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TeamUNIVA/
qwen3_asr_1.7b_ko_beta

Name: qwen3_asr_1.7b_ko_beta
Availability: InStock

safetensors

qwen3_asr

automatic-speech-recognition

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TeamUNIVA/qwen3_asr_1.7b_ko_beta

This model is a fine-tune of Qwen/Qwen3-ASR-1.7B on Korean speech data for automatic speech recognition (ASR). It achieves lower error rates than the base model across Korean benchmarks, with negligible degradation on English (LibriSpeech).

Evaluation Results

Korean scores are measured in CER (Character Error Rate); the English score is measured in WER (Word Error Rate). Lower is better for both metrics.

Dataset	Metric	Qwen3-ASR-1.7B (base)	This model	Change (%p)
clova_call	CER	0.0448	0.0293	-1.55
common_voice_ko	CER	0.0719	0.0628	-0.91
fleurs_ko	CER	0.0142	0.0138	-0.04
ksponspeech	CER	0.0865	0.0670	-1.95
zeroth	CER	0.0264	0.0230	-0.34
Korean total	CER	0.0686	0.0537	-1.49
librispeech (English, reference)	WER	0.0250	0.0246	-0.04

Evaluation Set

All open Korean benchmark evaluation sets used above were fully reviewed by hand: each utterance was checked for dual TN/ITN (text normalization / inverse text normalization) transcripts, low-quality samples were filtered out, and mis-transcriptions were corrected. Every utterance was then evaluated against both the TN and ITN transcripts, and the better-scoring transcript of the pair was used for the final aggregated result. After this review, 8,383 of the original 8,489 records were retained (106 excluded):

Dataset	Retained records
clova_call	1,083
common_voice_ko	564
fleurs_ko	382
zeroth	449
kspon_eval_clean	2,946
kspon_eval_other	2,959
Total	8,383

Usage

Uses the same qwen-asr package as the base model.

pip install -U qwen-asr

python

import torch
from qwen_asr import Qwen3ASRModel

model = Qwen3ASRModel.from_pretrained(
    "TeamUNIVA/qwen3_asr_1.7b_ko_beta",
    dtype=torch.bfloat16,
    device_map="cuda:0",
    max_inference_batch_size=32,
    max_new_tokens=256,
)

results = model.transcribe(
    audio="path/to/audio.wav",
    language="ko",
)

print(results[0].text)

Limitations

This model was fine-tuned specifically for Korean speech recognition; performance on other languages may be similar to the base model or require separate validation.
Evaluation is based on the benchmark datasets listed above; results may differ in real-world production conditions (noise, speaker variability, etc.).