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ARK-ASR-0.6B

Name: ARK-ASR-0.6B
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automatic-speech-recognition

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ARK-ASR-0.6B: Efficient Multilingual ASR with Online Policy Distillation

GitHub

arXiv

TL;DR ARK-ASR-0.6B is an automatic speech recognition model trained with teacher-data adaptation and on-policy distillation, using a compact 0.6B-scale decoder LLM together with a dedicated audio encoder and adapter. The accompanying training, inference, and evaluation code is available at AutoArk/open-audio-opd.

Abstract

ARK-ASR is an audio ASR student model optimized with the teacher-data adaptation + online policy distillation (TD + OPD) recipe from open-audio-opd.

Instead of relying only on static supervised transcripts, OPD lets the student generate transcripts online and trains it against token-level teacher scores on the student's own generated behavior. This checkpoint corresponds to the Ark-Base+TD+OPD model reported in the open-audio-opd results.

ARK-ASR currently supports Chinese, English, German, Japanese, French, Korean, Spanish, Polish, Italian, Romanian, Hungarian, Czech, Dutch, Finnish, Croatian, Slovak, Slovene, Estonian, and Lithuanian ASR.

Supported Languages

Chinese, English, German, Japanese, French, Korean, Spanish, Polish, Italian, Romanian, Hungarian, Czech, Dutch, Finnish, Croatian, Slovak, Slovene, Estonian, and Lithuanian.

Model Overview

Figure 1: ARK-ASR architecture. Audio is encoded by a Whisper-style encoder with RoPE, merged through an MLP adapter, and injected into a Qwen2 decoder by replacing audio placeholder token embeddings before transcript generation.

Model size: 0.6B decoder LLM parameters, with a separate 0.6B-scale Whisper-style audio encoder and MLP adapter
Task: automatic speech recognition
Architecture: audio-capable autoregressive Transformers model with custom arkasr remote code
Checkpoint format: safetensors
Sampling rate: 16 kHz
Recommended inference code: scripts/infer/ark_asr_transformers.py

The model should be loaded with trust_remote_code=True. The official inference script handles the processor, tokenizer, audio prompt format, generation cleanup, and ASR token filtering.

Performance

The following results are from the open-audio-opd evaluation. Lower CER/WER is better.

English WER

Model	AMI	Earnings22	GigaSpeech	LS Clean	LS Other	SPGISpeech	VoxPopuli	Avg
Ark-ASR	11.54%	10.07%	8.95%	1.87%	3.89%	2.89%	6.63%	6.55%
Qwen3-ASR-0.6B	11.66%	11.06%	9.14%	2.13%	4.45%	3.03%	7.07%	6.93%
Qwen3-ASR-1.7B	10.56%	10.25%	8.74%	1.63%	3.40%	2.84%	6.35%	6.25%

Chinese CER

Model	AISHELL-1	Wenet-meeting	Wenet-net	Avg
Ark-ASR	2.02%	5.92%	4.96%	4.30%
Qwen3-ASR-0.6B	2.07%	5.57%	5.45%	4.36%
Qwen3-ASR-1.7B	1.50%	4.69%	4.55%	3.58%

Ark-ASR is the 0.6B-scale ASR checkpoint trained with teacher-data adaptation and on-policy distillation from open-audio-opd.

Inference

Run ASR inference with Hugging Face Transformers:

python

import torch
from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer

model_path = "AutoArk-AI/ARK-ASR-0.6B"
audio_path = "assets/libai.wav"

device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if device == "cuda" else torch.float32

processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype=torch_dtype,
    attn_implementation="sdpa",
).to(device)
model.eval()


def build_bad_words_ids(tokenizer):
    eos_ids = tokenizer.eos_token_id
    keep_ids = {eos_ids} if isinstance(eos_ids, int) else set(eos_ids or [])
    bad_ids = set(tokenizer.all_special_ids) - keep_ids
    bad_ids.update(
        token_id
        for token, token_id in tokenizer.get_added_vocab().items()
        if token.startswith("<") and token.endswith(">") and token_id not in keep_ids
    )
    return [[token_id] for token_id in sorted(bad_ids)]

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "audio", "path": audio_path},
            {"type": "text", "text": "Please transcribe this audio."},
        ],
    }
]

inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    return_tensors="pt",
    sampling_rate=16000,
    audio_padding="longest",
    text_kwargs={"padding": "longest"},
    audio_max_length=30 * 16000,
)
inputs = inputs.to(device)
if "audios" in inputs:
    inputs["audios"] = inputs["audios"].to(dtype=torch_dtype)

bad_words_ids = build_bad_words_ids(tokenizer)
with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        do_sample=False,
        max_new_tokens=256,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
        bad_words_ids=bad_words_ids,
    )
decoded_outputs = tokenizer.batch_decode(
    outputs[:, inputs.input_ids.shape[1] :],
    skip_special_tokens=True,
)
print(decoded_outputs)

For batch JSONL inference, use the open-source inference code:

bash

git clone https://github.com/AutoArk/open-audio-opd
cd open-audio-opd
pip install -e .

The input JSONL should contain one ASR sample per line:

{"audio":"/path/to/audio.wav","text":"","task":"asr","begin_time":-1,"end_time":-1}

bash

python scripts/infer/ark_asr_transformers.py \
  --input /path/to/input.jsonl \
  --output runs/infer/predictions.jsonl \
  --model_path AutoArk-AI/ARK-ASR-0.6B \
  --processor_path AutoArk-AI/ARK-ASR-0.6B \
  --batch_size 40 \
  --dtype float16 \
  --attn_impl sdpa

The output JSONL preserves input metadata and adds:

pred_text: cleaned prediction text for downstream evaluation
pred_text_raw: raw decoded generation before cleanup

Evaluation

The repository also includes a J/WER evaluation entrypoint:

bash

python scripts/eval/eval_jwer_ark_asr_transformers.py \
  --input /path/to/test.jsonl \
  --output runs/eval/result.jsonl \
  --model_path AutoArk-AI/ARK-ASR-0.6B \
  --processor_path AutoArk-AI/ARK-ASR-0.6B \
  --batch_size 40 \
  --dtype float16 \
  --attn_impl sdpa

No evaluation audio or dataset files are bundled with this model repository.

Acknowledgements

The training code is based on THUNLP/OPD and verl. The OPD recipe uses a stronger ASR teacher to score online student rollouts.

Citation

If you find ARK-ASR or open-audio-opd useful, please cite:

bibtex

@misc{lin2026dataefficientopd,
  title={Data-Efficient On-Policy Distillation for Automatic Speech Recognition},
  author={Lin, Yu and Wang, Yiming and Cai, Runyuan and Zeng, Xiaodong},
  year={2026},
  eprint={2605.28139},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2605.28139}
}

Audio8/ARK-ASR-0.6B

ARK-ASR-0.6B: Efficient Multilingual ASR with Online Policy Distillation

Abstract

Supported Languages

Model Overview

Performance

English WER

Chinese CER

Inference

Evaluation

Acknowledgements

Citation

Audio8/
ARK-ASR-0.6B