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Cataloged from Edge0/ARK-ASR-3B
All models by Edge0 →TL;DR ARK-ASR-3B is a multilingual automatic speech recognition model. It achieves current state-of-the-art results on the Hugging Face Open ASR Leaderboard English short-form benchmark, with an average WER of 5.04% and RTFx of 490.98 across AMI, Earnings22, GigaSpeech, LibriSpeech, SPGISpeech, and VoxPopuli. The accompanying training, inference, and evaluation code is available at AutoArk/open-audio-opd.
ARK-ASR-3B is a 3B-scale audio-capable autoregressive Transformers model for automatic speech recognition.
It combines a Whisper-style audio encoder, an MLP adapter, and a Qwen decoder with custom arkasr remote code.
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.
Chinese, English, German, Japanese, French, Korean, Spanish, Polish, Italian, Romanian, Hungarian, Czech, Dutch, Finnish, Croatian, Slovak, Slovene, Estonian, and Lithuanian.
Figure 1: ARK-ASR architecture. Audio is encoded by a Whisper-style encoder with RoPE, merged through an MLP adapter, and injected into a Qwen decoder by replacing audio placeholder token embeddings before transcript generation.
arkasr remote codesafetensorsscripts/infer/ark_asr_transformers.pyscripts/vllm/ark_asr_vllmThe 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.
The following results are from the Hugging Face Open ASR Leaderboard. Lower WER is better. ARK-ASR-3B reaches the current state of the art on this English short-form benchmark.
Run ASR inference with Hugging Face Transformers:
For batch JSONL inference, use the open-source inference code:
The input JSONL should contain one ASR sample per line:
The output JSONL preserves input metadata and adds:
pred_text: cleaned prediction text for downstream evaluationpred_text_raw: raw decoded generation before cleanupARK-ASR can also be deployed as a vLLM-backed online ASR service with the
adapter in
scripts/vllm/ark_asr_vllm.
The service exposes both a compact /asr endpoint and an OpenAI-style
/v1/audio/transcriptions endpoint.
Clone and install the serving code:
Start the service:
Check the service:
Run one transcription request:
OpenAI-style transcription endpoint:
Stop the service:
The vLLM adapter registers the custom arkasr model, loads the local
processor/tokenizer with trust_remote_code=True, applies generation-time
token masking for non-ASR control tokens, and keeps <|im_end|> as the stop
token. Service logs and PID files are written under runs/vllm/.
The reported leaderboard numbers are evaluated with the Hugging Face
open_asr_leaderboard
evaluation code.
For local J/WER evaluation, the repository also includes this entrypoint:
No evaluation audio or dataset files are bundled with this model repository.
The training code is based on THUNLP/OPD and verl. The OPD recipe uses a stronger ASR teacher to score online student rollouts.
If you find ARK-ASR or open-audio-opd useful, please cite:
import torchfrom transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
model_path = "AutoArk-AI/ARK-ASR-3B"audio_path = "assets/libai.wav"
device = "cuda" if torch.cuda.is_available() else "cpu"torch_dtype = torch.bfloat16 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)git clone https://github.com/AutoArk/open-audio-opdcd open-audio-opdpip install -e .{"audio":"/path/to/audio.wav","text":"","task":"asr","begin_time":-1,"end_time":-1}python scripts/infer/ark_asr_transformers.py \ --input /path/to/input.jsonl \ --output runs/infer/predictions.jsonl \ --model_path AutoArk-AI/ARK-ASR-3B \ --processor_path AutoArk-AI/ARK-ASR-3B \ --batch_size 40 \ --dtype bfloat16 \ --attn_impl sdpagit clone https://github.com/AutoArk/open-audio-opdcd open-audio-opdpip install -e ".[vllm]"MODEL=AutoArk-AI/ARK-ASR-3B \GPU=0 \PORT=8025 \scripts/vllm/deploy_ark_asr_vllm_service.sh startscripts/vllm/deploy_ark_asr_vllm_service.sh statuscurl -sS http://127.0.0.1:8025/healthcurl -sS http://127.0.0.1:8025/token-maskcurl -sS -X POST http://127.0.0.1:8025/asr \ -F file=@/path/to/audio.wav \ -F max_new_tokens=256curl -sS -X POST http://127.0.0.1:8025/v1/audio/transcriptions \ -F file=@/path/to/audio.wav \ -F model=ark-asrscripts/vllm/deploy_ark_asr_vllm_service.sh stoppython scripts/eval/eval_jwer_ark_asr_transformers.py \ --input /path/to/test.jsonl \ --output runs/eval/result.jsonl \ --model_path AutoArk-AI/ARK-ASR-3B \ --processor_path AutoArk-AI/ARK-ASR-3B \ --batch_size 40 \ --dtype bfloat16 \ --attn_impl sdpa@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}}