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Deploy from Parakeet-V2-Personal-Finetune

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README.md CHANGED
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  ---
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  title: Parakeet Training Data Map
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- emoji: 🐨
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- colorFrom: green
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  colorTo: yellow
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  sdk: static
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  pinned: false
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  title: Parakeet Training Data Map
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+ emoji: 🗺️
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+ colorFrom: blue
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  colorTo: yellow
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  sdk: static
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  pinned: false
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+ license: cc-by-4.0
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+ short_description: Master dataset, modular subsets, fine-tune blends
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+ datasets:
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+ - danielrosehill/Read-Aloud-Voice-Sentences
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+ - danielrosehill/English-Hebrew-Codeswitching-Sentences
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+ - danielrosehill/Tech-Sentences
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+ - danielrosehill/AI-Interactions-Sentences
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+ - danielrosehill/Linux-Admin-Sentences
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+ - danielrosehill/Smart-Home-Sentences
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+ - danielrosehill/Personal-Vocabulary-Sentences
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+ - danielrosehill/Homophone-Context-Sentences
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+ - Hebrish/Yiddish-Sentences
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+ - Hebrish/Jewish-Religious-Terminology-Sentences
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+ - danielrosehill/General-English-Sentences
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+ - danielrosehill/AI-Tech-Vocabulary-Sentence-Bank
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+ - danielrosehill/AI-Interactions-Sentence-Bank
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+ - danielrosehill/Linux-Admin-Sentence-Bank
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+ - danielrosehill/Smart-Home-Sentence-Bank
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+ - danielrosehill/Personal-Vocabulary-Sentence-Bank
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+ - danielrosehill/Homophone-Context-Sentence-Bank
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  ---
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+ # Parakeet Training Data Map
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+
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+ How the training data for the English-Hebrew code-switching fine-tune of
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+ [`nvidia/parakeet-tdt-0.6b-v2`](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2) is organised:
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+ one master dataset ([`danielrosehill/Read-Aloud-Voice-Sentences`](https://huggingface.co/datasets/danielrosehill/Read-Aloud-Voice-Sentences)) where every take lands, modular datasets
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+ copied out of it (domain subsets and one task dataset per speech phenomenon), the text-only
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+ sentence banks, and which datasets each fine-tune blends.
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+
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+ A single static page, **generated** by `scripts/build_datasets_space.py` in the project repo from
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+ the same registries the sync and training code use, then deployed with
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+ `scripts/deploy_space.py datasets-map`. Do not edit it here; regenerate it.
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+
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+ All datasets: [training-data collection](https://huggingface.co/collections/Hebrish/parakeet-v2-fine-tune-training-data-6ad63c739d300685a62edb3e). Project page: [Hebrish/English-Hebrew-Codeswitching-Parakeet](https://huggingface.co/spaces/Hebrish/English-Hebrew-Codeswitching-Parakeet).
index.html CHANGED
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  <!doctype html>
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- <html>
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- <head>
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- <meta charset="utf-8" />
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- <meta name="viewport" content="width=device-width" />
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- <title>My static Space</title>
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- <link rel="stylesheet" href="style.css" />
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- </head>
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- <body>
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- <div class="card">
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- <h1>Welcome to your static Space!</h1>
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- <p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
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- <p>
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- Also don't forget to check the
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- <a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
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- </p>
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- </div>
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- </body>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ </style>
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+ </head>
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+ <body>
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+ <header class="top"><div class="wrap">
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+ <div class="eyebrow">Parakeet TDT 0.6B v2 · English-Hebrew code-switching fine-tune</div>
