Datasets:
license: other
language:
- yi
task_categories:
- automatic-speech-recognition
tags:
- yiddish
- asr
- benchmark
- multi-genre
pretty_name: Yiddish ASR Benchmark (Multi-Genre)
Yiddish ASR Benchmark (Multi-Genre)
A small, genre-diverse Yiddish ASR benchmark seed set: 6 clips (~53 minutes total), one per
genre, each with a Gemini-generated verbatim reference transcript. Intended to complement the
existing single-speaker Yiddish24 collections (Kohn-AI/yiddish24-audio,
Kohn-AI/yiddish24-wav, ABE101/yiddish24-dual-asr) with broader genre coverage for
benchmarking rather than training.
All source audio is from yiddish24.com.
| field | meaning |
|---|---|
file_name |
audio (mp3) under data/ |
genre |
one of torah_shiur, news, interview, monologue_podcast, general_podcast, vlog |
title |
episode/segment title |
speaker |
presenter/host name, where applicable |
category_path |
yiddish24.com category the source came from |
track_id |
yiddish24 post id(s); news clip concatenates 3 bulletins, ids ;-separated |
source_url |
original cloudfront/cdn url(s); ;-separated for the concatenated news clip |
clip_offset_sec |
start offset (seconds) into the original recording where this clip begins |
duration_sec |
clip duration |
text_gemini |
reference transcript (Yiddish, Hebrew script), verbatim, unclear spans marked [?] |
transcription_model |
model used to produce text_gemini |
Genres
| genre | title | source |
|---|---|---|
torah_shiur |
פרשת דברים | הרה"ג ר' גמליאל ראבינאוויטש שליט"א |
news |
3 concatenated news bulletins | Kol Mevaser news desk |
interview |
הרב דוד פינטער - עמעזאן דיסטריבורטער | אלגעמיינע אינטערוויוס |
monologue_podcast |
קול מדע | משה נחום קרויס |
general_podcast |
טשיקאווע ברעקלעך | יושע ווייס |
vlog |
עיטש וואק קאמפאני | חיים פערלאוויטש שאו |
6 clips · ~53 min.
Reference transcript method
text_gemini was produced by google/gemini-3.1-pro-preview, called via the Vercel AI Gateway
chat completions endpoint with the audio file attached as a file content part
(media_type: audio/mpeg) and a verbatim-transcription prompt. These are a first-pass gold
standard — worth a human spot-check before treating as strict ground truth, especially for
proper nouns and numbers. Unclear audio is marked [?] in the transcript.
from datasets import load_dataset
ds = load_dataset("Kohn-AI/yiddish-asr-benchmark")