Datasets:
license: cc-by-4.0
language:
- kk
- ru
task_categories:
- automatic-speech-recognition
tags:
- kazakh
- russian
- code-switching
- speech
- asr
- benchmark
pretty_name: Kazakh Code-Switching ASR Benchmark
size_categories:
- n<1K
Kazakh Code-Switching ASR Benchmark
A benchmark for evaluating ASR systems on natural Kazakh speech that code-switches with Russian — the everyday Kazakh–Russian mixing found in stand-up, interviews and vlogs, not scripted read speech. This is, to our knowledge, the first speech/ASR resource targeting the Kazakh–Russian code-switching pair (existing Kazakh–Russian NLP resources are text-only).
Code, scoring harness and full analysis: https://github.com/Tim2190/Kaz-ASR-codeswitch-benchmark
Dataset structure
31 clips (~3.7 min total, clips ≤ 20 s), sourced from CC BY YouTube material. Each clip carries two independent reference transcripts and four binary phenomenon flags:
| Field | Description |
|---|---|
file_name |
path to the WAV clip |
audio_id |
clip identifier |
transcript_verbatim |
literal transcript, exactly as spoken (reductions kept) |
transcript_normalized_written |
dictionary word forms |
has_contraction |
fast-speech reductions present |
has_dialect_slang |
dialectal/slang lexis present |
has_barbarisms |
a Russian insertion where a native Kazakh word exists |
has_propers |
proper nouns present |
audio_source_link |
source channel/title (CC BY attribution) |
Audio is 48 kHz stereo 16-bit WAV. The two-layer design separates genuine
recognition errors from a system's tendency to auto-normalize morphology.
Full annotation rules are in annotation_methodology.md in the GitHub repo.
Loading
from datasets import load_dataset
ds = load_dataset("Tim2190/kazakh-codeswitch-asr", split="train")
print(ds[0]["audio"], ds[0]["transcript_verbatim"])
Benchmark results (5 systems, WER / CER, lower is better)
| System | Type | WER | CER |
|---|---|---|---|
| Fine-tuned Kazakh Whisper | fine-tuned | 11.9% | 3.8% |
| Yandex SpeechKit | commercial STT | 17.1% | 6.5% |
| Gemini 2.5 Flash | commercial LLM | 29.5% | 12.5% |
| Whisper large-v3 | base, zero-shot | 42.5% | 13.0% |
| Google Cloud STT | commercial STT | 64.9% | 38.1% |
Key finding: commercial services span the entire quality range (Yandex 2nd, Google last), and Russian insertions are the dominant error driver — errors concentrate on the switched-in Russian words. See the GitHub repo for the per-phenomenon breakdown and word-level analysis.
License & attribution
Source audio is derived from Creative Commons CC BY YouTube material; the
originating channel/title for every clip is in the audio_source_link field.
Annotations and code are MIT-licensed. Created by Timur Seidalin.