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+ <h1>Training Data Map</h1>
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+ <p class="lede">Every recording lands in one <b>master</b> dataset. Modular datasets are copied out of it, one per
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+ domain or per task, so each can be downloaded on its own. A fine-tune is a <b>blend</b>: it reads the master at a pinned
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+ revision and takes the slices its recipe names, so any task dataset can be left out of a round.</p>
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+ <nav class="toc"><a href="#flow">How it fits together</a><a href="#rules">Rules</a><a href="#blends">Blends</a>
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+ <a href="#master">Master</a><a href="#domain">Domain subsets</a><a href="#tasks">Task datasets</a><a href="#banks">Sentence banks</a></nav>
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+ </div></header>
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+ <main><div class="wrap">
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+
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+ <h2 id="flow">How it fits together</h2>
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+ <div class="flow">
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+ <div class="node"><div class="k">Text layer</div><div class="t">Sentence banks and task prompts</div>
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+ <div class="small muted">What gets read aloud. Public, text-only <code>…-Sentence-Bank</code> datasets per targeted fix, plus
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+ self-written task prompts in <code>data/task-prompts/</code>. All of it is merged into the capture app's bank.</div></div>
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+ <div class="arrow">read aloud on the phone, reviewed in the capture app</div>
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+ <div class="node master"><div class="k">Master · every take</div>
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+ <div class="t"><a href="https://huggingface.co/datasets/danielrosehill/Read-Aloud-Voice-Sentences">danielrosehill/Read-Aloud-Voice-Sentences</a></div>
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+ <div class="small muted"><span class="badge ok">on the Hub</span> <span class="badge">gated</span> <span class="size">5,558 rows · 620.7 min</span></div></div>
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+ <div class="arrow">copied unchanged on every push (same ids, columns and split; a row can be in several)</div>
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+ <div class="row2">
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+ <div class="node"><div class="k">10 domain subsets</div><div class="t">What the sentence is about</div>
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+ <div class="chips"><a href="#code_switch" class="">English-Hebrew Code-Switching</a><a href="#tech" class="">Tech</a><a href="#ai_interactions" class="">AI Interactions</a><a href="#linux" class="">Linux Admin</a><a href="#smart_home" class="">Smart Home</a><a href="#personal" class="">Personal Vocabulary</a><a href="#homophone" class="">Homophone Context</a><a href="#yiddish" class="">Yiddish</a><a href="#religious" class="">Jewish Religious Terminology</a><a href="#general" class="">General English</a></div></div>
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+ <div class="node"><div class="k">13 task datasets</div><div class="t">One speech phenomenon each</div>
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+ <div class="chips"><a href="#disfluency" class="exp missing">Disfluent Read</a><a href="#self-corrections" class="exp missing">Self-Correction</a><a href="#asides" class="exp missing">Spoken Aside</a><a href="#hard-asr" class=" missing">Hard ASR</a><a href="#brand-names" class=" missing">Brand Name</a><a href="#acronyms" class=" missing">Acronym</a><a href="#irish-english" class=" missing">Irish English</a><a href="#repetitions" class="exp missing">Repeated Word</a><a href="#singing" class="exp missing">Singing</a><a href="#weird-voices" class="exp missing">Weird Voice</a><a href="#currency" class=" missing">Currency</a><a href="#paragraphs" class="exp missing">Paragraph Dictation Passages</a><a href="#expanded-acronyms" class=" missing">Expanded Acronym</a></div></div>
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+ </div>
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+ <div class="arrow">blended per fine-tune, selected by tag from the master</div>
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+ <div class="row2">
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+ <div class="node"><div class="k">Fine-tune 1 · released</div><div class="t">Code-switching</div>
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+ <div class="small muted">~70% code-switching, ~20% own English, ~10% public replay. Latest release <b>v1.0 · Jachnun</b>: run <code>r6-en30</code>, released 2026-10-19, dataset revision <code>550bfcaa0f84</code>, English share 0.298.</div></div>
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+ <div class="node"><div class="k">Fine-tune 2 · planned</div><div class="t">Tech vocabulary and accent</div>
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+ <div class="small muted">Same base model, no code-switching rows. Nothing is built yet.</div></div>
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+ </div>
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+ </div>
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+ <p class="legend"><span><span class="badge exp">experimental</span> experimental task</span>
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+ <span><span class="badge none">dashed</span> repo not created yet</span></p>
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+
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+ <h2 id="rules">Rules</h2>
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+ <div class="card"><ul class="rules">
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+ <li><b>One phenomenon, one dataset.</b> A task take's <code>categories</code> is exactly its tag, never mixed with
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+ another, so a task dataset can be left out of any training round without touching the rest.</li>
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+ <li><b>The master holds everything; the modular datasets are copies.</b> Fix a row in the master and re-sync, never in a subset.
