Unlike conventional ASR systems that transcribe every disfluency, this model performs intent-focused speech understanding—filtering repetitions, prolongations, and blocks to produce clean, intended language.

✨ Core Capabilities

Stutter-Awareness
Handles repetitions, prolongations, and speech blocks natively.
Intent Extraction
Identifies the speaker’s intended words rather than literal disfluent output.
Multimodal Intelligence
Uses Gemma 4’s audio encoder to interpret timing, tone, and structure of speech.

📦 Model Weights (GGUF)

File	Description
`voiceown-base-Q4_K_M.gguf`	Mobile-optimized
`voiceown-base-Q8_0.gguf`	Higher-fidelity evaluation build
`gemma-4-e2b-it.BF16-mmproj.gguf`	Required multimodal projector (core logic)

🧪 Training Insights

Objective: Intent-accurate output from disfluent speech
Dataset: naazimsnh02/voiceown-stutter-asr
Samples: 2,850 real-world recordings
Epochs: 2
Training Loss: 1.1124
Hardware: NVIDIA A100-SXM4
Training Time: ~60 minutes

⚙️ Usage

Run with llama.cpp multimodal CLI:

bash

./llama-mtmd-cli \
  -m voiceown-base-Q4_K_M.gguf \
  --mmproj gemma-4-e2b-it.BF16-mmproj.gguf \
  --audio user_clip.wav \
  -p "Capture the speaker's intended words, ignoring any stutters."

naazimsnh02/voiceown-base-gguf

VoiceOwn — Stutter-Aware Intelligence

✨ Core Capabilities

📦 Model Weights (GGUF)

🧪 Training Insights

⚙️ Usage

naazimsnh02/
voiceown-base-gguf