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seanghay/Qwen3-ASR-0.6B-Khmer
Qwen3-ASR-0.6B-Khmer is a automatic speech recognition model from seanghay. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
A Khmer (ខ្មែរ) automatic speech recognition model, fine-tuned from Qwen/Qwen3-ASR-0.6B on ~700 hours of Khmer speech from the DDD-Cambodia/khmer-speech-dataset. It substantially improves Khmer transcription accuracy…
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From the Hugging Face model README
A Khmer (ខ្មែរ) automatic speech recognition model, fine-tuned from Qwen/Qwen3-ASR-0.6B on ~700 hours of Khmer speech from the DDD-Cambodia/khmer-speech-dataset. It substantially improves Khmer transcription accuracy over the base model while keeping the compact 0.6B footprint.
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.
| 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% |
Install the qwen-asr package (transformers backend):
pip install -U qwen-asr
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, # increase for long audio to avoid truncation
)
results = model.transcribe(
audio="path/to/khmer.wav", # local path, URL, base64, or (np.ndarray, sr)
)
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.
| 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 |
| Hardware | 1× NVIDIA RTX 3090 (24 GB) |
| Final eval loss | 0.040 |
The model is trained to emit a language tag followed by the transcript
(language Khmer<asr_text>…); the qwen-asr package parses this automatically.
Released under the Apache-2.0 license, inheriting the license of the base Qwen3-ASR-0.6B model.
Built on Qwen3-ASR by the Alibaba Qwen team.
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}
}