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Part 3: Speech Models Converge Toward LLMs — TTS and Speech2Speech Training Data
AI
GenAI

Part 3: Speech Models Converge Toward LLMs — TTS and Speech2Speech Training Data

Text-to-speech once used the Mel spectrogram as its answer. But recent models convert audio into "audio tokens" and predict those — a structure that now looks just like an LLM. Part 3 follows how training data is built for TTS and Speech2Speech (speech translation, voice conversion, end-to-end conversational AI), including the difficulty of gathering paired data and how it's worked around. Part 3 of a 5-part series.

#ai#learning
Part 2: Image Generation Guesses the "Noise" or the "Token" — TXT2IMG Training Data and Captions
AI
GenAI

Part 2: Image Generation Guesses the "Noise" or the "Token" — TXT2IMG Training Data and Captions

Image generation comes in lineages — GAN, autoregressive (token) type, and diffusion — and today's mainstream is diffusion models like Stable Diffusion. The diffusion type learns to "guess the added noise" rather than "draw the image directly," while the autoregressive type guesses the "next image token" like an LLM. Part 2 covers gathering image-text pairs, CLIP quality filtering, and the training data for both approaches — with minimal code. Part 2 of a 5-part series.

#ai#stablediffusion#learning
Part 1: What Does Generative AI Learn as the "Answer"? — Self-Supervised Learning and LLMs
AI
GenAI

Part 1: What Does Generative AI Learn as the "Answer"? — Self-Supervised Learning and LLMs

Neither LLMs nor image generators learn from training data that humans have labeled item by item. The key is self-supervised learning: mechanically constructing inputs and answers from raw data. Part 1 clarifies the difference between raw data and training data, and shows how an LLM turns text itself into a next-token prediction problem, with minimal code. Part 1 of a 5-part series.

#ai#learning#data

Part 3: Speech Models Converge Toward LLMs — TTS and Speech2Speech Training Data

Text-to-speech once used the Mel spectrogram as its answer. But recent models convert audio into "audio tokens" and predict those — a structure that now looks just like an LLM. Part 3 follows how training data is built for TTS and Speech2Speech (speech translation, voice conversion, end-to-end conversational AI), including the difficulty of gathering paired data and how it's worked around. Part 3 of a 5-part series.

Part 2: Image Generation Guesses the "Noise" or the "Token" — TXT2IMG Training Data and Captions

Image generation comes in lineages — GAN, autoregressive (token) type, and diffusion — and today's mainstream is diffusion models like Stable Diffusion. The diffusion type learns to "guess the added noise" rather than "draw the image directly," while the autoregressive type guesses the "next image token" like an LLM. Part 2 covers gathering image-text pairs, CLIP quality filtering, and the training data for both approaches — with minimal code. Part 2 of a 5-part series.

Part 1: What Does Generative AI Learn as the "Answer"? — Self-Supervised Learning and LLMs

Neither LLMs nor image generators learn from training data that humans have labeled item by item. The key is self-supervised learning: mechanically constructing inputs and answers from raw data. Part 1 clarifies the difference between raw data and training data, and shows how an LLM turns text itself into a next-token prediction problem, with minimal code. Part 1 of a 5-part series.

Part 3: Make AI Your Student and Escape the Intermediate Trap — Graduating from "I Thought I Understood"

The key for intermediates who stall at "I basically get it" is to teach AI. Verbalizing your premises, constraints, criteria, and edge cases becomes training that turns tacit knowledge into explicit knowledge. A learning method that uses AI as a mirror of your understanding, plus a practical five-step routine.

Part 2: Beginners, Build Experience with AI — The Ones Who Grow Fastest Are Beginners

In an era where AI hands you answers, are beginners at a disadvantage? Quite the opposite. By trying sticky material—dev environments, containers, VMs—together with AI and failing together, beginners build experience the fastest through iteration. Includes how to let AI fail a lot and learn by watching. Part 2.