Card: standalone, no gate (public)

README.md

@@ -18,62 +18,19 @@ tags:
 datasets:
 - danielrosehill/Read-Aloud-Voice-Sentences
 - openslr/librispeech_asr
-extra_gated_prompt: "Access to this model is by request and is governed by the Hebrish terms of use (https://hebrish.app/terms). Non-commercial use is free; commercial use needs a commercial licence from access@hebrish.app."
-extra_gated_fields:
-  Name: text
-  Organisation: text
-  Intended use: text
-  "I agree to use this model under the Hebrish terms of use (non-commercial unless I hold a commercial licence)": checkbox
 ---
 
 # Fritter v1.0: Parakeet TDT 0.6B v2 for English dictation, no code-switching
 
-A personal speech-recognition fine-tune for **English dictation without code-switching**: tech vocabulary (model and product names, CLI and cloud terms), brand names, acronyms, numbers and currency in digits, Irish-English phrasing, fast and slow speech, whispering, and everyday email and message dictation, all in one speaker's voice. It is the successor of Boxty v1.0 (`Hebrish/Parakeet-TDT-0.6B-v2-Tech-And-Accent`), retrained from stock on more than twice the data.
+A personal speech-recognition fine-tune for **English dictation without code-switching**: tech vocabulary (model and product names, CLI and cloud terms), brand names, acronyms, numbers and currency in digits, Irish-English phrasing, fast and slow speech, whispering, and everyday email and message dictation, all in one speaker's voice.
 
-**Name.** Each fine-tune line has a food code name. **Fritter** (formerly Boxty, renamed 2026-10-22) is the no-code-switching line; the training run id is `r11-general`.
+**Name.** Fritter is the code name of this model line; later retrains keep it and bump the version. The training run id is `r11-general`.
 
 A full fine-tune of [`nvidia/parakeet-tdt-0.6b-v2`](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2) on one
 speaker's read-aloud phone recordings (an Irish English speaker living in Israel), run `r11-general`, trained
 2026-10-27. Output keeps v2's casing and punctuation; numbers are written in digits.
 
-Sibling models from the same data revision: [Sabich, kitchen sink, everything](https://huggingface.co/Hebrish/Sabich) · [Jachnun, English-Hebrew code-switching](https://huggingface.co/Hebrish/Jachnun).
-
-**Access and terms of use.** Governed by the [Hebrish terms of use](https://hebrish.app/terms); access is by
-request (button on this page, or [access@hebrish.app](mailto:access@hebrish.app)). Non-commercial
-use is free; commercial use needs a commercial licence.
-
-<!-- hebrish-listen:begin -->
-## Listen: what Hebrish is and how it's made
-
-A spoken introduction to the project by the person who makes it: why Hebrish exists, why stock
-speech-to-text gets Hebrew words wrong, how the fine-tunes are trained, the model lines (Sabich,
-Jachnun, Shakshuka, Meorav), how the training data is recorded, and the plans for the apps.
-
-| | |
-| --- | --- |
-| Listen | [hebrish.app/updates](https://hebrish.app/updates): player, chapters, edited transcript, MP3 download |
-| Author | Daniel Rosehill |
-| Recorded | 2026-10-22 |
-| Runtime | 1:07:03 |
-
-Chapters (each opens the player at that point):
-[0:00](https://hebrish.app/updates#t=0) Why this voice note ·
-[1:36](https://hebrish.app/updates#t=96) Coming to speech to text ·
-[6:36](https://hebrish.app/updates#t=396) Why Hebrew words go wrong ·
-[9:23](https://hebrish.app/updates#t=563) How the fine-tune works ·
-[12:27](https://hebrish.app/updates#t=747) Why Parakeet, and the model plan ·
-[17:44](https://hebrish.app/updates#t=1064) Disfluencies, shekels, paragraphs ·
-[25:40](https://hebrish.app/updates#t=1540) Tech vocabulary and Meorav ·
-[29:07](https://hebrish.app/updates#t=1747) The kitchen sink: Sabich and Jachnun ·
-[33:35](https://hebrish.app/updates#t=2015) Licensing, accents, noisy recordings ·
-[39:10](https://hebrish.app/updates#t=2350) One recorder for ASR and TTS ·
-[41:04](https://hebrish.app/updates#t=2464) Desktop shortcuts and the macro pad ·
-[43:34](https://hebrish.app/updates#t=2614) Android: Bluetooth and one button ·
-[45:35](https://hebrish.app/updates#t=2735) Streaming and Shakshuka ·
-[50:28](https://hebrish.app/updates#t=3028) Long-form transcription ·
-[56:00](https://hebrish.app/updates#t=3360) Output modes ·
-[63:01](https://hebrish.app/updates#t=3781) Report a word, and more voices
-<!-- hebrish-listen:end -->
+**Licence and access.** Public, CC-BY-NC-4.0 (non-commercial). A personal project by Daniel Rosehill; not affiliated with NVIDIA.
 
