cstr/ark-asr-3b-GGUF

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

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:

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.