Speech Recognition
267 episodes · Page 5 of 12
#6055: How to Fine-Tune ASR on Noisy Audio
A small noise library mixed into clean speech beats collecting hours of noisy recordings. Here's the ratio that actually works.
#6052: What's Actually in the Hugging Face ASR Catalog?
Four real architectural bets sit underneath thousands of Hugging Face ASR repos. Here's how to tell the bets from the repackagings.
#6048: Under the Hood, October 2026: Seventeen Prompt Versions and Three-Times-Faster Voices
Four weeks of pipeline changes, grounded in the code: grace periods, duplicate detection, Hilbert's rules, echo deletion, and voices nearly three times faster.
#6047: A Human Guest on His Own AI Podcast
Daniel built an AI podcast with cloned voices — then sat down as a guest on it. What six thousand episodes taught him about prompts.
#6032: Blending Datasets vs Merging Weights for Fine-Tuning
Merging weights happens in parameter space. Blending datasets happens in data space — and the messy part is worth it.
#6028: ASR vs Multimodal LLMs: What Actually Won
Dedicated ASR plus a cleanup model is quietly beating end-to-end dictation on the hardware you already own.
#6027: Can You Snap Two Fine-Tunes Together Like LEGO?
Two fine-tunes, one base model, and a question from Jerusalem: can you merge Hebrew code-switching with technical vocab — without retraining?
#6023: How Much Voice Data Is Enough?
Two hours of phone recordings took Hebrew code-switching from 20% to 60%. But when does more data stop mattering?
#6022: Fine-Tuning for Code-Switched Hebrew on 2 Hours of Audio
Two hours of one man's voice beat models trained on 120,000 hours — but only for code-switched Hebrew.
#6000: 6,000 Episodes and a GPU Bill: Inside My Weird Prompts
Six thousand episodes, one human in the loop, and a Modal bill that keeps growing. A behind-the-scenes update on the podcast.
#5994: Hebrew in Latin Letters: A TTS Pipeline Fix
A token classifier that finds Hebrew words written in Latin letters so TTS stops mangling them — built for under $3.
#5995: NVIDIA ASR: Parakeet, Canary, and Nemotron Explained
Whisper gives you one dial. NVIDIA gives you five. A practical map of the Parakeet, Canary, and Nemotron speech models.
#5982: Teaching a Voice Engine to Say "Yalla" Right
A small token classifier that spots Hebrew words hiding in English sentences — and why finding the word is the easy half.
#5974: One Model or Two? ASR Cleanup Explained
If speech models can punctuate and capitalize on their own, why does a separate 484MB cleanup model exist at all?
#5962: How Word Error Rate Lies to Us
WER is the number everyone reports for speech recognition — and the one that agrees with human judgment least. Here's what it actually measures.
#5959: What RTF Actually Tells You About Speech Models
Accuracy gets the headlines, but speed decides what ships. How RTF, latency percentiles, and memory shape real ASR deployments.
#5941: Five Minutes Reading Your Dictation Prompt
A listener's speech-to-text cleanup prompt, read out in full — then stress-tested against what ASR actually gets wrong.
#5938: One Regex Rule Worked, One Was Impossible
A dictation app, two text-replacement rules, and the mathematical line between what regex can match and what it provably cannot.
#5878: How a Sloth Learned to Talk: Corn's Origin Story
A neural interface gave Corn the bandwidth for speech. Nobody can explain where the accent, the manner, or Mongolia came from.
#5868: Fine-Tuning Parakeet for Um-Free, Hebrish Speech
Parakeet runs great on a phone — but fine-tuning it happens on a GPU. Here's the actual recipe for dropping ums and catching Hebrew.
#5851: The Earbud Assistant Problem Nobody Solves
The AI model is solved. So why does the assistant in your ear still fail? The answer is the unglamorous plumbing nobody demos.
#5850: Ambient Computing: Why Glasses Beat the Phone
Weiser's 1991 vision said tech should disappear. Thirty-five years later, the device that finally worked wasn't new — it was glasses.
#5840: Why Voice Clones Lisp: Sibilants, Codecs, and the Sounds We Lose
Sibilants and plosives are the most fragile sounds in speech — and every stage of the audio chain is quietly built to destroy them.
#5825: Why Voices Sound Good in Some Rooms and Not Others
Same words, opposite effect. What the research actually says about why some voices hold you and others make you reach for skip.