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+ Training reads the master at a pinned revision and selects by tag, so the subsets show what goes in; they are not a second copy that is trained on.</li>
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+ <li><b>Domain subsets overlap; task datasets don't.</b> A sentence can be both Yiddish and code-switching. General English excludes every task tag.</li>
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+ <li><b>Task datasets are out of training unless a run names them</b> (<code>prepare --include-tags &lt;tag&gt;</code>), except
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+ the defaults (<code>currency</code>).</li>
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+ <li><b>One speaker.</b> Subsets and training use Daniel's phone takes only; the master also has friends' takes and 2025 imports.</li>
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+ <li><b>Task subsets are created on demand</b>, after the first takes with that tag are pushed.</li>
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+ </ul></div>
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+
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+ <h2 id="blends">Blends: which fine-tune takes what</h2>
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+ <p class="small muted">Rows are datasets; columns are fine-tunes. <b>default</b>: in every round. <b>opt-in</b>: only when a run names
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+ it. <b>never</b>: excluded by design. The code-switching column is computed from <code>training/cs_lib.py</code>; the second
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+ fine-tune's column is the plan in <code>docs/objectives.md</code> and may change before it is built.</p>
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+ <div class="tbl"><table>
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+ <thead><tr><th>Dataset</th>
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+ <th>Code-switching fine-tune<span class="sub">next round's recipe; latest release v1.0 · Jachnun</span></th>
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+ <th>Tech + accent fine-tune<span class="sub">planned, no code-switching</span></th></tr></thead>
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+ <tbody><tr class="grp"><th colspan="3">Code-switching subsets</th></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/danielrosehill/English-Hebrew-Codeswitching-Sentences">English-Hebrew Code-Switching Sentences</a></th><td><span class="pill default">default</span><div class="note">CS share, ~70%</div></td><td><span class="pill never">never</span><div class="note">carries Hebrew</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Yiddish-Sentences">Yiddish Sentences</a></th><td><span class="pill default">default</span><div class="note">CS share, ~70%</div></td><td><span class="pill never">never</span><div class="note">carries Hebrew</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Jewish-Religious-Terminology-Sentences">Jewish Religious Terminology Sentences</a></th><td><span class="pill default">default</span><div class="note">CS share, ~70%</div></td><td><span class="pill never">never</span><div class="note">carries Hebrew</div></td></tr><tr class="grp"><th colspan="3">English subsets</th></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/danielrosehill/Tech-Sentences">Tech Sentences</a></th><td><span class="pill default">default</span><div class="note">own-English pool, ~20%, filled by priority</div></td><td><span class="pill default">default</span><div class="note">no Hebrew in its rule</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/danielrosehill/AI-Interactions-Sentences">AI Interactions Sentences</a></th><td><span class="pill default">default</span><div class="note">own-English pool, ~20%, filled by priority</div></td><td><span class="pill default">default</span><div class="note">no Hebrew in its rule</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/danielrosehill/Linux-Admin-Sentences">Linux Admin Sentences</a></th><td><span class="pill default">default</span><div class="note">own-English pool, ~20%, filled by priority</div></td><td><span class="pill default">default</span><div class="note">no Hebrew in its rule</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/danielrosehill/Smart-Home-Sentences">Smart Home Sentences</a></th><td><span class="pill default">default</span><div class="note">own-English pool, ~20%, filled by priority</div></td><td><span class="pill default">default</span><div class="note">no Hebrew in its rule</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/danielrosehill/Personal-Vocabulary-Sentences">Personal Vocabulary Sentences</a></th><td><span class="pill default">default</span><div class="note">own-English pool, ~20%, filled by priority</div></td><td><span class="pill default">default</span><div class="note">no Hebrew in its rule</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/danielrosehill/Homophone-Context-Sentences">Homophone Context Sentences</a></th><td><span class="pill default">default</span><div class="note">own-English pool, ~20%, filled by priority</div></td><td><span class="pill default">default</span><div class="note">no Hebrew in its rule</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/danielrosehill/General-English-Sentences">General English Sentences</a></th><td><span class="pill default">default</span><div class="note">own-English pool, ~20%, filled by priority</div></td><td><span class="pill default">default</span><div class="note">no Hebrew in its rule</div></td></tr><tr class="grp"><th colspan="3">Task datasets (one phenomenon each)</th></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Disfluent-Read-Sentences">Disfluent Read Sentences</a> <span class="badge exp">experimental</span> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags disfluency; formatting, out of scope</div></td><td><span class="pill opt-in">opt-in</span><div class="note">undecided</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Self-Correction-Sentences">Self-Correction Sentences</a> <span class="badge exp">experimental</span> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags self-corrections; formatting, out of scope</div></td><td><span class="pill opt-in">opt-in</span><div class="note">undecided</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Spoken-Aside-Sentences">Spoken Aside Sentences</a> <span class="badge exp">experimental</span> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags asides; formatting, out of scope</div></td><td><span class="pill opt-in">opt-in</span><div class="note">undecided</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Hard-ASR-Sentences">Hard