Audio8-ASR

The iOS release is designed for local iPhone transcription with roughly 200 MB peak runtime memory footprint, depending on device, iOS version, and workload.

Demo Video

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Evaluation Results

The Open ASR results use the seven current public splits from hf-audio/open-asr-leaderboard at dataset revision b6bdcd0beb34f8975dc659796176d88f43aff502. They were measured with the standalone Transformers package on standardized H200 Hugging Face Jobs using BF16, eager attention, greedy decoding, max_new_tokens=256, and the documented 30-second audio cap. Per-split batch sizes were 1152, 1024, 1408, 1024, 1024, 2048, and 628. Raw manifests are stored in hf://buckets/AutoArk-AI/audio8-asr-open-asr-results, and the corresponding machine-readable results are provided in .eval_results/open_asr_leaderboard.yaml.

The internal canonical WenetSpeech results come from the reproducibility-checked teacher0p6B-step3000 export with batch size 128. Its effective model tensors are byte-identical to this standalone release; the release only removes a redundant tied LM-head tensor and packages the same weights for standalone use. Chinese results are reported as character error rate. AISHELL is intentionally excluded from this table.

Model Overview

• Task: automatic speech recognition
• Checkpoint format: safetensors
• Sampling rate: 16 kHz
• Decoder: 8-layer Qwen-style causal LM
• Audio front end: Qwen3-ASR audio encoder plus MLP adapter/projector
• Language-model parameters: 103,502,336 (about 0.104B)
• End-to-end unique parameters: 323,990,528 (about 0.324B)
• Runtime: Hugging Face Transformers
• Hotwords: optional decode-time logit boosting, no fine-tuning required

The model should be loaded with trust_remote_code=True.

Files

• config.json, tokenizer files, processor files, and model.safetensors
• configuration_arkasr.py, modeling_arkasr.py, processing_arkasr.py
• qwen3_asr_audio_config.py, qwen3_asr_audio_model.py
• hotword/: backend-agnostic hotword trie
• examples/: Transformers inference examples

The root config.json is intentionally kept in this repository so Hugging Face can recognize the model package and count downloads through normal model-file queries.

Transformers Inference

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

model_path = "AutoArk-AI/Audio8-ASR-0.1B"
audio_path = "path/to/audio.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)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype=torch_dtype,
    attn_implementation="eager",
).to(device)
model.eval()

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "audio", "path": audio_path},
            {"type": "text", "text": "Please transcribe this audio."},
        ],
    }
]

batch = processor.apply_chat_template(
    conversation,
    return_tensors="pt",
    sampling_rate=16000,
    audio_padding="longest",
    add_generation_prompt=True,
    audio_max_length=30 * 16000,
    text_kwargs={"padding": "longest", "truncation": True, "max_length": 1000},
)
batch = {key: value.to(device) if hasattr(value, "to") else value for key, value in dict(batch).items()}

with torch.inference_mode():
    output_ids = model.generate(**batch, max_new_tokens=128, do_sample=False)

prompt_len = int(batch["input_ids"].shape[1])
text = processor.decode(output_ids[0, prompt_len:], skip_special_tokens=True).strip()
print(text)

Equivalent script:

python examples/transcribe.py path/to/audio.wav --model AutoArk-AI/Audio8-ASR-0.1B

For local staging before upload:

python examples/transcribe.py path/to/audio.wav --model .

Hotword Boosting

Hotwords are applied at decode time by nudging logits for tokenizer paths that match the requested words. Th