README.md

---
license: cc-by-4.0
language: [en, he]
pretty_name: Hebrish ASR Error List
tags: [asr, speech-recognition, code-switching, error-analysis, hebrish]
configs:
- config_name: words
  data_files: words.jsonl
  default: true
- config_name: occurrences
  data_files: occurrences.jsonl
- config_name: release_tests
  data_files: release_tests.jsonl
---

# Hebrish ASR Error List

The words the Hebrish speech-to-text fine-tunes still get wrong in real dictation, what they were
heard as, and how far targeted retraining on each one has got.

The errors come from one speaker's own dictation with the Hebrish desktop app (English with Hebrew
code-switching, Jerusalem). Each word here is fed back into training as a **priority word**: the
recording app serves sentences with it first until there are enough takes of it. No transcript text,
audio or other content of the dictations is published, only the word, the wrong forms and the model.

- 170 words, 303 occurrences. Last review: 2026-10-29.
- `models` / `model_line`: the Hebrish model that produced the error (Sabich, Shakshuka, …, with
  version); `model_rev` is the exact weights' revision, recorded from app 0.7.3. Errors from before
  the app recorded the model have none.
- `source: reported` means the speaker reported the error directly; it is not in the reviewed history.
- `takes` / `target`: recordings of the word made for retraining so far, against the number wanted.
  `status`: `training` (under target), `trained` (target reached; a later error means it still fails).
- `active`: the word is still being recorded for retraining (takes under target); it switches off by
  itself when the target is reached. `priority`: `high` words are recorded before `normal` ones.
- `source: manual` means the word was added by hand, not from a reviewed error, so it has no occurrences.

Active words first, high priority first among them.