ASR Sentences</a> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags hard-asr; meant for the second fine-tune</div></td><td><span class="pill default">default</span><div class="note">hard-for-ASR sentences</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Brand-Name-Sentences">Brand Name Sentences</a> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags brand-names; meant for the second fine-tune</div></td><td><span class="pill default">default</span><div class="note">names are its tech vocabulary</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Acronym-Sentences">Acronym Sentences</a> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags acronyms; meant for the second fine-tune</div></td><td><span class="pill default">default</span><div class="note">tech vocabulary</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Irish-English-Sentences">Irish English Sentences</a> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags irish-english; meant for the second fine-tune</div></td><td><span class="pill default">default</span><div class="note">accent</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Repeated-Word-Sentences">Repeated Word Sentences</a> <span class="badge exp">experimental</span> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags repetitions; formatting, out of scope</div></td><td><span class="pill opt-in">opt-in</span><div class="note">undecided</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Singing-Sentences">Singing Sentences</a> <span class="badge exp">experimental</span> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags singing; robustness data, no objective yet</div></td><td><span class="pill opt-in">opt-in</span><div class="note">undecided</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Weird-Voice-Sentences">Weird Voice Sentences</a> <span class="badge exp">experimental</span> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags weird-voices; robustness data, no objective yet</div></td><td><span class="pill opt-in">opt-in</span><div class="note">undecided</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Currency-Sentences">Currency Sentences</a> <span class="badge none">not created yet</span></th><td><span class="pill default">default</span><div class="note">in by default: joins the own-English pool</div></td><td><span class="pill default">default</span><div class="note">no Hebrew words</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Paragraph-Dictation-Passages">Paragraph Dictation Passages</a> <span class="badge exp">experimental</span> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags paragraphs; formatting, out of scope; approach undecided</div></td><td><span class="pill opt-in">opt-in</span><div class="note">undecided</div></td></tr><tr><th scope="row"><a href="https://huggingface.co/datasets/Hebrish/Expanded-Acronym-Sentences">Expanded Acronym Sentences</a> <span class="badge none">not created yet</span></th><td><span class="pill opt-in">opt-in</span><div class="note">--include-tags expanded-acronyms</div></td><td><span class="pill opt-in">opt-in</span><div class="note">undecided</div></td></tr><tr class="grp"><th colspan="3">Outside the master</th></tr><tr><th scope="row">LibriSpeech-PC replay <span class="badge">external</span></th><td><span class="pill default">default</span><div class="note">public replay, ~10%</div></td><td><span class="pill default">default</span><div class="note">public replay; Irish-accented replay suits it too</div></td></tr></tbody></table></div>
162
+ <p class="small muted" style="margin-top:8px">v1.0 · Jachnun was trained on 2026-10-19, before task datasets
163
+ existed; task-tag defaults apply from the next round. Rows tagged <code>names</code>, <code>numeric</code>, <code>email</code>,
164
+ <code>message</code> or Correct-mode corrections have no subset of their own but are in the master and in the own-English pool
165
+ (corrections and numbers first).</p>
166
+
167
+ <h2 id="master">Master dataset</h2>
168
+ <div class="card ds">
169
+ <div class="head"><h3><a href="https://huggingface.co/datasets/danielrosehill/Read-Aloud-Voice-Sentences">Read-Aloud Voice Sentences</a></h3><span class="size">5,558 rows · 620.7 min</span></div>
170
+ <p class="repo"><code>danielrosehill/Read-Aloud-Voice-Sentences</code></p>
171
+ <p class="badges"><span class="badge ok">on the Hub</span> <span class="badge">gated</span></p>
172
+ <p>Every take from the capture app lands here first: code-switching, English, tech, every task. It is the only dataset written
173
+ directly, and the one training reads. Columns and conventions are on its card.</p>
174
+ </div>
175
+
176
+ <h2 id="domain">Domain subsets</h2>
177
+ <p class="small muted">Copied from the master by a category rule. 10 of 23 modular datasets exist on the Hub.</p>
178
+ <div class="grid">
179
+ <article class="card ds" id="code_switch">
180
+ <div class="head"><h3><a href="https://huggingface.co/datasets/danielrosehill/English-Hebrew-Codeswitching-Sentences">English-Hebrew Code-Switching Sentences</a></h3><span class="size">3,643 rows · 415.2 min</span></div>
181
+ <p class="repo"><code>danielrosehill/English-Hebrew-Codeswitching-Sentences</code></p>
182
+ <p class="badges"><span class="badge ok">on the Hub</span> <span class="badge">gated</span></p>
183
+ <p>English sentences with everyday Hebrew words and Israeli names written in <b>Latin letters</b>, read aloud by one English speaker living in Israel. Hebrew words follow one canonical spelling convention, and Hebrew proper nouns are capitalised (Yerushalayim, Bituach Leumi).</p>
184
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>code_switch</code>.</p>
185
+ </article>
186
+ <article class="card ds" id="tech">
187
+ <div class="head"><h3><a href="https://huggingface.co/datasets/danielrosehill/Tech-Sentences">Tech Sentences</a></h3><span class="size">153 rows · 24.5 min</span></div>
188
+ <p class="repo"><code>danielrosehill/Tech-Sentences</code></p>
189
+ <p class="badges"><span class="badge ok">on the Hub</span> <span class="badge">public</span></p>
190
+ <p>Dictation-style English sentences dense in software, DevOps, cloud, self-hosting and AI vocabulary (product names, CLI tools, acronyms, model names), read aloud by one speaker. Terms use the vendor's own casing (GitHub, PostgreSQL, kubectl). Includes long-form dictation of instructions to AI coding agents. Sentences that also code-switch into Hebrew are <b>not</b> included (since 2026-10-19); they are in <a href="https://huggingface.co/datasets/danielrosehill/English-Hebrew-Codeswitching-Sentences">English-Hebrew-Codeswitching-Sentences</a>.</p>
191
