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ASR-Error-List

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ASR-Error-List / README.md
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danielrosehill
Error list: 203 words, 359 occurrences (review 2026-10-31)
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metadata 
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.

  • 203 words, 359 occurrences. Last review: 2026-10-31.
  • 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, Abdominophorenic disinersia 2 7 / 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 7 / 20 high
feed fee 1 7 / 20 high
fluticasone Fluticazone 1 7 / 20 high
hunches punches 1 7 / 20 high
lifelike lifly 1 7 / 20 high
OTC ODC 1 7 / 20 high
pairs pears 1 8 / 20 high
potentially prudentially 1 7 / 20 high
re-architect re-arcolate 1 7 / 20 high
Relvar Ralvar 1 7 / 20 high
salbutamol Samputumal 1 6 / 20 high
sans-serif Sanserif 1 6 / 20 high
serif Sara 1 7 / 20 high
Singulair Singular 1 7 / 20 high
spacer Spacerin 1 7 / 20 high
text test 1 7 / 20 high
Ventolin Ventilin 1 7 / 20 high
Vercel inversel 1 7 / 20 high
gooseneck microphone 0 0 / 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, claudmd 5 13 / 20 normal
Easy Effects EasyFX 4 8 / 20 normal
Infisical Infiniscal, Infinisco, Inphysical, physical 4 11 / 20 normal
Parquet Parkwell, parket, Parkwet, partwet 4 5 / 20 normal
AdGuard Adgard, Adguard, ad? guard 3 0 / 20 normal
ComfyUI Comfy UI, System UI 3 2 / 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
Broadlink broad link, broadlink 2 1 / 20 normal
Clerk clerk 2 0 / 20 normal
Composio Compi Posio, Compose 2 7 / 20 normal
Hey Yossi heiyasi, Hei Yossi 2 0 / 20 normal
named entity recognition Name density recognition, named density recognition 2 0 / 20 normal
OnePlus Nord 3 5G OnePlus Nord 3.5G, OnePlus Nord 35G 2 7 / 20 normal
Parakeet Power Key, PowerKey 2 11 / 20 normal
RecForge II Recforged, Rec Fortage 2 0 / 20 normal
sqlite-vec SQLite Fex, SQLite Bec 2 0 / 20 normal
Typeform type form 2 0 / 20 normal
wake word Wakeford, Wakeward 2 0 / 20 normal
agentic AI Agentica 1 0 / 20 normal
auto gain auto game 1 7 / 20 normal
cache Cash 1 1 / 20 normal
capybara kapybara 1 0 / 20 normal
ChatGPT CatGPT 1 2 / 20 normal
Claude uclaud 1 15 / 20 normal
convention invention 1 7 / 20 normal
cutlery cuddlery 1 7 / 20 normal
Debian Debin 1 10 / 20 normal
Edifier T5 Edifir T5 1 0 / 20 normal
Flic Flick 1 0 / 20 normal
gallbladder rollbladder 1 0 / 20 normal
GERD Gerd 1 0 / 20 normal
Google Cloud Console Google Cloud Konsole 1 2 / 20 normal
Haiku haiku 1 2 / 20 normal
Humanity's Last Exam humanity's last evaluation 1 0 / 20 normal
IBM Plex Sans IBM Plexans 1 7 / 20 normal
IR IARC 1 0 / 20 normal
kibbutzim kiputzim 1 0 / 20 normal
Kokoro Kikoro 1 2 / 20 normal
llama.cpp llama.cp 1 1 / 20 normal
LXQt LXT 1 5 / 20 normal
manga magma 1 0 / 20 normal
MCP Hub MCB Hub 1 6 / 20 normal
microcode macro code 1 3 / 20 normal
microWakeWord Microweak 1 0 / 20 normal
MkDocs mkdocs 1 0 / 20 normal
moshavim oshavim 1 0 / 20 normal
Neo4j Neo4J 1 0 / 20 normal
NVMe NBMA 1 4 / 20 normal
OTP OTB 1 8 / 20 normal
post-cholecystectomy syndrome post-collasis syndrome 1 0 / 20 normal
SmugMug Smug Mug 1 0 / 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
VRAM ERAM 1 6 / 20 normal
Wise wise 1 0 / 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 14 / 6
LoRA Lores, Loas, Laura, lores, Loa, LoRa, lore, lures 15 9 / 6
ROCm ROCM, Rockham, Rockin, RCM, Rock'em 15 12 / 11
Qdrant Qdrint, Compute rant, Qdrin, Qutrin, quadrant, queue drint 7 5 / 5
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
evals evalves, valves, haval, efils 4 6 / 6
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
F-Droid FDroid, Fdroid 3 10 / 10
Nemotron Nematron, Nementron 3 14 / 11
Netanyahu Etanyahu, Etnia 3 6 / 6
rclone Arclone, arc loan, our clone 3 6 / 4
Unsloth Unsoft, On Sloth, Onsloth 3 9 / 7
Z alias ZALAS, Zed Elias, ZLA 3 7 / 5
ab initio a benissio, abenisio 2 5 / 5
AliExpress Alley, Alliance 2 7 / 7
Carrot Cake AI Karrot Cake AI, Car Cake AI 2 10 / 10
DeepSeek DeepSee, DeepC 2 13 / 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
ONNX ONIX, onyx 2 10 / 10
Qwen Qwen1, quen 2 14 / 11
series Ceres, Siries 2 5 / 5
TTS CTS, tth 2 10 / 7
Ubiquiti ubiquity 2 6 / 6
WhisperX Whisper X, Whisper Air 2 6 / 6
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
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 14 / 7
marking marketing 1 4 / 4
mass sender math sender 1 6 / 6
MCP MCP2 1 23 / 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
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 7 / 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 13 / 10
gezuntheit 0 6 / 6
vibe coded Vipecode 0 6 / 6
vibe coding Vipecode 0 7 / 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.