turbospeak-correction-model – AI Model by solaarphunk | AlphaNeural AI

You can deploy this model and start earning money today!

solaarphunk/
turbospeak-correction-model

like

0

text-generation

Qwen/Qwen3-1.7B

endpoints_compatible

Views

No views yet

TurboSpeak Correction Model

Fine-tuned Qwen3-1.7B (Q4_K_M quantization) for cleaning up speech transcription output.

What it does

Removes filler words (um, uh, like, you know, basically)
Fixes stutters (w-w-want → want)
Resolves mid-sentence self-corrections (speaker says X then corrects to Y → keeps only Y)
Preserves all content words — never adds words the speaker didn't say

Performance

Metric	Score
Correction accuracy	87.5% (35/40 P+G)
Filler/stutter handling	100%
Avg latency	~100ms on Apple Silicon
Model size	1.0 GB (Q4_K_M)

Training

Base model: Qwen/Qwen3-1.7B
Fine-tuning: LoRA (rank=8, lr=5e-5, 500 iterations)
Training data: 2,390 examples (1,710 base + 680 hard corrections)
Quantization: Q4_K_M via llama.cpp

Usage

Used by TurboSpeak macOS dictation app. Runs locally via llama.cpp / llama-cpp-2 Rust bindings.

System prompt (ChatML format)

Clean up the transcribed text. Remove filler words, fix stutters, and resolve mid-sentence corrections. Output only the cleaned text.

License

Apache 2.0 (same as base Qwen3 model)