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>tech</code> and not <code>code_switch</code>.</p><p class="meta">Sentence bank: <a href="https://huggingface.co/datasets/danielrosehill/AI-Tech-Vocabulary-Sentence-Bank">AI-Tech-Vocabulary-Sentence-Bank</a> (2,033 sentences)</p>
192
+ </article>
193
+ <article class="card ds" id="ai_interactions">
194
+ <div class="head"><h3><a href="https://huggingface.co/datasets/danielrosehill/AI-Interactions-Sentences">AI Interactions Sentences</a></h3><span class="size">0 rows</span></div>
195
+ <p class="repo"><code>danielrosehill/AI-Interactions-Sentences</code></p>
196
+ <p class="badges"><span class="badge ok">on the Hub</span> <span class="badge">public</span></p>
197
+ <p>Instructions dictated to AI coding agents (Claude Code, Codex and others) and the git and GitHub workflow around them, read aloud by one speaker: subagents, worktrees, compaction, plan mode, hooks, MCP servers, pull requests, rebases. Sentences to read: <a href="https://huggingface.co/datasets/danielrosehill/AI-Interactions-Sentence-Bank">AI-Interactions-Sentence-Bank</a>.</p>
198
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>ai_interactions</code> and not <code>code_switch</code>.</p><p class="meta">Sentence bank: <a href="https://huggingface.co/datasets/danielrosehill/AI-Interactions-Sentence-Bank">AI-Interactions-Sentence-Bank</a> (317 sentences)</p>
199
+ </article>
200
+ <article class="card ds" id="linux">
201
+ <div class="head"><h3><a href="https://huggingface.co/datasets/danielrosehill/Linux-Admin-Sentences">Linux Admin Sentences</a></h3><span class="size">1 row · 0.2 min</span></div>
202
+ <p class="repo"><code>danielrosehill/Linux-Admin-Sentences</code></p>
203
+ <p class="badges"><span class="badge ok">on the Hub</span> <span class="badge">public</span></p>
204
+ <p>Dictation-style English sentences about Linux desktop and server administration (KDE Plasma, GNOME, systemd, storage, networking, homelab), read aloud by one speaker. The focus is names that a speech model writes as an ordinary English word: Konsole (console), Okular (ocular), Thunar (thunder), LUKS (looks), Btrfs, GRUB. Sentences to read: <a href="https://huggingface.co/datasets/danielrosehill/Linux-Admin-Sentence-Bank">Linux-Admin-Sentence-Bank</a>.</p>
205
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>linux</code> and not <code>code_switch</code>.</p><p class="meta">Sentence bank: <a href="https://huggingface.co/datasets/danielrosehill/Linux-Admin-Sentence-Bank">Linux-Admin-Sentence-Bank</a> (341 sentences)</p>
206
+ </article>
207
+ <article class="card ds" id="smart_home">
208
+ <div class="head"><h3><a href="https://huggingface.co/datasets/danielrosehill/Smart-Home-Sentences">Smart Home Sentences</a></h3><span class="size">1 row · 0.1 min</span></div>
209
+ <p class="repo"><code>danielrosehill/Smart-Home-Sentences</code></p>
210
+ <p class="badges"><span class="badge ok">on the Hub</span> <span class="badge">public</span></p>
211
+ <p>Dictation-style English sentences about home automation (Home Assistant, Zigbee, Z-Wave, Matter, Thread, ESPHome, MQTT and device brands), read aloud by one speaker. Includes product names a speech model writes as an ordinary word (Matter, Thread, Hue, Nest, Assist). Sentences to read: <a href="https://huggingface.co/datasets/danielrosehill/Smart-Home-Sentence-Bank">Smart-Home-Sentence-Bank</a>.</p>
212
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>smart_home</code> and not <code>code_switch</code>.</p><p class="meta">Sentence bank: <a href="https://huggingface.co/datasets/danielrosehill/Smart-Home-Sentence-Bank">Smart-Home-Sentence-Bank</a> (270 sentences)</p>
213
+ </article>
214
+ <article class="card ds" id="personal">
215
+ <div class="head"><h3><a href="https://huggingface.co/datasets/danielrosehill/Personal-Vocabulary-Sentences">Personal Vocabulary Sentences</a></h3><span class="size">20 rows · 2.1 min</span></div>
216
+ <p class="repo"><code>danielrosehill/Personal-Vocabulary-Sentences</code></p>
217
+ <p class="badges"><span class="badge ok">on the Hub</span> <span class="badge">public</span></p>
218
+ <p>English sentences built around names a speech model gets wrong for one speaker: his own name, his company and his project names (Daniel Rosehill, DSR Holdings, Carrot Cake AI, the podcast My Weird Prompts and its hosts Corn and Herman Poppleberry), read aloud by that speaker. Sentences to read: <a href="https://huggingface.co/datasets/danielrosehill/Personal-Vocabulary-Sentence-Bank">Personal-Vocabulary-Sentence-Bank</a>.</p>
219
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>personal</code> and not <code>code_switch</code>.</p><p class="meta">Sentence bank: <a href="https://huggingface.co/datasets/danielrosehill/Personal-Vocabulary-Sentence-Bank">Personal-Vocabulary-Sentence-Bank</a> (83 sentences)</p>
220
+ </article>
221
+ <article class="card ds" id="homophone">
222
+ <div class="head"><h3><a href="https://huggingface.co/datasets/danielrosehill/Homophone-Context-Sentences">Homophone Context Sentences</a></h3><span class="size">27 rows · 2.8 min</span></div>
223
+ <p class="repo"><code>danielrosehill/Homophone-Context-Sentences</code></p>
224
+ <p class="badges"><span class="badge ok">on the Hub</span> <span class="badge">public</span></p>
225
+ <p>English sentences whose context settles which spelling of a homophone is meant (their/there, principal/principle, and 58 more sets), read aloud by one speaker. Sentences to read: <a href="https://huggingface.co/datasets/danielrosehill/Homophone-Context-Sentence-Bank">Homophone-Context-Sentence-Bank</a>.</p>
226
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>homophone</code> and not <code>code_switch</code>.</p><p class="meta">Sentence bank: <a href="https://huggingface.co/datasets/danielrosehill/Homophone-Context-Sentence-Bank">Homophone-Context-Sentence-Bank</a> (426 sentences)</p>
227
+ </article>
228
+ <article class="card ds" id="yiddish">
229
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Yiddish-Sentences">Yiddish Sentences</a></h3><span class="size">139 rows · 15.0 min</span></div>
230
+ <p class="repo"><code>Hebrish/Yiddish-Sentences</code></p>
231
+ <p class="badges"><span class="badge ok">on the Hub</span> <span class="badge">public</span></p>
232
+ <p>English sentences with Yiddish words written in <b>Latin letters</b> (shul, daven, cholent, mensch, freier, tachles), the way English speakers in Israel use them, read aloud by one speaker. Many of these words are also used in modern Hebrew. Yiddish counts as code-switching here, so every row is also in <a href="https://huggingface.co/datasets/danielrosehill/English-Hebrew-Codeswitching-Sentences">English-Hebrew-Codeswitching-Sentences</a>. The word list is <code>data/yiddish-terms.json</code> in the project repository.</p>
