Exeaon1-Voice-0.8B

Speech recognition, compressed with E-PURE. Runs with the free epure-runtime and stays compressed in memory — the dense weight is never assembled.

Base model	openai/whisper-large-v3-turbo
Size on disk	0.44 GB (base 1.51 GB)
Compression	3.43x
Bits per weight	4.22 (measured index entropy)
Compensated layers	233 of 233 — none fell back to plain rounding
Format	.ebin

Quality

Word error rate, not perplexity: perplexity is meaningless for ASR and file size proves nothing. Both models saw identical audio and decoded greedily, so the only variable is the weights.

	WER
whisper-large-v3-turbo	5.20%
Exeaon1-Voice-0.8B	5.20%
token disagreement	0.12%

No measurable degradation. The original's WER is reported alongside because a compressed model can only be judged against what the model could do in the first place — if the original errs on a clip, the compressed one repeating that error is not damage we caused.

Sample, same clip, both models:

ref   MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO
      WELCOME HIS GOSPEL
base  Mr. Quilter is the apostle of the middle classes, and we are glad to
      welcome his gospel.
ours  Mr. Quilter is the apostle of the middle classes, and we are glad to
      welcome his gospel.

Why audio compresses this well

Whisper is an encoder-decoder: 32 encoder layers carry most of the parameters and 4 decoder layers the rest. Both stacks are compensated — compressing only the larger one would leave an eighth of the model on plain rounding.

Calibration uses real speech. The encoder's activations are dominated by mel-spectrogram structure that random input does not reproduce, so noise calibration would compensate against statistics the model never sees.

Convolutional weights in the audio frontend are left dense: conv1.weight is [1280, 128, 3], a kernel of 3 against a group size of 128, which cannot be usefully quantized and is a negligible share of parameters.

Usage

pip install epure-runtime

from epure import load

model, proc = load("Exeaon/Exeaon1-Voice-0.8B")

The container bundles preprocessor_config.json, so the feature extractor builds without fetching anything from the base repository.

Limitations

• Evaluated on read English speech (LibriSpeech-style). Accented, noisy, overlapping or non-English audio is not covered by the number above.
• WER was measured on a small clip set; treat 5.20% as an indicative figure on clean speech, not a benchmark-suite result.
• Inherits every limitation and bias of the base model.
• Not evaluated for safety-critical, medical or legal transcription.

Licence and attribution

Derived from openai/whisper-large-v3-turbo, released under the MIT licence, which permits redistribution of modified versions. The base repository ships no LICENSE file; the licence is declared in its model card metadata and is reproduced in this repository.

Ours: the compression method, calibration, packaging, runtime. Not ours: the pretrained knowledge, which comes from OpenAI. This model is not endorsed by or affiliated with OpenAI.

Citation

@misc{exeaon2026,
  title  = {Exeaon: compressed models that run, and train, without
            decompressing},
  author = {Akpalu, Elliot Elikplim},
  year   = {2026},
  publisher = {Zenux Plimver Technologies LTD},
  url    = {https://huggingface.co/Exeaon}
}

---

_{Zenux Plimver Technologies LTD, Ghana}