🚀 Introducing Aero-1-Audio — a compact yet mighty audio model.

🧠 Built on Qwen-2.5-1.5B
⚡ Trained in <24h on just 16×H100
🎧 Handles 15+ min audio seamlessly
💡 Outperforms bigger models like Whisper, Qwen-2-Audio & commercial services from ElevenLabs/Scribe

Aero shows: smart data > massive scale.

Github Repo: https://github.com/EvolvingLMMs-Lab/Aero-1
Model Checkpoints: https://huggingface.co/lmms-lab/Aero-1-Audio-1.5B
Evaluation Results: https://github.com/EvolvingLMMs-Lab/lmms-eval/tree/dev/aero
Cookbook: https://www.lmms-lab.com/posts/lmms-lab-docs/aero_audio/

Evaluation Result

20250424_092927_results.json
20250421_203304_results.json
20250421_202840_results.json
20250421_170326_results.json

*Note: for some benchmarks, we use gpt-4o-2024-11-20 as judge model

Examples

We supports batch evaluation for faster inference. Notice that the result might be slightly difference for different batch size

TASK=open_asr_tedlium
CKPT_PATH=lmms-lab/Aero-1-Audio
echo $TASK
TASK_SUFFIX="${TASK//,/_}"
echo $TASK_SUFFIX

accelerate launch --num_processes 8 --main_process_port 30000 -m lmms_eval \
    --model aero \
    --model_args pretrained=$CKPT_PATH,attn_implementation="flash_attention_2" \
    --tasks $TASK \
    --batch_size 32 \
    --log_samples_suffix $TASK_SUFFIX \
    --output_path ./logs/ --verbosity DEBUG