{ "item_title" : "Patriola's Guide to Claude", "item_author" : [" Patriola's Guide "], "item_description" : "Two paths to a voice that sounds like you. This book teaches both and is honest about what each one costs. The fast path is free. Upload thirty seconds of clean audio to a HuggingFace Space and a clone comes back - without a GPU, a training run, or any setup. A recognizable voice arrives in an afternoon. Delivery is flat, emotion absent, and you own nothing - for a demo or a quick test, that is often enough, and the book says so plainly. The thorough path takes hours of recordings, a rented GPU, and real money. What comes out is a model you own and serve yourself, with emotional range built from a labeled dataset you designed. It can sound tired or sharp or dry. Flat neutral is the floor and the model has headroom above it. Getting there requires a dataset prep pipeline, a training run on rented hardware with billing guards so a failed job does not keep charging, a checkpoint retrieval, and a serving wrapper running on a machine you control. This book covers both paths in full. The fast path is chapter one. Everything after is the thorough path. Claude runs the pipeline for the thorough path: dataset prep automation (silence trim, normalization, format conversion, metadata write), training config generation, run monitoring, checkpoint evaluation, server startup, and the verification pass that signs off before a checkpoint goes to production. The model trainer does the learning. Claude handles everything around it. Chapter coverage: the fast clone first - what zero-shot cloning is, what the reference recording must clear, and where this path ends. Dataset architecture next - the real recording numbers, the folder structure and metadata format XTTS expects, and the prep pipeline including the 44.1 kHz record / 22.05 kHz train split. Training on rented hardware: the EC2 bootstrap, the 8-fix hardening pass drawn from real failed runs, the shutdown-on-exit billing guard, and the S3 checkpoint storage pattern. Reading the run: loss curves, checkpoint selection, retrieving results after the instance terminates. Serving: pulling the checkpoint to a local GPU machine, the Flask endpoint, inference calls from other pipelines. Verification: acoustic checks, the listen test, the sign-off gate. Raw to served in the final chapter - one complete session transcript showing every seam. Written for operators who already run Claude Code across real projects and want a voice in their pipeline they actually own - and want to know exactly what that costs before committing to it.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/818/086/9798180867469_b.jpg", "price_data" : { "retail_price" : "7.99", "online_price" : "7.99", "our_price" : "7.99", "club_price" : "7.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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