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Chirp

Local Windows dictation with ParakeetV3 no executable required

Details

External ID
45930659
Source
HN
Company
—
Product
Chirp
Website domain
github.com
Launched
Nov. 14, 2025
Cohort
—
Upvotes
34
Upvotes percentile
0.7729257641921398
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Description

I’ve been working in fairly locked‑down Windows environments where I’m allowed to run Python, but not install or launch new `.exe` files. In addition the built-in windows dictations are blocked (the only good one isn't local anyway). At the same time, I really wanted accurate, fast dictation without sending audio to a cloud service, and without needing a GPU. Most speech‑to‑text setups I tried either required special launchers, GPU access, or were awkward to run day‑to‑day.To scratch that itch, I built Chirp, a Windows dictation app that runs fully locally, uses NVIDIA’s ParakeetV3 model, and is managed end‑to‑end with `uv`. If you can run Python on your machine, you should be able to run Chirp—no additional executables required.Under the hood, Chirp uses the Parakeet TDT 0.6B v3 ONNX bundle. ParakeetV3 has accuracy in the same ballpark as Whisper‑large‑v3 (multilingual WER ~4.9 vs ~5.0 in the open ASR leaderboard), but it’s much faster and happy on CPU.The flow is: - One‑time setup that downloads and prepares the ONNX model: - `uv run python -m chirp.setup` - A long‑running CLI process: - `uv run python -m chirp.main` - A global hotkey that starts/stops recording and injects text into the active window.A few details that might be interesting technically:- Local‑only STT: Everything runs on your machine using ONNX Runtime; by default it uses CPU providers, with optional GPU providers if your environment allows.- Config‑driven behavior: A `config.toml` file controls the global hotkey, model choice, quantization (`int8` option), language, ONNX providers, and threading. There’s also a simple `[word_overrides]` map so you can fix tokens that the model consistently mishears.- Post‑processing pipeline: After recognition, there’s an optional “style guide” step where you can specify prompts like “sentence case” or “prepend: >>” for the final text.- No clipboard gymnastics required on Windows: The app types directly into the focused window; there are options for clipboard‑based pasting and cleanup behavior for platforms where that makes more sense.- Audio feedback: Start/stop sounds (configurable) let you know when the mic is actually recording.So far I’ve mainly tested this on my own Windows machines with English dictation and CPU‑only setups. There are probably plenty of rough edges (different keyboard layouts, language settings, corporate IT policies, etc.), and I’d love feedback from people who:- Work in restricted corporate environments and need local dictation. - Have experience with Parakeet/Whisper or ONNX Runtime and see obvious ways to improve performance or robustness. - Want specific features (e.g., better multi‑language support, more advanced post‑processing, or integrations with their editor/IDE).Repo is here: `https://github.com/Whamp/chirp`If you try it, I’d be very interested in:- CPU usage and latency on your hardware, - How well it behaves with your keyboard layout and applications, - Any weird failure cases or usability annoyances you run into.Happy to answer questions and dig into technical details in the comments.

Enrichment

Theme
voice dictation and control tools
Vertical
Horizontal
Function
Dev tools
Audience
B2C
AI stance
AI feature
Project type
Commercial product
Normalized one-liner
local windows speech-to-text without executables
Manually corrected
False

Could you build this?

Yes This is a Python script utilizing HuggingFace or ONNX runtimes to execute NVIDIA's Parakeet ASR model locally and feed keystrokes into Windows via pywin32 or ctypes.

Discussion

18 comments analyzed.

Competitors mentioned: MacWhisper, Whisper/Whisper.cpp, Windows built-in dictation (Win+H), Fluid Inference CoreML/OpenVINO implementations

Concerns raised: Parakeet accuracy slightly lower than Whisper Large V3 (5.05 vs 4.91 WER), Security/policy contradiction: can't install .exe but can download ~200 wheels with binary blobs, Struggles with technical terms transcription, Configuration complexity for optimal results

Feature requests: macOS equivalent/native app, YouTube subtitle generation with word-level accuracy, Live streaming transcription display, Automatic technical term handling (JSON, proper nouns, etc)

Competitors

Other products that read as similar to this one — 124 launches clear the similarity bar, closest 8 shown.

Attention rank: #29 of 125 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Looks like the first mover among its competitors.

Other launches for this product

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