| Word | Heard as | Occurrences | Takes / target | Active priority |
|---|---|---|---|---|
| arXiv | arcs of, Artives, arcsiv, argsiv, Clike | 6 | 7 / 20 | high |
| Konsole | console | 4 | 10 / 20 | high |
| abdominophrenic dyssynergia | abominophrenic dysinurgia | 1 | 6 / 20 | high |
| accent transfer | Axon Transfer | 1 | 7 / 20 | high |
| base | bass | 1 | 7 / 20 | high |
| code-switching | co-switching | 1 | 7 / 20 | high |
| DHL | HL | 1 | 7 / 20 | high |
| dyspepsia | dyspepsy | 1 | 6 / 20 | high |
| feed | fee | 1 | 6 / 20 | high |
| fluticasone | Fluticazone | 1 | 7 / 20 | high |
| hunches | punches | 1 | 6 / 20 | high |
| lifelike | lifly | 1 | 6 / 20 | high |
| OTC | ODC | 1 | 6 / 20 | high |
| pairs | pears | 1 | 7 / 20 | high |
| potentially | prudentially | 1 | 6 / 20 | high |
| re-architect | re-arcolate | 1 | 6 / 20 | high |
| Relvar | Ralvar | 1 | 7 / 20 | high |
| salbutamol | Samputumal | 1 | 5 / 20 | high |
| sans-serif | Sanserif | 1 | 6 / 20 | high |
| serif | Sara | 1 | 6 / 20 | high |
| Singulair | Singular | 1 | 7 / 20 | high |
| spacer | Spacerin | 1 | 7 / 20 | high |
| text | test | 1 | 6 / 20 | high |
| Ventolin | Ventilin | 1 | 7 / 20 | high |
| Vercel | inversel | 1 | 7 / 20 | high |
| My Weird Prompts | MyWord Prompts, My WordPrompts, My Word Promise, mywordprompt, My Word prompts | 7 | 17 / 20 | normal |
| Typst | TyPst, TIPS-type, tips toypt, TyPstip, tipst, Tips | 6 | 12 / 20 | normal |
| CLAUDE.md | userlevelclaw.md, 4 MD, Claude NMD, FlawdMD | 4 | 12 / 20 | normal |
| Easy Effects | EasyFX | 4 | 8 / 20 | normal |
| Infisical | Infiniscal, Infinisco, Inphysical, physical | 4 | 11 / 20 | normal |
| AdGuard | Adgard, Adguard, ad? guard | 3 | 0 / 20 | normal |
| disfluency | disluency, Dispuency, fluencies | 3 | 12 / 20 | normal |
| mic | Mike | 3 | 8 / 20 | normal |
| TEN VAD | Tenvad, 10vAD, 10VAD | 3 | 0 / 20 | normal |
| Boxty | E box d, Boxy | 2 | 3 / 20 | normal |
| Composio | Compi Posio, Compose | 2 | 7 / 20 | normal |
| OnePlus Nord 3 5G | OnePlus Nord 3.5G, OnePlus Nord 35G | 2 | 7 / 20 | normal |
| Parakeet | Power Key, PowerKey | 2 | 10 / 20 | normal |
| RecForge II | Recforged, Rec Fortage | 2 | 0 / 20 | normal |
| sqlite-vec | SQLite Fex, SQLite Bec | 2 | 0 / 20 | normal |
| auto gain | auto game | 1 | 7 / 20 | normal |
| capybara | kapybara | 1 | 0 / 20 | normal |
| convention | invention | 1 | 7 / 20 | normal |
| cutlery | cuddlery | 1 | 7 / 20 | normal |
| Debian | Debin | 1 | 10 / 20 | normal |
| Flic | Flick | 1 | 0 / 20 | normal |
| IBM Plex Sans | IBM Plexans | 1 | 7 / 20 | normal |
| Kokoro | Kikoro | 1 | 1 / 20 | normal |
| manga | magma | 1 | 0 / 20 | normal |
| MCP Hub | MCB Hub | 1 | 6 / 20 | normal |
| OTP | OTB | 1 | 8 / 20 | normal |
| STT | SST | 1 | 10 / 20 | normal |
| tech vocab | type vocab | 1 | 7 / 20 | normal |
| ThinkPad | ThinkPaddy | 1 | 7 / 20 | normal |
| Truecaller | TrueColer | 1 | 0 / 20 | normal |
| Twilio | Trilio | 1 | 1 / 20 | normal |
| Yealink | Ya Link | 1 | 0 / 20 | normal |
| hasbara |  | 0 | 0 / 20 | normal |
| Silero |  | 0 | 0 / 20 | normal |
| Claude Code | clawed code, clawed, claud, clawed coat, claw code, Core Code, claudcode, claud Code, the claud, clause claud code | 15 | 13 / 6 |  |
| LoRA | Lores, Loas, Laura, lores, Loa, LoRa, lore, lures | 15 | 8 / 6 |  |
| ROCm | ROCM, Rockham, Rockin, RCM, Rock'em | 15 | 12 / 11 |  |
| Corn | Coren, Cornak's, Eminent Corn, Corner, Korn, Corin | 6 | 5 / 5 |  |
| Hugging Face | Hooking Face, hugging face, hooking face, Hogging Face, hogging face | 6 | 11 / 11 |  |
| Hebrish | HeBrish, Hebris, keybrush, Hebrew Shop, Hebrew shtal | 5 | 13 / 11 |  |
| 8BitDo | 8-bit do, four-bit do | 4 | 9 / 9 |  |
| NocoDB | No CodeDB, Nocode, NokoDB, Noko DB | 4 | 6 / 6 |  |
| Sabich | Tzabich, Savich, sabich | 4 | 22 / 11 |  |
| skill | scale, kills | 4 | 5 / 5 |  |
| Cloudflare | cloud fair, Cloud Claire, Cloud Clare | 3 | 5 / 5 |  |
| Colab | Collab, collab | 3 | 6 / 6 |  |
| evals | evalves, valves, haval | 3 | 6 / 6 |  |
| F-Droid | FDroid, Fdroid | 3 | 10 / 10 |  |
| Nemotron | Nematron, Nementron | 3 | 13 / 11 |  |
| Unsloth | Unsoft, On Sloth, Onsloth | 3 | 8 / 7 |  |
| ab initio | a benissio, abenisio | 2 | 5 / 5 |  |
| AliExpress | Alley, Alliance | 2 | 7 / 7 |  |
