ARK-ASR-3B#

ARK-ASR-3B (AutoArk-AI, Apache-2.0) is a multilingual open ASR model served through the OpenAI-compatible /v1/audio/transcriptions endpoint. It accepts one uploaded audio file per request and returns text. Architecturally it is a Whisper-style audio tower (RoPE self-attention) plus an MLP frame-merge adapter feeding a dense Qwen2 LM, so it runs on the same single-stage batched ASR pipeline as Qwen3-ASR and reuses SGLang’s native Qwen2 decoder. The checkpoint’s tokenizer and config are loaded with trust_remote_code=True (the served ServerArgs also sets it), so the first launch will prompt to execute the checkpoint’s bundled code.

Prerequisites#

Install sglang-omni by following Installation, then download the model:

hf download AutoArk-AI/ARK-ASR-3B

Server Configuration#

ARK-ASR runs a single ASR stage on one GPU, in bfloat16 by default.

sgl-omni serve \
  --model-path AutoArk-AI/ARK-ASR-3B \
  --port 8000

Transcribe Audio#

curl -X POST http://localhost:8000/v1/audio/transcriptions \
  -F model=AutoArk-AI/ARK-ASR-3B \
  -F file=@tests/data/query_to_cars.wav \
  -F language=en \
  -F response_format=json

import requests

with open("tests/data/query_to_cars.wav", "rb") as f:
    resp = requests.post(
        "http://localhost:8000/v1/audio/transcriptions",
        data={
            "model": "AutoArk-AI/ARK-ASR-3B",
            "language": "en",
            "response_format": "json",
        },
        files={"file": ("query_to_cars.wav", f, "audio/wav")},
        timeout=300,
    )

resp.raise_for_status()
print(resp.json()["text"])

Request Parameters#

Parameter	Type	Default	Description
`file`	file	required	Audio file uploaded as multipart form data
`model`	string	server default	Model identifier
`language`	string	`en`	Language hint recorded on the request. The transcription instruction is a fixed English prompt (`Please transcribe this audio.`); ARK-ASR auto-detects the spoken language, so this field does not switch prompt templates.
`response_format`	string	`json`	`json`, `verbose_json`, or `text`
`temperature`	float	`0` (greedy)	Sampling temperature. When unset or `0`, SGLang’s sampling normalization selects greedy decoding (`top_k=1`); no non-zero temperature is substituted.

Response Formats#

The three response_format values return different shapes:

text — the raw transcript as text/plain.
json — {"text": "...", "usage": {"type": "duration", "seconds": <int>}}.

verbose_json — the OpenAI verbose shape, built by the default transcription adapter (whole transcript as a single segment):

{
  "task": "transcribe",
  "language": "en",
  "duration": 12.34,
  "text": "...",
  "segments": [{"id": 0, "start": 0.0, "end": 12.34, "text": "..."}],
  "usage": {"type": "duration", "seconds": 13}
}

language and usage are omitted when unknown (exclude_none). If the uploaded audio’s duration cannot be probed, duration and the segment end time are 0.0.

Audio Handling#

Audio is resampled to 16 kHz before feature extraction.
Features use a 128-bin log-mel front end (stock WhisperFeatureExtractor).
The feature extractor truncates at the 30-second Whisper boundary (n_samples = 480000); audio longer than 30 s is truncated to its first 30 s. Mel padding is "longest", so short clips do not pay the full 30 s of FFT.
The audio-token count fed to the LM is (mel_frames + 1) // 2 // merge_factor with merge_factor = 4 (conv2 stride-2 down-sampling, then a 4-frame merge).

dtype#

Default serving dtype is bfloat16. This is the validated path: the native audio-encoder reimplementation was checked for parity against the reference transformers implementation on identical mel inputs.
A float16 path is also exposed. The encoder layers clamp the post-residual activations under fp16 (matching the reference modeling_audio.py) so large activations stay finite; this clamp is a no-op under bfloat16.

Marker-Token Suppression#

The stock checkpoint ships no bad_words_ids, so plain skip_special_tokens=True decoding can leak non-special added markers (e.g. <tool_call>, <|audio|>) on adversarial / OOD audio. The request builder defensively suppresses every reserved marker (all special + <...>-added ids except EOS) at sampling via logit_bias and strips them on decode. This is a verified no-op on clean speech.

Benchmarking#

Use benchmarks/eval/benchmark_asr_seedtts.py to sweep ASR concurrency on SeedTTS reference audio through /v1/audio/transcriptions. Pass --model-path AutoArk-AI/ARK-ASR-3B; the shared request and metric logic lives in benchmarks.tasks.asr.

sgl-omni serve --model-path AutoArk-AI/ARK-ASR-3B --port 8000

# Sweep the full SeedTTS EN set (1088 clips) at 1..64 concurrency, 3 repeats:
python -m benchmarks.eval.benchmark_asr_seedtts \
  --port 8000 --model-path AutoArk-AI/ARK-ASR-3B \
  --concurrencies 1,2,4,8,16,32,64 --repeats 3 --warmup

The script reports corpus WER, throughput, and latency per concurrency level. Transcription accuracy tracks the official transformers checkpoint on the same audio.

Known Limitations#

The endpoint accepts one uploaded file per request.
prompt is accepted by the HTTP endpoint for OpenAI compatibility, but ARK-ASR currently ignores it (the transcription instruction is fixed).
Audio is resampled to 16 kHz and truncated at 30 s before transcription.

ARK-ASR-3B

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