Minutes?

    AI-Powered Automatic Chapters: More Retention in 5 Minutes?
    2025/10/12
    Summary: - The episode, hosted by Andrés Díaz, explains how AI-generated automatic chapters can boost podcast retention, listening time, and SEO in about five minutes. - Chapters are timestamps and short, catchy section titles derived from the episode’s transcription. They help listeners jump to relevant parts and can appear in search results as “key moments.” - Recent updates boost their usefulness: Apple Podcasts now supports automatic transcripts in Spanish, YouTube has better automatic chapters, and Podcasting 2.0 enables enriched chapters via RSS with text, links, and images. - Five-minute workflow: 1) Transcribe the episode (automatic tools or services) and check key terms. 2) Use AI to generate clear chapters with approximate timestamps and concise, keyword-rich titles using verbs. 3) Refine titles to emphasize benefits and solve listener questions. 4) Insert chapters into ID3 tags, RSS feed, or descriptions (and duplicate in other platforms for consistency). 5) Measure performance (average listening time, completion rate) and adjust (move essential content earlier; optimize successful chapters into clips or articles). - Practical tips: use verbs and benefits in titles, include real keywords, place an essentials chapter early, and add a final call-to-action as a chapter. - Common mistakes to avoid: vague titles like “Part One,” overly long chapters, and titles that promise content not delivered. - The piece emphasizes a fast, repeatable process and encourages experimentation with a concrete challenge: generate 4–6 chapters for the latest episode, tweak two details, publish today, and monitor results. - Additional guidance covers platform compatibility, how images or other media can accompany chapters, and the benefits of consistent chapter templates to build listener loyalty. - Final takeaway: AI-generated chapters are the quickest way to turn audio into a guided, retention-friendly experience; five focused minutes beat hours of editing. Remeber you can contact me at andresdiaz@bestmanagement.org
    続きを読む 一部表示
    8 分
まだレビューはありません