Results
Character Error Rate (CER, lower is better). Khmer has no spaces between words,
so CER is computed space-insensitive; the out-of-domain set is additionally
normalized by removing punctuation.
Table with columns: Evaluation set, Clips, CER (corpus), CER (avg), Median CER, Perfect (CER=0)| Evaluation set | Clips | CER (corpus) | CER (avg) | Median CER | Perfect (CER=0) |
|---|
| In-domain dev | 1,000 | 1.96% | 1.95% | 0.65% | 49.2% |
| Out-of-domain | 2,906 | 7.91% | 8.26% | 6.25% | 26.4% |
- CER (corpus) = total edit distance ÷ total reference characters (micro-average).
- CER (avg) = mean of per-clip CER (macro-average).
Usage
Install the qwen-asr package (transformers backend):
import torch
from qwen_asr import Qwen3ASRModel
model = Qwen3ASRModel.from_pretrained(
"seanghay/Qwen3-ASR-0.6B-Khmer",
dtype=torch.bfloat16,
device_map="cuda:0",
max_inference_batch_size=32,
max_new_tokens=256,
)
results = model.transcribe(
audio="path/to/khmer.wav",
)
print(results[0].text)
Audio is resampled to 16 kHz mono internally. Long recordings are automatically
chunked; for long clips set a larger max_new_tokens so the transcript is not cut off.
Training
Table | |
|---|
| Base model | Qwen/Qwen3-ASR-0.6B |
| Language | Khmer (km) |
| Training data | DDD-Cambodia/khmer-speech-dataset (~700 h, ~384k clips) |
| Epochs | 3 (35,997 steps) |
| Effective batch size | 32 (per-device 4 × grad-accum 8) |
| Learning rate | 2e-5, linear schedule with warmup |
| Precision | bf16 |
|
The model is trained to emit a language tag followed by the transcript
(language Khmer<asr_text>…); the qwen-asr package parses this automatically.
Limitations
- Tuned primarily for read/clean Khmer speech. Accuracy degrades on noisy,
spontaneous, or heavily code-switched (Khmer–English) technical speech,
where English terms may be transliterated phonetically into Khmer script.
- Output is unpunctuated / minimally segmented Khmer text.
- As with most ASR models, very long or hesitant/repetitive speech can
occasionally produce repeated phrases.
License
Released under the Apache-2.0 license, inheriting the license of the base
Qwen3-ASR-0.6B model.
Acknowledgements
Built on Qwen3-ASR by the Alibaba Qwen team.
Citation
If you use this model, please cite:
@misc{seanghay2026qwen3asrkhmer,
title = {Qwen3-ASR-0.6B-Khmer},
author = {Seanghay},
year = {2026},
howpublished = {\url{https://huggingface.co/seanghay/Qwen3-ASR-0.6B-Khmer}}
}
This model is fine-tuned from Qwen3-ASR:
@misc{qwen3asr,
title = {Qwen3-ASR},
author = {Qwen Team},
year = {2025},
url = {https://github.com/QwenLM/Qwen3-ASR}
}