 ## Results
 
@@ -96,58 +53,6 @@ Source: [`danielrosehill/Read-Aloud-Voice-Sentences`](https://huggingface.co/dat
   plus 35.6 min of LibriSpeech-PC replay (cased, punctuated; CC-BY-4.0) = 391.0 min, 3,848 clips.
 - Validation: 1 in 16 of the speaker's train takes by id hash, used only to pick the checkpoint.
 
-<!-- training-categories:begin -->
-## Data categories
-
-Every category in the training data, what it is for, one real prompt, and which model line trains on it (Sabich = everything blend, Jachnun = code-switching, Fritter = everything except code-switching, formerly Boxty). Generated by `scripts/build_training_categories.py` in the training repo.
-
-| Category | What it trains | Example (said → written) | Sabich | Jachnun | Fritter |
-|---|---|---|---|---|---|
-| Code-switching | Hebrew words inside English sentences, written in the canonical romanization (the core objective). | “Do you want amba on your sabich or just tahini?” | ✓ | ✓ | – |
-| Israeli names and places | Israeli and Jewish first names, surnames and place names. | “Hi Omri, the tiyul to Ein Gedi is still on for Saturday morning if you want to join us.” | ✓ | ✓ | – |
-| Yiddish | Yiddish words in English; always also code-switching. | “These rugelach are fresh and absolutely delicious.” | ✓ | ✓ | – |
-| Jewish religious terms | Jewish religious and observance vocabulary; always also code-switching. | “Where exactly did the Beis Hamikdash stand relative to the Old City walls?” | ✓ | ✓ | – |
-| Plain English | Everyday English, kept so the model does not forget ordinary speech. | “Do we have any rice left, or should I make pasta for dinner tonight instead?” | ✓ | ✓ (English share) | ✓ |
-| Dictated email | Email register: greetings, requests, sign-offs. | “Hi Dana, just following up on my email from last week, did you get a chance to look at it?” | ✓ | ✓ (English share) | ✓ |
-| Messages and notes | WhatsApp messages and notes to self. | “Hey, did the money for the cinema tickets ever come through? No rush, just checking because I have to pay them back.” | ✓ | ✓ (English share) | ✓ |
-| Homophones | Sentences whose context settles a homophone (their / there, steak / stake). | “Could we hire a van for Saturday instead of borrowing Pete's?” | ✓ | ✓ (English share) | ✓ |
-| Numbers in digits | Numbers written in digits, not words (DeepSeek 4.1, 45 seconds). | “The battery dropped from 80% to 15% in under three hours.” | ✓ | ✓ (English share) | ✓ |
-| Personal vocabulary | Daniel's own names, companies and project names. | “Daniel Rosehill writes the prompts, and Corn and Herman turn them into an episode.” | ✓ | ✓ (English share) | ✓ |
-| Tech vocabulary | AI and software terms, product names, commands. | “A GitHub Gist is still the fastest way to share a one-off shell script with someone.” | ✓ | ✓ (English share) | ✓ |
-| Talking to AI tools | Prompts and instructions dictated to AI assistants. | “Turn on flash attention so the long recordings fit in memory.” | ✓ | ✓ (English share) | ✓ |
-| Linux admin | Linux commands, tools and system administration. | “Run a traceroute to the VPS from the home server and see where the latency jumps.” | ✓ | ✓ (English share) | ✓ |
-| Smart home | Home Assistant and smart-home devices and commands. | “SmartThings was my very first hub, before I found Home Assistant.” | ✓ | ✓ (English share) | ✓ |
-| Long dictation | Paragraph-length dictation, 50 to 110 words in one take. | “Quick update on the server situation. The WhatsApp bot stopped replying last night because uvicorn crashed after I updated Ubuntu. …” | ✓ | ✓ (English share) | ✓ |
-| Field corrections | Free speech the stock model got wrong, corrected by hand (Correct mode). |  | ✓ | ✓ (English share) | ✓ |
-| Currency | Spoken amounts written as typed: fifty shekels becomes 50 NIS, twenty dollars $20. | The license is 99 dollars once, no subscription. → “The license is $99 once, no subscription.” | ✓ | ✓ (English share) | ✓ |
-| Numbers (not money) | Temperatures, decimals, percentages, units, times, dates and ordinals in digits. | It was minus two degrees Celsius in Berlin this morning. → “It was -2 degrees Celsius in Berlin this morning.” | ✓ | – | ✓ |
-| Web addresses | Addresses said aloud (dot, slash, colon) written as typed. | The meeting notes are at example dot com slash notes slash october dash twenty. → “The meeting notes are at example.com/notes/october-20.” | ✓ | – | ✓ |
-| Spelling out | Letters spelled aloud written as the word they spell. | My surname is B R E N N A N. → “My surname is Brennan.” | ✓ | – | ✓ |
-| Acronyms | Letter-by-letter acronyms (API, SSH) and word acronyms (NASA, JSON) in their standard form. | “The ROI on that campaign was higher than anyone expected.” | ✓ | – | ✓ |
-| Expanded acronyms | Chat and business shorthand said as letters, written out (EOD becomes end of day). | The documentation is the weakest part of the project, IMO. → “The documentation is the weakest part of the project, in my opinion.” | ✓ | – | ✓ |
