ONNX (CPU) version of
lilfugu for Linux / Windows / macOS. Runs without CUDA or MLX. See the main model card for details.
Encoder FP32 + decoder INT8 dynamic quantization, following the split used in
Daumee/Qwen3-ASR-0.6B-ONNX-CPU. The FP32 decoder variants are included alongside for reference.
1pip install -U "huggingface_hub[cli]" onnxruntime numpy soundfile librosa transformers torch
2hf download holotherapper/lilfugu-onnx --local-dir lilfugu-onnx
3python3 lilfugu-onnx/inference.py audio.wav
The package follows the same split / file naming convention as
andrewleech/qwen3-asr-onnx (
encoder.onnx,
decoder_init*,
decoder_step*,
embed_tokens.bin), so tooling built around that layout should work with little or no change.
Composite = 0.4 × (1 - CER) + 0.6 × Term Accuracy (includes exact and flexible matches). Benchmark:
ADLIB. Scores above were measured on Apple Silicon. CPU architecture and onnxruntime build can shift token-level results, so validate on your target hardware if benchmark parity matters.
INT8 dynamic quantization costs roughly 0.05 on Composite vs the MLX build. Switch to the FP32 decoder variants (--variant fp32) if that drop matters for your use case.
Peak memory ~13 GB (INT8) / ~22 GB (FP32). Plan for 24 GB+ on desktops; 16 GB only works dedicated.