233
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>yiddish</code>.</p>
234
+ </article>
235
+ <article class="card ds" id="religious">
236
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Jewish-Religious-Terminology-Sentences">Jewish Religious Terminology Sentences</a></h3><span class="size">795 rows · 96.8 min</span></div>
237
+ <p class="repo"><code>Hebrish/Jewish-Religious-Terminology-Sentences</code></p>
238
+ <p class="badges"><span class="badge ok">on the Hub</span> <span class="badge">public</span></p>
239
+ <p>English sentences with Jewish religious vocabulary written in <b>Latin letters</b>: Shabbat and the festivals, prayer and the synagogue, Torah portions, Talmud and halacha, kashrut, and life-cycle events, read aloud by one speaker. Many words are recorded in both their <b>Modern Israeli</b> and <b>Ashkenazi</b> forms (Shabbat / Shabbos, Sukkot / Sukkos, brit / bris, Tosafot / Tosfos), and each row says which: <code>variant_groups</code> links the forms of one word, <code>variant_spelling</code> gives the tradition of the transcript's spelling and <code>hebrew_pronunciation</code> how it was read. Every row is also in <a href="https://huggingface.co/datasets/danielrosehill/English-Hebrew-Codeswitching-Sentences">English-Hebrew-Codeswitching-Sentences</a>. The word lists are <code>data/religious-terms.json</code> and <code>data/pronunciation-variants.json</code> in the project repository.</p>
240
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>religious</code>.</p>
241
+ </article>
242
+ <article class="card ds" id="general">
243
+ <div class="head"><h3><a href="https://huggingface.co/datasets/danielrosehill/General-English-Sentences">General English Sentences</a></h3><span class="size">385 rows · 42.3 min</span></div>
244
+ <p class="repo"><code>danielrosehill/General-English-Sentences</code></p>
245
+ <p class="badges"><span class="badge ok">on the Hub</span> <span class="badge">public</span></p>
246
+ <p>Plain, everyday English sentences (emails, messages, general prose and homophone pairs) read aloud by one speaker, with <b>no</b> Hebrew or Yiddish words, Israeli names or tech vocabulary. A small, general set for adapting a model to one speaker's voice and accent.</p>
247
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include none of <code>code_switch</code>, <code>tech</code>, <code>names</code>, <code>personal</code> and <code>yiddish</code>, and that are not from a task dataset (disfluency, self-corrections, asides, hard-asr, brand-names, acronyms, irish-english, repetitions, singing, weird-voices, currency, paragraphs, expanded-acronyms).</p>
248
+ </article>
249
+ </div>
250
+
251
+ <h2 id="tasks">Task datasets</h2>
252
+ <p class="small muted">One per capture-app task. The capture app writes <code>categories</code> = <code>[tag]</code>.</p>
253
+ <div class="grid">
254
+ <article class="card ds" id="disfluency">
255
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Disfluent-Read-Sentences">Disfluent Read Sentences</a></h3></div>
256
+ <p class="repo"><code>Hebrish/Disfluent-Read-Sentences</code></p>
257
+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge exp">experimental</span> <span class="badge">tag <code>disfluency</code></span></p>
258
+ <p>Experimental. Plain English sentences read <b>with deliberate hesitations</b> (uh, um, eh; repeated words have their own dataset since 2026-10-20) by one speaker. <code>text</code> is the clean sentence that was shown, the target for a model that drops fillers. <code>verbatim_text</code>, when filled in, is the same take with the fillers marked <code>{uh}</code>, <code>{um}</code>, <code>{eh}</code>.</p>
259
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>disfluency</code> (the capture app's task of that name).</p><p class="meta">Created on demand: <code>sync_subsets.py --only disfluency</code> after the first takes are pushed.</p>
260
+ </article>
261
+ <article class="card ds" id="self-corrections">
262
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Self-Correction-Sentences">Self-Correction Sentences</a></h3></div>
263
+ <p class="repo"><code>Hebrish/Self-Correction-Sentences</code></p>
264
+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge exp">experimental</span> <span class="badge">tag <code>self-corrections</code></span></p>
265
+ <p>One speaker correcting himself mid-sentence ("send it Tuesday, no, Wednesday"). <code>spoken_text</code> is the script that was read; <code>text</code> is the corrected sentence only, the transcript a dictation model should give.</p>
266
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>self-corrections</code> (the capture app's task of that name).</p><p class="meta">Prompts: 100 in <code>data/task-prompts/self-corrections.json</code></p><p class="meta">Created on demand: <code>sync_subsets.py --only self-corrections</code> after the first takes are pushed.</p>
267
+ </article>
268
+ <article class="card ds" id="asides">
269
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Spoken-Aside-Sentences">Spoken Aside Sentences</a></h3></div>
270
+ <p class="repo"><code>Hebrish/Spoken-Aside-Sentences</code></p>
271
+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge exp">experimental</span> <span class="badge">tag <code>asides</code></span></p>
272
+ <p>One speaker dictating a sentence and, partway through, saying something to someone in the room ("come in, Hannah"). <code>text</code> excludes the aside; <code>aside_text</code> and <code>aside_position</code> (start, middle, end) give it; <code>spoken_text</code> is the full script.</p>
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+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>asides</code> (the capture app's task of that name).</p><p class="meta">Prompts: 100 in <code>data/task-prompts/asides.json</code></p><p class="meta">Created on demand: <code>sync_subsets.py --only asides</code> after the first takes are pushed.</p>
274
+ </article>
275
+ <article class="card ds" id="hard-asr">
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+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Hard-ASR-Sentences">Hard ASR Sentences</a></h3></div>
277
+ <p class="repo"><code>Hebrish/Hard-ASR-Sentences</code></p>
278
+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge">tag <code>hard-asr</code></span></p>
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+ <p>English sentences speech recognition finds hard, read by one speaker: Harvard sentences (IEEE 1969, public domain), tongue twisters, homographs, garden-path sentences, rare words, dense numbers and units, unusual proper nouns. <code>prompt_kind</code> names the kind and <code>prompt_source</code> the origin of each sentence.</p>
