Build Voice AI, Part 1: STT Done Right
Автор: Aditya Jethani
Загружено: 2025-10-29
Просмотров: 22
Build Voice AI, Part 1: STT Done Right — learn how speech‑to‑text turns your mic input into clean, real‑time transcripts in Python using a modern cloud API.
This episode demystifies STT fundamentals, latency vs accuracy trade‑offs, VAD, sample rate, punctuation, noise handling, and saving clean text to feed your LLM next.
No prior AI experience required — follow along step‑by‑step and ship your first mic‑to‑text pipeline today.
What you’ll learn
How STT works end‑to‑end: mic → stt.py → file.txt, including streaming vs batch and when to use each.
Practical setup: audio devices, sample rates, VAD, punctuation, and noise reduction for higher accuracy.
Reliable engineering: retries, timeouts, partials buffering, and writing clean text for downstream LLMs.
Exactly how to test, benchmark, and validate transcripts before moving to LLM and TTS in Parts 2 and 3.
Timestamps:
00:00 Overview and goals.
01:35 Architecture: Mic → stt.py → text.txt (series roadmap).
05:45 Setup: Components and APIs
07:05 What is Groq Cloud
08:15 Creating a free API key
09:20 Building STT
10:39 Testing Boiler Function
13:30 Understanding VAD with STT
16:30 What are speech segments
19:30 Testing the complete module
20:36 Summing up
If this helped, like the video, subscribe for Parts 2 and 3, and drop questions you want covered next.
Code, notes, and updates will be in the pinned comment for easy access.
Hashtags
#ai #speechtotext #free #unique #generativeai #STT #Python #realtime #assistant #llm #tts #project #freeapi
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