TrorYong ASR Model

TrorYongASR, is an Automatic Speech Recognition Model implemented by KrorngAI.

TrorYong (ត្រយ៉ង) is Khmer word for giant ibis, the bird that symbolises Cambodia.

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Installation

You can easily install tror-yong-asr using pip command as the following:

pip install tror-yong-asr

To use TrorYongASR, there are few dependencies: transformers, safetensors, and torchaudio.

Usage

Get started with the code below

from transformers import AutoProcessor
from tror_yong_asr import TrorYongASRModel, transcribe, translate, identify_language


model_id = "KrorngAI/TrorYongASR2-small"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = TrorYongASRModel.from_pretrained(model_id)

result1 = identify_language('/path/to/audio_file.mp3', model, processor)
print(result1)

result2 = transcribe('/path/to/audio_file.mp3', model, processor, max_tokens=64)
print(result2)

result3 = translate('/path/to/audio_file.mp3', model, processor, max_tokens=64)
print(result3)

TrorYongASR has 2 pre-trained weights that support Khmer and English:

• Tiny version with model_id=KrorngAI/TrorYongASR2-tiny
• Small version with model_id=KrorngAI/TrorYongASR2-small

Evaluation

Dataset

The evaluation assesses transcription capability on two datasets: 1) google/fleurs for Khmer and 2) openslr/librispeech_asr for English. All results are from the test split of each dataset.

Audio longer than 30 seconds are excluded from the evaluation (that is why google/fleurs has 765 samples instead of 771 and librispeech_asr.clean has 2611 instead of 2620).

Metrics

For transcription task, 3 metrics below are used

• Token Error Rate (TER): Proportion of incorrectly predicted tokens
• Character Error Rate (CER): Proportion of characters that are incorrect
• Word Error Rate (WER): Proportion of words that are incorrect

TER measures model's capability in predicting the next token given the audio input and the current sequence of tokens. This metric is weaker than WER and CER because it doesn't account for insertions, deletions, substitutions, and autoregression as comprehensively.

Results

Both configurations performance well on English language. However, Khmer language remains a challenge.

Note: To compute CER and WER, whitespaces are added between words in Khmer text (Khmer text does not have word boundaries like English text). To do so, khmercut PyPI package is used to tokenize Khmer text into words, and then the words are joined back together with whitespaces. It is due to no-boundary Khmer words that Token Error Rate is utilized as one of the metrics.

Comparison with TrorYongASR1 and Whisper:

Key Observations:

• As mentioned above, TrorYongASR2 clearly outperforms TrorYongASR1 and requires just a small increase in the number of parameters (the increase comes from additional length predictor and gate projection layers)
• Whisper has the highest number of parameters for comparable configuration (39M vs 29M for tiny, and 244M vs 136M for small)
• Whisper shows significantly lower word error rates on English (7.6% for tiny, 3.4% for small)
• TrorYongASR2 performs best on Khmer dataset (57.07% for tiny, and 36.70% for small)

Note: WER data of Whisper is taken directly from their paper.

Note on Translation Task: According to KrorngAI, the 2 translation datasets above are built using the API of TranslateKH which, in turn, uses an AI model for the translation (Khmer to English and vice-versa). Without any means to check the correctness of the translation over 209 hours of audio, TrorYongASR2 is not evaluated on any translation datasets. It can be assumed that the translation performance of TrorYongASR2 is upper bounded by the AI model used in the backend of TranslateKH.

Fine-tuning

You can follow this notebook below to learn to fine-tune TrorYongASR2.

Citation

BibTeX:

@online{khun2026,
  author = {Khun, Kimang},
  title = {TrorYongASR2: {Permuted} {Language} {Modeling} with
    {Monotonic} {Alignment,} {Gated} {Attention,} and
    {Normalization-free} {Design}},
  date = {2026-10-19},
  url = {https://kimang18.github.io/krorngai-blog/TrorYongASR2/},
  langid = {en}
}