280
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>hard-asr</code> (the capture app's task of that name).</p><p class="meta">Prompts: 100 in <code>data/task-prompts/hard-asr.json</code></p><p class="meta">Created on demand: <code>sync_subsets.py --only hard-asr</code> after the first takes are pushed.</p>
281
+ </article>
282
+ <article class="card ds" id="brand-names">
283
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Brand-Name-Sentences">Brand Name Sentences</a></h3></div>
284
+ <p class="repo"><code>Hebrish/Brand-Name-Sentences</code></p>
285
+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge">tag <code>brand-names</code></span></p>
286
+ <p>Natural sentences with company, product, AI model and open-source project names (Anthropic, OpenRouter, Amazon Bedrock, Qwen, Kubernetes, Nginx) read by one speaker, written in each name's official form. <code>target_terms</code> lists the names, main name first. <code>prompt_kind</code> is the brand category (ai-lab, ai-model, ai-platform, ai-dev-tool, ai-app, open-source, cloud, hardware, …) and <code>term_language</code> the origin language of the main name (english, coined, french, chinese, …; null on takes recorded before release 61).</p>
287
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>brand-names</code> (the capture app's task of that name).</p><p class="meta">Prompts: 378 in <code>data/task-prompts/brand-names.json</code></p><p class="meta">Created on demand: <code>sync_subsets.py --only brand-names</code> after the first takes are pushed.</p>
288
+ </article>
289
+ <article class="card ds" id="acronyms">
290
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Acronym-Sentences">Acronym Sentences</a></h3></div>
291
+ <p class="repo"><code>Hebrish/Acronym-Sentences</code></p>
292
+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge">tag <code>acronyms</code></span></p>
293
+ <p>Natural sentences with acronyms read by one speaker, both spelled out (API, SSH, HTTPS) and said as words (NASA, JSON, YAML), each in its standard written form. <code>prompt_kind</code> is letters, word or mixed.</p>
294
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>acronyms</code> (the capture app's task of that name).</p><p class="meta">Prompts: 97 in <code>data/task-prompts/acronyms.json</code></p><p class="meta">Created on demand: <code>sync_subsets.py --only acronyms</code> after the first takes are pushed.</p>
295
+ </article>
296
+ <article class="card ds" id="irish-english">
297
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Irish-English-Sentences">Irish English Sentences</a></h3></div>
298
+ <p class="repo"><code>Hebrish/Irish-English-Sentences</code></p>
299
+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge">tag <code>irish-english</code></span></p>
300
+ <p>Everyday sentences in Irish English read by one speaker born in Ireland: vocabulary (craic, grand, giving out, press, messages), grammar (I'm after doing it, so it is, ye), Irish-language words used in English (Taoiseach, Dáil, Gardaí, sláinte) and Irish names and places (Siobhán, Tadhg, Dún Laoghaire), in standard Irish spelling with fadas. <code>prompt_kind</code> is vocab, grammar, irish-language or name.</p>
301
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>irish-english</code> (the capture app's task of that name).</p><p class="meta">Prompts: 300 in <code>data/task-prompts/irish-english.json</code></p><p class="meta">Created on demand: <code>sync_subsets.py --only irish-english</code> after the first takes are pushed.</p>
302
+ </article>
303
+ <article class="card ds" id="repetitions">
304
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Repeated-Word-Sentences">Repeated Word Sentences</a></h3></div>
305
+ <p class="repo"><code>Hebrish/Repeated-Word-Sentences</code></p>
306
+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge exp">experimental</span> <span class="badge">tag <code>repetitions</code></span></p>
307
+ <p>Experimental. Plain English sentences read by one speaker <b>repeating a word or phrase now and then</b> ("the the report", "I think I think"), with no fillers. <code>text</code> is the clean sentence that was shown. <code>verbatim_text</code>, when filled in, is the take as said, repeats written out.</p>
308
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>repetitions</code> (the capture app's task of that name).</p><p class="meta">Created on demand: <code>sync_subsets.py --only repetitions</code> after the first takes are pushed.</p>
309
+ </article>
310
+ <article class="card ds" id="singing">
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+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Singing-Sentences">Singing Sentences</a></h3></div>
312
+ <p class="repo"><code>Hebrish/Singing-Sentences</code></p>
313
+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge exp">experimental</span> <span class="badge">tag <code>singing</code></span></p>
314
+ <p>Experimental. Short plain English sentences <b>sung</b> by one speaker, to any tune. <code>text</code> is the sentence as written, the transcript a robust recognizer should still give.</p>
315
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>singing</code> (the capture app's task of that name).</p><p class="meta">Created on demand: <code>sync_subsets.py --only singing</code> after the first takes are pushed.</p>
316
+ </article>
317
+ <article class="card ds" id="weird-voices">
318
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Weird-Voice-Sentences">Weird Voice Sentences</a></h3></div>
319
+ <p class="repo"><code>Hebrish/Weird-Voice-Sentences</code></p>
320
+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge exp">experimental</span> <span class="badge">tag <code>weird-voices</code></span></p>
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+ <p>Experimental. Plain English sentences read by one speaker <b>in a put-on voice</b> (very deep, squeaky, robot, cartoon, mock accent, mumbly). <code>text</code> is the sentence as written.</p>
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+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>weird-voices</code> (the capture app's task of that name).</p><p class="meta">Created on demand: <code>sync_subsets.py --only weird-voices</code> after the first takes are pushed.</p>
323
+ </article>
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+ <article class="card ds" id="currency">
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+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Currency-Sentences">Currency Sentences</a></h3></div>
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+ <p class="repo"><code>Hebrish/Currency-Sentences</code></p>