| DeepSeek | DeepSee, DeepC | 2 | 12 / 9 |  |
| DMARC | GMARC, Dimark | 2 | 5 / 5 |  |
| Hannah | Hanna, Anna | 2 | 6 / 6 |  |
| Jupyter | Jupiter | 2 | 10 / 10 |  |
| Karpathy | Karpathi, Karpathi's | 2 | 10 / 10 |  |
| KSP | NKS, Kiss | 2 | 8 / 8 |  |
| MiniMax | Mini Max | 2 | 8 / 8 |  |
| Netanyahu | Etanyahu, Etnia | 2 | 6 / 6 |  |
| ONNX | ONIX, onyx | 2 | 10 / 10 |  |
| Qdrant | Qdrint, Compute rant | 2 | 5 / 5 |  |
| Qwen | Qwen1, quen | 2 | 13 / 11 |  |
| series | Ceres, Siries | 2 | 5 / 5 |  |
| TTS | CTS, tth | 2 | 9 / 7 |  |
| Ubiquiti | ubiquity | 2 | 6 / 6 |  |
| WhisperX | Whisper X, Whisper Air | 2 | 6 / 6 |  |
| Z alias | ZALAS, Zed Elias | 2 | 5 / 5 |  |
| abliteration | Obliteration | 1 | 5 / 5 |  |
| agentic | agenda | 1 | 5 / 5 |  |
| Ali | Alley | 1 | 8 / 8 |  |
| AMD | MD | 1 | 5 / 5 |  |
| APK | ABK | 1 | 6 / 6 |  |
| ASR | ACR | 1 | 6 / 6 |  |
| backhaul | backall | 1 | 6 / 6 |  |
| Bezeq | Bezek | 1 | 4 / 4 |  |
| Bituach Leumi | betuachlomi | 1 | 9 / 6 |  |
| Carrot Cake AI | Karrot Cake AI | 1 | 10 / 10 |  |
| Chatterbox | Shatterbox | 1 | 10 / 10 |  |
| Chevrat Hashmal | Chashmal Yisrael | 1 | 4 / 4 |  |
| Claude in Chrome | Claude Grouping Chrome | 1 | 4 / 4 |  |
| co.il | co.al | 1 | 4 / 4 |  |
| do you want me | Dramat me | 1 | 4 / 4 |  |
| DSR Holdings | Vidyester Holdings | 1 | 8 / 8 |  |
| eSpeak | Espeak | 1 | 10 / 10 |  |
| EXE | AXE | 1 | 10 / 10 |  |
| exports | experts | 1 | 6 / 6 |  |
| Ezra | Esther | 1 | 5 / 5 |  |
| fal | Fel | 1 | 5 / 5 |  |
| feedback loop | feedback leap | 1 | 6 / 6 |  |
| few-shot | fusil | 1 | 7 / 5 |  |
| fill | Phil | 1 | 6 / 6 |  |
| gut chodesh | Good church chodesh | 1 | 6 / 6 |  |
| Hagihon | Gichon | 1 | 4 / 4 |  |
| Hegseth | Hagsets | 1 | 5 / 5 |  |
| in bed | embed | 1 | 6 / 6 |  |
| inbound | for bound | 1 | 4 / 4 |  |
| inline | inland | 1 | 5 / 5 |  |
| Ivory | Ivor | 1 | 8 / 8 |  |
| Janglo | Danglo | 1 | 5 / 5 |  |
| Jupyter Notebook | Jupiter notebooks | 1 | 10 / 10 |  |
| Kaizen | Kaiser | 1 | 5 / 5 |  |
| Kdenlive | Caden Live | 1 | 10 / 10 |  |
| lav mic | Lav Mike | 1 | 4 / 4 |  |
| LLM | nelam | 1 | 13 / 7 |  |
| marking | marketing | 1 | 4 / 4 |  |
| mass sender | math sender | 1 | 6 / 6 |  |
| MCP | MCP2 | 1 | 20 / 20 |  |
| Meorav | Maorab | 1 | 18 / 11 |  |
| Modbus | Mod bus | 1 | 6 / 6 |  |
| Muse Spark | News Spark | 1 | 5 / 5 |  |
| NIC | NISH | 1 | 6 / 6 |  |
| nikud | ikut | 1 | 6 / 6 |  |
| Nvidia | NVideo | 1 | 6 / 6 |  |
| Ollama | ulama | 1 | 13 / 8 |  |
| OPNsense | OpenSense | 1 | 5 / 5 |  |
| Opus | Focus | 1 | 7 / 5 |  |
| populated | opulated | 1 | 4 / 4 |  |
| post-processing | PostgreSQL processing | 1 | 5 / 5 |  |
| prosody | porosity | 1 | 10 / 10 |  |
| pseudospam | Surasfam | 1 | 3 / 3 |  |
| rclone | Arclone | 1 | 4 / 4 |  |
| regex | RedJack | 1 | 6 / 6 |  |
| remote | ote | 1 | 4 / 4 |  |
| repo | Refo | 1 | 4 / 4 |  |
| SaaS | SAS | 1 | 5 / 5 |  |
| SBC | SPCAdmin | 1 | 5 / 5 |  |
| SFP | SFEN | 1 | 6 / 6 |  |
| Sonnet | Sommet | 1 | 8 / 8 |  |
| spray and pray | spray and prey | 1 | 6 / 6 |  |
| SSE | SSC | 1 | 6 / 5 |  |
| subscription | prescription | 1 | 4 / 4 |  |
| Superwhisper | Superwisper | 1 | 5 / 5 |  |
| system tray | system try | 1 | 4 / 4 |  |
| tab | top | 1 | 4 / 4 |  |
| tell her | Teller | 1 | 4 / 4 |  |
| TP-Link | TPM | 1 | 6 / 6 |  |
| vibe code | Vipecode | 1 | 6 / 6 |  |
| vocab | Rocab | 1 | 4 / 4 |  |
| Whisperian | Wisperion | 1 | 5 / 5 |  |
| Fritter |  | 0 | 12 / 10 |  |
| gezuntheit |  | 0 | 6 / 6 |  |
| vibe coded | Vipecode | 0 | 6 / 6 |  |
| vibe coding | Vipecode | 0 | 6 / 6 |  |
| zei gezunt |  | 0 | 6 / 6 |  |

## Release tests

`release_tests`: each Hebrish model release scored on held-out read recordings of these words (test
split of the speaker's read-aloud corpus, never trained on), with the shipped int8 ONNX files and the
Hebrish desktop engine on CPU. `exact`: clips whose transcript has the word exactly as the reference
writes it; `ci`: ignoring case; `heard_as_hits`: a known wrong form still appears. Small counts while
the test recordings accumulate (1 in 5 of each priority word's sentences is recorded for test).

Maintained by Daniel Rosehill (Hebrish). Built by `scripts/publish_error_list.py` in the
Voice-Training-Recorder repo.