{
 "schema_version": 1,
 "generated": "2026-10-26",
 "id": "sahilmahendrakar-paradee--study-distillation-of-a-tts-model",
 "repo": "sahilmahendrakar/paradee",
 "repo_url": "https://github.com/sahilmahendrakar/paradee",
 "repo_slug": "sahilmahendrakar-paradee",
 "title": "Study how a TTS model was distilled",
 "page_title": "How to study how a TTS model was distilled with Paradee",
 "summary": "Read the paper and training code to reproduce a teacher-student speech model on one laptop.",
 "who": "A researcher or student who wants to learn how a large TTS model was compressed into a small one.",
 "problem": "Distillation steps for speech models are rarely laid out end to end with code that runs on a single machine.",
 "outcome": "A working understanding of the corpus, text-side training, decoder training and assembly steps, plus a local run of one stage.",
 "steps": [
  "Read the paper at arxiv.org/abs/2610.06817 and the How it works section of the README.",
  "Open the `training/` folder and the `training/data/` sentences, which come from WikiText-103.",
  "Review how the text side, decoder and assembly steps are split, noting that the decoder is trained with spectrogram losses and then adversarial training.",
  "Run one stage from `training/` on your laptop, since the README says the code runs on one laptop."
 ],
 "agent_prompt": "Clone sahilmahendrakar/paradee, read the How it works section and the training/ folder, then summarize the four distillation steps and the parameter split for the text side and decoder. Expect a short summary with the 4.23M and 3.65M parameter counts. Verify that every number you report appears in the README or paper before finalizing.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "2 hours",
 "basis": "readme",
 "caveat": "The paper describes version 1.0 and the 1.1 decoder head is only summarized in the README, so the code for 1.1 may differ from the paper's method.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "Python with pip installed",
  "Internet access for the first run, to download the 8.5 MB model and misaki's English dictionary data",
  "Audio output support (soundfile is used in the README example)"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.7,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/sahilmahendrakar-paradee/study-distillation-of-a-tts-model/",
 "md": "https://dedupedai.com/use-cases/sahilmahendrakar-paradee/study-distillation-of-a-tts-model/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/sahilmahendrakar-paradee--study-distillation-of-a-tts-model.json",
 "api_version": "v1"
}