TeamUNIVA/qwen3_asr_1.7b_ko_beta
English | 한국어
Update: A further-trained successor to this model is available at
TeamUNIVA/qwen3_asr_1.7b_ko. It was initialized from this model and achieves lower error rates on Korean benchmarks, so we recommend using it for new work.
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
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,
)
# The language is detected automatically. To force Korean, pass
# language="Korean" (the full language name, as expected by qwen-asr).
results = model.transcribe(
audio="path/to/audio.wav",
)
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.).