Hebrish

Sabich-Qwen3-ASR-1.7B

README

License: apache-2.0

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.

Table

Listen	hebrish.app/updates/2026-10-22: 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 Why this voice note · 1:36 Coming to speech to text · 6:36 Why Hebrew words go wrong · 9:23 How the fine-tune works · 12:27 Why Parakeet, and the model plan · 17:44 Disfluencies, shekels, paragraphs · 25:40 Tech vocabulary and Meorav · 29:07 The kitchen sink: Sabich and Jachnun · 33:35 Licensing, accents, noisy recordings · 39:10 One recorder for ASR and TTS · 41:04 Desktop shortcuts and the macro pad · 43:34 Android: Bluetooth and one button · 45:35 Streaming and Shakshuka · 50:28 Long-form transcription · 56:00 Output modes · 63:01 Report a word, and more voices

Output

English, punctuated and cased. Hebrew words are written in Latin script, never Hebrew script. Always transcribe with language="English", which is the prefix the model was trained with.

Results

Held-out test split of the speaker's recordings (term-disjoint from training), dataset revision cf84f7e4. WER is normalised (lower-cased, punctuation stripped); term recall is a word-boundary match on the romanized Hebrew term.

Table with columns: Stock Qwen3-ASR-1.7B, This model
	Stock Qwen3-ASR-1.7B	This model
English-Hebrew code-switching, WER	10.0%	5.4%
Hebrew terms never seen in training, recall	32%	50%
Hebrew terms seen in training, recall	40%	73%
Speaker's own English, WER	4.6%	2.5%
LibriSpeech test-clean (200 utterances), WER	1.2%	1.35%
Long-form (about 3-minute joined clips), WER	12.0%	8.7%
Numbers written in digits	2/8	8/8

Use

python

import torchfrom qwen_asr import Qwen3ASRModel  # pip install qwen-asr model = Qwen3ASRModel.from_pretrained("Hebrish/Sabich-Qwen3-ASR-1.7B", dtype=torch.bfloat16, device_map="cuda:0")print(model.transcribe(audio="note.wav", language="English")[0].text)

About 5 GB of VRAM in bf16. On AMD GPUs it runs under PyTorch ROCm.

Run it without PyTorch

GGUF builds are in Hebrish/Sabich-Qwen3-ASR-1.7B-GGUF:

llama.cpp/ for desktops: llama-mtmd-cli or llama-server with --mmproj, on a GPU through HIP (ROCm), Vulkan or CUDA, or on a CPU. bf16, Q8_0 and Q4_K_M.
transcribe.cpp/ for Android: import the Q4_K_M file into Offline Voice Input (dev.notune.transcribe).

The GGUF card has the exact commands and a check of every file against this model.

Training

Data: danielrosehill/Read-Aloud-Voice-Sentences at revision e918e381, blend all: 592 minutes of the speaker's own takes (383 of them code-switching), plus 10% LibriSpeech-PC replay against forgetting. 658 minutes in total.
Recipe: the official Qwen3-ASR full fine-tuning script (finetuning/qwen3_asr_sft.py, upstream commit 7c6daf7). 3 epochs, lr 2e-5 with linear decay, 5% warmup, effective batch 32, bf16, one H100, 15 minutes. The epoch-3 checkpoint was chosen.
Targets: language English<asr_text> + the punctuated, cased transcript.

Limits

Trained on one speaker (an Irish English speaker living in Israel), reading prompts on a phone. Other voices will gain less.
About half of unseen Hebrew words are still missed or misspelled.
Long-form audio is its weakest area.

Licence: the base model is Apache-2.0; use of this fine-tune is governed by the Hebrish terms of use.

Base model and licence notices

The licence stated at the top of this card covers Hebrish's contribution; the base model's licence keeps applying to the base model, and nothing here restricts what that licence allows for it. Terms of use: https://hebrish.app/terms.

Hebrish is not affiliated with or endorsed by Alibaba Cloud.

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Model Details

Model Provider

Hebrish

Model Tree

Base

Qwen/Qwen3-ASR-1.7B

Fine-tuned

this model

Input Modalities

Audio

Output Modalities

Text

Supported Functionality