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+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge">tag <code>currency</code></span></p>
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+ <p>One speaker saying amounts of money the natural way ("50 shekels", "a dollar fifty", "6 pounds 50"). <code>spoken_text</code> is the script that was read; <code>text</code> is the written form a dictation model should give: <code>50 NIS</code>, <code>$1.50</code>, <code>€18</code>, <code>£6.50</code> (NIS rather than ₪, which the Parakeet v2 tokenizer cannot emit). <code>prompt_kind</code> is the currency: ILS, USD, EUR, GBP, mixed, other (yen, francs: written as words), or word (the currency named without an amount, which stays a word).</p>
329
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>currency</code> (the capture app's task of that name).</p><p class="meta">Prompts: 104 in <code>data/task-prompts/currency.json</code></p><p class="meta">Created on demand: <code>sync_subsets.py --only currency</code> after the first takes are pushed.</p>
330
+ </article>
331
+ <article class="card ds" id="paragraphs">
332
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Paragraph-Dictation-Passages">Paragraph Dictation Passages</a></h3></div>
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+ <p class="repo"><code>Hebrish/Paragraph-Dictation-Passages</code></p>
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+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge exp">experimental</span> <span class="badge">tag <code>paragraphs</code></span></p>
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+ <p>Experimental. Passages of 2 to 4 paragraphs (emails, prompts to AI coding agents, voice notes, messages, meeting notes, blog drafts) dictated in one take by one speaker, pausing naturally between paragraphs. <code>text</code> is the passage on one line, the transcript Parakeet v2 can emit (its tokenizer has no newline). <code>paragraph_text</code> is the same passage with the paragraph breaks as blank lines, the label for a model that puts the breaks back. <code>prompt_kind</code> is the register.</p>
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+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>paragraphs</code> (the capture app's task of that name).</p><p class="meta">Prompts: 49 in <code>data/task-prompts/paragraphs.json</code></p><p class="meta">Created on demand: <code>sync_subsets.py --only paragraphs</code> after the first takes are pushed.</p>
337
+ </article>
338
+ <article class="card ds" id="expanded-acronyms">
339
+ <div class="head"><h3><a href="https://huggingface.co/datasets/Hebrish/Expanded-Acronym-Sentences">Expanded Acronym Sentences</a></h3></div>
340
+ <p class="repo"><code>Hebrish/Expanded-Acronym-Sentences</code></p>
341
+ <p class="badges"><span class="badge none">not created yet</span> <span class="badge">tag <code>expanded-acronyms</code></span></p>
342
+ <p>Chat and business shorthand said as letters by one speaker ("by E O D", "F Y I", "I M O") with the transcript writing it out in full ("by end of day", "For your information", "In my opinion"). <code>spoken_text</code> is the script with the abbreviation; <code>text</code> is the expanded target. 16 abbreviations (EOD, EOW, COB, FYI, BTW, FWIW, ICYMI, IMO, IMHO, AFAIK, IIRC, TBD, TBC, WFH, OOO, LMK), 6 sentences each. None of them appears as letters in the companion Acronym-Sentences set, so the two never contradict. <code>prompt_kind</code> is time, aside, opinion, status or request.</p>
343
+ <p class="meta"><b>Match rule:</b> Rows whose <code>categories</code> include <code>expanded-acronyms</code> (the capture app's task of that name).</p><p class="meta">Prompts: 96 in <code>data/task-prompts/expanded-acronyms.json</code></p><p class="meta">Created on demand: <code>sync_subsets.py --only expanded-acronyms</code> after the first takes are pushed.</p>
344
+ </article>
345
+ </div>
346
+
347
+ <h2 id="banks">Sentence banks (text only)</h2>
348
+ <p class="small muted">What is read for the domain subsets, with per-row provenance. Task prompts are self-written or public-domain
349
+ files in the project repository instead.</p>
350
+ <div class="tbl"><table>
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+ <thead><tr><th>Sentence bank</th><th class="n">Sentences</th><th>Audio subset it feeds</th></tr></thead>
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+ <tbody><tr><td><a href="https://huggingface.co/datasets/danielrosehill/AI-Tech-Vocabulary-Sentence-Bank">AI-Tech-Vocabulary-Sentence-Bank</a> </td><td class="n">2,033</td><td>Tech-Sentences</td></tr><tr><td><a href="https://huggingface.co/datasets/danielrosehill/AI-Interactions-Sentence-Bank">AI-Interactions-Sentence-Bank</a> </td><td class="n">317</td><td>AI-Interactions-Sentences</td></tr><tr><td><a href="https://huggingface.co/datasets/danielrosehill/Linux-Admin-Sentence-Bank">Linux-Admin-Sentence-Bank</a> </td><td class="n">341</td><td>Linux-Admin-Sentences</td></tr><tr><td><a href="https://huggingface.co/datasets/danielrosehill/Smart-Home-Sentence-Bank">Smart-Home-Sentence-Bank</a> </td><td class="n">270</td><td>Smart-Home-Sentences</td></tr><tr><td><a href="https://huggingface.co/datasets/danielrosehill/Personal-Vocabulary-Sentence-Bank">Personal-Vocabulary-Sentence-Bank</a> </td><td class="n">83</td><td>Personal-Vocabulary-Sentences</td></tr><tr><td><a href="https://huggingface.co/datasets/danielrosehill/Homophone-Context-Sentence-Bank">Homophone-Context-Sentence-Bank</a> </td><td class="n">426</td><td>Homophone-Context-Sentences</td></tr></tbody></table></div>
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+
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+ </div></main>
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+ <footer><div class="wrap">
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+ Generated 2026-10-20 by <code>scripts/build_datasets_space.py</code> from the project's registries
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+ (master revision <code>99db413e0f84</code>); do not edit by hand.
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+ All datasets: <a href="https://huggingface.co/collections/Hebrish/parakeet-v2-fine-tune-training-data-6ad63c739d300685a62edb3e">training-data collection</a> · <a href="https://huggingface.co/spaces/Hebrish/English-Hebrew-Codeswitching-Parakeet">project hub</a> ·
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+ <a href="https://hebrish.app/datasets">hebrish.app/datasets</a>.
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+ </div></footer>
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+ </body>
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  </html>