A works-demonstration that the zero-parameter byte-signal substrate hsl-embedding-zero, plus a model-side spectral lens (log-mel + a learnable gated fusion over the lossless HSL substrate), wired into a plain character-CTC ASR baseline, transcribes speech — and that the lens is what makes it work.

Code / reproduce: https://github.com/Woojiggun/HoLo-ToLk-STT
Author: Jinhyun Woo (ggunio5782@gmail.com) · License: CC BY-NC 4.0 (non-commercial; the HSL substrate is separately MIT)

The honest claim

Pure hsl-embedding-zero fed straight to a char-CTC ASR baseline is weak (CER ~0.67). Adding a model-side spectral lens flips it: CER 0.194, beating the mel-spectrogram baseline (0.213) in the same setup, confirmed across 4 seeds (hslspec-gate < mel on every seed; mean 0.193, range 0.002). HSL is a raw substrate — alone it underperforms, but with the right lens its performance changes. This is a feasibility / works demonstration (8 kHz, no language model, char-CTC) — readable but rough in absolute terms, NOT a competitive/SOTA ASR product.

front-end	held-out CER
`hsl` (substrate only)	0.673
`hslwav` (substrate + time-domain)	0.709
`mel` (spectral only)	0.213
`hslspec` + gated fusion	0.194

Multi-seed (hslspec+gate vs mel): seeds 0–3 = 0.194 / 0.194 / 0.194 / 0.192 vs 0.213 / 0.208 / 0.342* / 0.210 (*mel seed 2 unstable; hslspec still beats the good mel seeds). Fusion gate learned 0.1 → 0.26.

Intended use

Research / educational demonstration that a lossless byte substrate + a model-side spectral lens > mel in a controlled char-CTC ASR setup; and a worked example of feeding bytes through the zero-parameter HSL encoder.
A starting point for experiments with tokenizer-free / byte-native signal front-ends.

Not intended for production transcription, captioning, or any use needing accurate text.

How it works

mu-law audio bytes (8 kHz) → two paths: (a) the frozen 27-D hsl-embedding-zero derivative substrate → ConvSub; (b) a parameter-free log-mel spectral lens → projection. Fused as lens + gate · substrate (learnable gate, init 0.1) → Pre-LN Transformer encoder (8 layers, dim 384) → char-CTC head (29 symbols) → greedy decode, no language model. See the repo README and asr_lens.py for the exact architecture.

Training data

LibriSpeech train-clean-100 (≈28.5k clips, 100h) for training; dev-clean (≈2.6k) held out — official splits, license CC BY 4.0. Audio resampled to 8 kHz, mu-law encoded. The dataset is not redistributed; rebuild it with collect_libri.py (streams from openslr/librispeech_asr).

Training procedure

char-CTC, Pre-LN Transformer, SortaGrad short-clips-first. dim 384 / layers 8 / heads 6 / ff 1536, batch 24, AdamW lr 4e-4 (cosine, 800-step warmup), 12k steps, grad-clip 5.0, seed 0. The reported checkpoint is the best held-out CER over training.

Evaluation

CER / WER on the full dev-clean held-out set (n = 2642), greedy CTC decode, no LM. Reported: CER 0.194 / WER 0.535 for hslspec + gate (seed 0).

Limitations

8 kHz / no LM / char-CTC ⇒ output is readable but garbled; expect substitutions and malformed words. Not usable where accurate text matters.
English read-speech (LibriSpeech) only; not evaluated on other languages, noisy/far-field audio, spontaneous speech, or other sampling rates.
The contribution is a controlled comparison (substrate + lens > mel, same setup, multi-seed), not state-of-the-art accuracy.

Files / checkpoint to upload

Upload the frozen seed-0 winner to this model repo:

asr_lens_best_hslspec_gate.pt — hslspec + gate, step 12k, seed 0, CER 0.194 (~200 MB). Self-contained: stores its own config + vocab, so asr_lens.py rebuilds the model on load.

Load & score:

bash

pip install hsl-embedding-zero zstandard numpy torch torchaudio
python asr_lens.py --eval-only asr_lens_best_hslspec_gate.pt --data ./data/librispeech100

Acknowledgments

Independent research, developed in collaboration with AI assistants — Claude Code (Anthropic) and Codex. The HSL work and experimental direction are the author's; the tools assisted with engineering and review.

Citation

bibtex

@software{woo_holotolk_stt_2026,
  author = {Jinhyun Woo},
  title  = {HoLo-ToLk (STT): an HSL-lens speech-to-text feasibility model},
  year   = {2026},
  doi    = {10.5281/zenodo.21004333},
  url    = {https://github.com/Woojiggun/HoLo-ToLk-STT}
}

ggunio/HoLo-ToLk-STT

HoLo-ToLk (STT) — an HSL-lens speech-to-text feasibility model