Lisper Gemma 4 E2B Audio LoRA
This is the Lisper Gemma 4 E2B LoRA adapter for raw-audio lisp coaching.
Lisper is a hackathon prototype for low-pressure /s/ practice. It classifies a short speech clip as clear, frontal, lateral, dental, or palatal, then returns one concise reason, one corrective cue, and one encouragement line.
Model Lineage
- Base model:
google/gemma-4-E2B-it - Fine-tuning: Unsloth supervised fine-tuning with QLoRA / LoRA
- Trainable parameters: about
29.86Mof5.15B - Training rows:
16,000 - Validation rows:
2,000 - Held-out test rows:
2,000 - Training steps:
4,000 - Selected checkpoint:
checkpoint-2500
This is not a dense full-parameter fine-tune. The base model was frozen and the learned update is stored as LoRA adapter weights.
Evaluation
The release-quality evaluation is the v18 hybrid acoustic+Gemma path:
- Held-out rows:
2,000 - Hard errors:
0 - Verdict:
pass - Class match:
0.976 - Clear/non-clear match:
0.989 - Exact four-line format:
1.0 - Reason/cue/encouragement present:
1.0
The evaluated pipeline uses acoustic features for the lisp-class hint and Gemma for structured coaching text and tone. Do not interpret these metrics as a pure direct-Gemma raw-audio classification result.
See eval_summary.json and publish_verdict.json for the public summary.
Companion Artifacts
- Merged full checkpoint:
thomasjvu/lisper-gemma4-e2b-audio-full - Browser q4f16 ONNX/WebGPU package:
thomasjvu/lisper-gemma4-e2b-audio-onnx-q4f16 - Server-side demo Space:
thomasjvu/lisper-zerogpu
Limitations
- The lisp dataset is synthetically generated from speaker-disjoint source speech.
- This is a practice assistant, not a medical diagnosis tool or a replacement for a speech-language pathologist.
- The browser q4f16 package is large for consumer devices, so a ZeroGPU fallback is provided.
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