-| Brand and product names | Company, product, AI model and open-source names in their official spelling. | “I asked Claude to review the pull request, and it caught a typo in the config.” | ✓ | – | ✓ |
-| Procurement | Incoterms, POs, RFQs, lead times and payment terms. | “Ask them if they'll lower the MOQ for the first order and treat it as a trial.” | ✓ | – | ✓ |
-| Idioms | Idioms ASR tends to spell as a sound-alike (free rein, toe the line). | “We gave the contractor free rein on the kitchen layout, and honestly it turned out better than my plan.” | ✓ | – | ✓ |
-| Latin phrases | Latin phrases used in English (de facto, ad hominem, de minimis) in their standard spelling: lowercase, no hyphen. | “We ended up with a de facto ban on parking outside the school after the council's new notice.” | ✓ | – | ✓ |
-| Short fragments | One- to six-word replies, notes, list items and headings (Parakeet v2 is weakest on incomplete sentences). |  | ✓ | – | ✓ |
-| Names and figures | Sentences packed with names, amounts, percentages and dates, numbers in digits (Earnings-22 style). |  | ✓ | – | ✓ |
-| Spoken punctuation | Dictated comma, full stop, question mark and colon written as the mark; controls keep the words. |  | ✓ | – | ✓ |
-| Hard sentences | Harvard sentences, tongue twisters, homographs, garden paths, rare words. | “The birch canoe slid on the smooth planks.” | ✓ | – | ✓ |
-| Irish English | Irish English words, grammar and Irish-language names with fadas. | “The new lad at work is deadly, he sorted out the printer in 5 minutes.” | ✓ | – | ✓ |
-| Disfluency reduction *(experimental)* | Read with uh, um and eh; the transcript stays clean. | Read with hesitations: uh, um, eh. → “Losing your grandad must be so hard, I'm really sorry, if you want to talk or just sit together for a bit, I'm around all week.” | ✓ | – | ✓ |
-| Repeated words *(experimental)* | Words repeated the way people restart; the transcript stays clean. | Read repeating a word now and then (the the report). → “Honestly, I never properly thanked her for helping us last week, and it's bugging me now.” | ✓ | – | ✓ |
-| False starts *(experimental)* | Abandoned starts and doubled words; the transcript drops them. | It's not that I — I just don't think we have time for it this week. → “I just don't think we have time for it this week.” | ✓ | – | ✓ |
-| Self-corrections *(experimental)* | A spoken correction (Tuesday, no, Wednesday); the transcript keeps only the corrected version. | Leave the box by the front door, no, by the back door, please. → “Leave the box by the back door, please.” | ✓ | – | ✓ |
-| Asides *(experimental)* | A remark to someone else in the room mid-dictation; it stays out of the transcript. | We need to buy a new washing machine — Ezra, not now, sweetie — before the old one breaks down completely, ideally under $600. → “We need to buy a new washing machine before the old one breaks down completely, ideally under $600.” | ✓ | – | ✓ |
-| Paragraphs *(experimental)* | Whole passages in one take, for the separate paragraph model; never in Hebrish training. | “Things to sort out with the internet this week. The router keeps dropping the connection for a minute or so in the evenings. …” | – | – | – |
-| Whispered *(experimental)* | Whispered speech (robustness). | Whispered. → “Honestly I thought the flat looked lovely but the kitchen was tiny, do you think we could live with that?” | ✓ | – | ✓ |
-| Singing *(experimental)* | Sung sentences (robustness). | Sung, any tune. → “Did your cousin ever send you the recipe for that lamb stew or do I need to ask her again?” | ✓ | – | ✓ |
-| Weird voices *(experimental)* | Put-on voices: deep, squeaky, robot, accents (robustness). | Read in a put-on voice. → “Do I need to take the parcel back to the post office or can they collect it from the door?” | – | – | – |
-| Name cipher (PII test) *(experimental)* | Experiment: real names read, swapped by rule in the transcript (Hannah becomes Sarah). | Can you tell Daniel the meeting moved to 3 PM? → “Can you tell Dave the meeting moved to 3 PM?” | – | – | – |
-
-✓ = in the training recipe. “English share” = competes for Jachnun's 30% monolingual-English share. Experimental sets are recorded but off in the capture app by default. This is the current recipe; a published run's exact composition is in its blend file.
-<!-- training-categories:end -->
-
 ## Training
 
 NeMo 2.7.3 on one L40S (Modal), bf16. AdamW, lr 3e-05 (cosine, 10% warmup), 20 epochs,
@@ -173,9 +78,8 @@ Q4_K_M and F16.
 - The test sets are small (Hebrew-free English is 319 clips), so differences under about
   one WER point are within noise.
 - Not trained on English-Hebrew code-switching: romanized Hebrew words come out as stock v2 writes them.
-- Not benchmarked at release.
 - Disfluent takes were trained on with clean targets, so the model may drop some fillers and repeated words.
 
 ## Licence
 
-CC-BY-4.0, from the base model's licence, with use governed by the Hebrish terms above. Base model by NVIDIA.
+CC-BY-NC-4.0. The base model, `nvidia/parakeet-tdt-0.6b-v2`, is CC-BY-4.0, by NVIDIA.