ARK-ASR-0.6B ONNX

ONNX export of Audio8/ARK-ASR-0.6B for onnx-asr. All credit for the model goes to Audio8 (AutoArk AI). This repository only contains the converted graphs; the weights are the original ones.

The model is a speech-LLM: a Whisper-large-v3-style audio encoder with rotary position embeddings, an MLP adapter that merges four encoder frames into one embedding, and a Qwen2 0.6B causal language model that writes the transcription.

Usage

pip install onnx-asr[cpu,hub]

import onnx_asr

model = onnx_asr.load_model("speech-llm", "OpenVoiceOS/ARK-ASR-0.6B-onnx")
print(model.recognize("audio.wav"))

# int8 weights
model = onnx_asr.load_model("speech-llm", "OpenVoiceOS/ARK-ASR-0.6B-onnx", quantization="int8")

Files

File	Contents
`encoder.onnx`	audio encoder and MLP adapter, log-mel features in, LM embeddings out
`embed_tokens.onnx`	token embedding table
`decoder.onnx`	Qwen2 decoder with KV cache, logits out
`*.int8.onnx`	dynamically quantized int8 weights
`config.json`	model type, prompt token ids, suppressed token ids
`vocab.json`	tokenizer vocabulary for detokenization

Graph contract

Graph	Inputs	Outputs
`encoder.onnx`	`input_features (1, 128, frames)`	`audio_embeds (1, frames/8, 896)`
`embed_tokens.onnx`	`input_ids (1, S)`	`inputs_embeds (1, S, 896)`
`decoder.onnx`	`inputs_embeds (1, S, 896)`, `attn_bias (1, 1, S, P+S)`, `position_ids (1, S)`, `past_key_values.{0..23}.{key,value} (1, 2, P, 64)`	`logits (1, S, 163958)`, `present.{0..23}.{key,value} (1, 2, P+S, 64)`

Accuracy

Four FLEURS clips (2 English, 2 Mandarin), greedy decoding, compared against the PyTorch model in float32:

fp32 ONNX: 4 of 4 transcriptions identical to PyTorch, character for character.
int8 ONNX: 3 of 4 identical. One Mandarin clip stops early and loses the last clause.

Speed on a 12-core CPU under load: RTF 0.35 to 0.61 (fp32) and 0.09 to 0.29 (int8).