---

license: other

license_name: see-base-model

license_link: https://huggingface.co/AutoArk-AI/ARK-ASR-3B

base_model: AutoArk-AI/ARK-ASR-3B

library_name: gguf

pipeline_tag: automatic-speech-recognition

tags:

- automatic-speech-recognition

- asr

- gguf

- whisper

- qwen2

- crispasr

- ggml

language:

- zh

- en

- de

- ja

- fr

- ko

- es

- pl

- it

- ro

- hu

- cs

- nl

- fi

- hr

- sk

- sl

- et

- lt

---

ARK-ASR-3B — GGUF

GGUF conversions of AutoArk-AI/ARK-ASR-3B,

a 19-language automatic speech recognition model, for use with

CrispASR (a ggml/llama.cpp-style C++ engine).

> ⚠️ Experimental / work-in-progress. Core ASR is validated and accurate (see

> below), but the backend has known rough edges — read Limitations before relying on it.

Architecture

ARK-ASR-3B = Whisper-large-v3 encoder with partial rotary position embeddings

→ MLP adapter → Qwen2.5-3B decoder with audio-token injection. A single

self-contained GGUF holds the encoder, adapter, and language model.

• Encoder — Whisper-large-v3 conv stem + 32 layers, but Whisper's learned

positional embeddings are replaced by partial interleaved RoPE (rotates the

first 32 of 64 head dims, θ=10000, on Q and K; k_proj has no bias; the

encoder's own final LayerNorm is dropped). Because positions are rotary, the

encoder handles arbitrary-length audio in one pass — there is no 30 s cap.

• Adapter — LayerNorm → merge 4 consecutive frames → Linear 5120→4096 → GELU

→ Linear 4096→2048.

• Decoder — stock Qwen2.5-3B (2048 hidden, 36 layers, GQA 16Q/2KV, SwiGLU,

RMSNorm, θ=1e6, tied embeddings). The <|audio|> placeholder embeddings are

overwritten by the adapter's audio features, then the transcript is decoded.

Mel features use the stock WhisperFeatureExtractor recipe (128 bins, n_fft 400,

hop 160).

Files

| File | Size | Notes |

|------|------|-------|

| ark-asr-3b-f16.gguf | 7.0 GB | Full precision. Reference quality. |

| ark-asr-3b-q8_0.gguf | 4.0 GB | Recommended. Near-lossless; validated against the F16/PyTorch reference. |

| ark-asr-3b-q4_k.gguf | 3.3 GB | Smallest. Encoder/adapter/embeddings kept in F16, only the Qwen2 decoder body is Q4_K. Verbatim on test clips. |

All three transcribe the JFK sample verbatim. Q8_0 is the best size/quality trade-off.

Usage (CrispASR)

# Build CrispASR, then:
crispasr -m ark-asr-3b-q8_0.gguf --backend ark-asr -f audio.wav

# Optional best-effort language hint (the model is promptless; see Limitations):
crispasr -m ark-asr-3b-q8_0.gguf --backend ark-asr -l de -f audio.wav

# Force CPU (GPU is the default):
CRISPASR_ARKASR_CPU=1 crispasr -m ark-asr-3b-q8_0.gguf --backend ark-asr -f audio.wav

-m auto --backend ark-asr also resolves these files via the model registry.

Validation

The C++ port was checked stage-by-stage against the original PyTorch model

(trust_remote_code, bf16) with CrispASR's diff harness on the JFK clip:

| Stage | Cosine vs. reference |

|-------|----------------------|

| log-mel spectrogram | 0.999993 |

| first decoder logits (Q8_0) | 0.999646 |

| audio embeddings (Q8_0, mean) | 0.999445 |

End-to-end the transcript matches the reference verbatim (English and German tested).

Limitations (WIP)

• Whole-audio by default. Matching the reference, CrispASR encodes the entire

clip in one pass (no chunking). Very long files fall back to internal 30 s

chunking above a cap (CRISPASR_ARKASR_MAX_SINGLE_PASS_S, default 300 s) to

bound memory; chunked segments can re-detect language independently, so pass

--vad or raise the cap for long multilingual audio.

• Promptless language steering. The model has no language parameter; -l

injects a best-effort "Transcribe the audio in <Language>." instruction, but

the model was not instruction-trained, so it is not a hard guarantee.

• GPU: default and validated on Apple Metal (verbatim; ~5.6× faster prefill,

roughly neutral per-token decode on unified memory). CUDA is not yet validated —

use CRISPASR_ARKASR_CPU=1 if you hit issues.

Attribution & license

Base model: AutoArk-AI/ARK-ASR-3B

(on-policy distilled, THU-NLP). These GGUFs are a community conversion; the

license follows the base model — check the upstream repository for terms.

Conversion and the CrispASR ark-asr backend by CrispStrobe.