Qwen3-ASR-0.6B-Khmer-v4

A 0.6B-parameter Khmer automatic speech recognition (ASR) model, fine-tuned from Qwen3-ASR-0.6B on a large Khmer speech corpus with on-the-fly audio augmentation. It transcribes Khmer audio into Khmer script.

• Base model: Qwen3-ASR-0.6B (qwen3_asr / Qwen3ASRForConditionalGeneration)
• Language: Khmer (km)
• Task: Automatic Speech Recognition
• Selected checkpoint: step 68000 (~3.67 epochs). This is the lowest dev-set eval_loss checkpoint (0.0407) of the augmented run.

Evaluation

Evaluated on a held-out set of 145 Khmer utterances. The metric is Character Error Rate (CER), computed whitespace-insensitive (all whitespace stripped and text NFC-normalized before the character-level edit distance). We report two variants: raw, and with punctuation removed (Unicode categories P/S, which covers the Khmer full-stop ។ and other marks that carry little acoustic signal).

The raw CER is dominated by the trailing Khmer full-stop ។, which the model often omits; removing punctuation raises exact-match to ~41% and gives a cleaner view of true recognition quality.

Usage

from qwen_asr import Qwen3ASRModel

asr = Qwen3ASRModel.from_pretrained(
    "seanghay/Qwen3-ASR-0.6B-Khmer-v4",
    max_new_tokens=256,
)
asr.model.eval()

results = asr.transcribe("audio.wav", language="Khmer")
print(results[0].text)

Recommended decoding for longer audio

This is a small (0.6B) model trained on ~30s windows. Feeding a whole long file at once can trigger repetition loops. Pre-split the audio at silence boundaries into ~15s chunks and cap generation length:

from qwen_asr.inference.utils import SAMPLE_RATE, normalize_audios, split_audio_into_chunks

wav = normalize_audios("long_audio.ogg")[0]                 # mono float32 @ 16 kHz
parts = split_audio_into_chunks(wav, SAMPLE_RATE, max_chunk_sec=15)
chunks = [(c, SAMPLE_RATE) for c, _ in parts]

results = asr.transcribe(chunks, language="Khmer")
text = " ".join(r.text.strip() for r in results if r.text.strip())
print(text)

Training

• Base: Qwen3-ASR-0.6B
• Data: ~592k Khmer speech–text pairs (train) / 1,000 held-out (dev)
• Augmentation: on-the-fly audio augmentation on the train split (p=0.7); eval kept clean
• Epochs: 4 (this checkpoint is step 68000 of 74036, ~3.67 epochs)
• Effective batch size: 32 (per-device 4 × grad-accum 8)
• Learning rate: 2e-5
• Precision: bf16
• Hardware: single 24 GB GPU (RTX 3090)

Limitations

• Repetition loops can occur on long single chunks or acoustically ambiguous segments (dense Khmer–English code-switching). Use ~15s chunking to mitigate.
• Short/silent chunks (< ~1s) may be hallucinated into a common phrase; drop sub-second tail chunks before transcribing.
• Trained primarily on read/short-form Khmer speech; far-field, noisy, or heavily code-switched audio is out of distribution.
• Punctuation (esp. the sentence-final ។) is often omitted.

Files

Inference weights only (model.safetensors, config, tokenizer). Optimizer and training state are not included, so this repo cannot resume training.