README.md

metadata

title: Wispr Flow Clone
emoji: 🎙️
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.26.0
python_version: '3.10'
app_file: app.py
pinned: false
short_description: Wispr Flow Clone space

Voice → Clean Text

A Wispr Flow-style pipeline: speak, get a verbatim transcript, then a cleaned-up version with filler words removed and tone adapted to your chosen mode (email / chat / notes).

How it's wired

• ASR (fine-tuned Whisper) — transcribes audio verbatim. It does not rewrite content; its only job is faithful transcription, including disfluencies. This matters: a dictation tool that "corrects" during transcription can silently put words in your mouth.
• Cleanup (fine-tuned LLM) — takes the verbatim transcript and rewrites it: removes filler words and false starts, fixes grammar, adapts tone to the selected mode. All rewriting happens here, where it's visible and controlled by an explicit system prompt.

Running locally

pip install -r requirements.txt
export WHISPER_REPO_ID="aijadugar/wispr-clone-whisper"
export LLM_REPO_ID="aijadugar/wispr-clone-llm"
python app.py

Or just edit the default repo IDs at the top of app.py.

Deploying as a Hugging Face Space

1. Create a new Space (SDK: Gradio).
2. Push app.py, requirements.txt, and this README.md.
3. Set WHISPER_REPO_ID / LLM_REPO_ID as Space variables (Settings → Variables), or hardcode them in app.py.
4. Pick a GPU hardware tier if you want low-latency generation — CPU works but the LLM stage will be noticeably slower.

Training

The models this app loads are produced by the companion training notebook (wf-voice-to-clean-text.ipynb), designed to run on Kaggle's free T4×2 GPU quota. See that notebook for the full fine-tuning + Hub push pipeline.