Voice: transcription in, speech out | Master AI Automation in 4 hours Master AI Automation in 4 hours Course About Ayush Modules Sample chapter Toolbox The Microcap Minute Classroom / Module 09: Images, Voice & Video / Chapter 3 Voice: transcription in, speech out Watch first, then read. Same lesson, your pace. What you will learn – Turning audio into accurate text (free options) – Turning text into natural speech – Where voice cloning crosses lines Audio → text Transcription is a solved problem at free-tier level: Built-in dictation (phone keyboards, Google Docs voice typing), instant, decent, zero setup WhatsApp’s own voice-message transcripts , for the messages you can’t play aloud in class Whisper (OpenAI’s open-source model), the quality benchmark; runs via free web demos or locally if your machine allows; handles Indian accents and Hinglish far better than older tools Chat apps increasingly accept an audio file upload: “transcribe this” works on uploaded lectures/meetings The workflow that changes lives: record lectures/meetings/your own revision ramblings → transcribe → feed transcript to Module 8’s patterns (summaries, flashcards, extraction). Your spoken thoughts become searchable notes. Text → speech Free tiers everywhere: Gemini/Google Translate voices, Edge Read Aloud, ElevenLabs’ limited free characters (the most natural-sounding), plus open-source options. Uses: listening to notes while walking, narrating study decks, podcast-style digests (Module 6 Chapter 5’s digest pattern with a voice attached). For longer listens, generate per-section and keep scripts in files, regeneration gets expensive in quota otherwise. Cloning: one paragraph of seriousness Modern tools can clone a voice from seconds of audio. The capability is real; so are the laws and harms. The course line: Clone only your own voice, or with explicit written consent. Label synthetic voice content. Never imitate real people to deceive anyone, pranks included. Deepfake audio scams are a Module 11 topic precisely because they work. Being fluent in the tools means being fluent in their misuse, choose fluency without misuse. Try it yourself Record 2 minutes of yourself explaining today’s hardest concept from any subject (teaching = best revision, Module 2 would agree). Transcribe it free (dictation or Whisper demo). Clean up the transcript with R-C-T-F prompting (“keep my voice, fix grammar only”). Then TTS it back and listen. Save transcript + tool names used in learn/voice-loop.md . You’ve built the speak-study-listen loop. Key takeaways – Transcription is free and good now; Whisper-class handles accents/Hinglish well. – Record → transcribe → summarise turns talk into searchable notes. – TTS free tiers suffice for learning; generate per section. – Clone only yourself/consenting adults; label synthetic voices always. Download the exercise sheet (PDF) Module workbook (PDF) ← Prev: Vision: making AI see Next: Video, avatars and honesty labels → Classroom / Module 09: Images, Voice & Video / Chapter 3 Voice: transcription in, speech out What you will learn – Turning audio into accurate text (free options) – Turning text into natural speech – Where voice cloning crosses lines Audio → text Transcription is a solved problem at free-tier level: Built-in dictation (phone keyboards, Google Docs voice typing), instant, decent, zero setup WhatsApp’s own voice-message transcripts , for the messages you can’t play aloud in class Whisper (OpenAI’s open-source model), the quality benchmark; runs via free web demos or locally if your machine allows; handles Indian accents and Hinglish far better than older tools Chat apps increasingly accept an audio file upload: “transcribe this” works on uploaded lectures/meetings The workflow that changes lives: record lectures/meetings/your own revision ramblings → transcribe → feed transcript to Module 8’s patterns (summaries, flashcards, extraction). Your spoken thoughts become searchable notes. Text → speech Free tiers everywhere
Voice: transcription in, speech out
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