parrotia 1.1.0

Fast, free, fully local audio/video transcription powered by Whisper.

Fast, free, fully local audio and video transcription powered by Whisper.
No internet connection, accounts, or API keys required — everything runs on your machine.

---

Features

• State-of-the-art models — tiny → large-v3, plus the fast large-v3-turbo and distil-large-v3 variants
• Any language — auto-detect, or pin a specific language for better accuracy
• Single file or whole folder — transcribe one file, or point the app at a folder to batch every audio/video file inside it
• Multiple export formats — txt, md, srt, vtt, json — pick any combination
• CPU and GPU support — auto-detects your hardware; gracefully falls back to CPU if the GPU is unavailable
• Responsive UI — transcription runs in a background thread with a live progress bar and a cancel button; the window never freezes
• No ffmpeg needed — audio is decoded via the bundled PyAV
• Headless CLI — same engine, scriptable for batch jobs
• Model benchmarking — time several models on one file and compare their transcription speed (CLI)

---

Preview

---

Requirements

• Python 3.9 or later
• pip

---

Installation

Install the package (provides the parrotia and parrotia-gui commands):

pip install parrotia

Or install from a checkout of this repository:

pip install .
# For development (editable install):
pip install -e .

Prefer not to install? You can still run straight from the source tree with pip install -r requirements.txt and the launchers below.

The first time you use a model it is downloaded automatically (a few hundred MB to ~1.5 GB depending on the model) and cached for offline use afterwards.

Optional: NVIDIA GPU acceleration

For much faster transcription on an NVIDIA GPU, install the CUDA 12 runtime wheels — no system-wide CUDA or cuDNN install required:

pip install "parrotia[cuda]"

The app discovers these automatically at startup. Set Device → cuda in the GUI (or pass --device cuda on the CLI). If the GPU is unavailable for any reason it falls back to CPU transparently.

---

Usage

GUI

parrotia-gui
# or, without installing:  python -m parrotia.app

Or use the platform launcher (runs from source; edit the PYTHON variable inside if needed):

Platform	Launcher
Windows	double-click run.bat
macOS / Linux	chmod +x run.sh && ./run.sh

1. Pick a single audio/video File…, or a Folder… to transcribe every supported file inside it
2. Choose a model, language, device, and output formats
3. Click Transcribe — each file's outputs are saved next to it (or in a folder you choose) and previewed in the window; a batch reports overall progress and a per-file summary at the end

Command line

parrotia "talk.mp3" --model large-v3-turbo --formats txt srt --language en
# or, without installing:  python -m parrotia "talk.mp3" ...

usage: parrotia [-h] [--model MODEL] [--language LANGUAGE] [--device DEVICE]
                [--compute COMPUTE] [--formats FORMAT [FORMAT ...]] [--outdir OUTDIR]
                audio

positional arguments:
  audio                 Path to the audio/video file

options:
  --model               Whisper model to use (default: large-v3-turbo)
  --language            Language code, e.g. en, pt (omit to auto-detect)
  --device              cpu | cuda | auto (default: auto)
  --compute             int8 | float16 | float32 | auto (default: auto)
  --formats             One or more output formats: txt md srt vtt json
  --outdir              Output folder (defaults to the source file's folder)
  --benchmark           Time several models on the file and print a speed
                        comparison instead of transcribing
  --models              Models to compare in --benchmark mode (default: all)

Benchmarking model speed

Compare how fast different models transcribe the same file — useful for picking the best speed/accuracy trade-off for your hardware:

parrotia "talk.mp3" --benchmark --models tiny base small large-v3-turbo

This loads and runs each model in turn, then prints a table reporting model load time, decode time, and speed — the real-time factor (seconds of audio transcribed per second of wall-clock time; higher is faster):

Model                 Audio     Load   Decode     Speed  Segments
-----------------------------------------------------------------
tiny                  78.8s     3.1s     6.6s      11.9x        26
base                  78.8s     9.4s     2.8s      28.0x        24
small                 78.8s     5.2s     7.1s      11.1x        25
large-v3-turbo        78.8s    12.0s     9.3s       8.5x        23

Fastest: base — 28.0x real-time (2.8s to decode 78.8s of audio)

Omit --models to benchmark every available model. The report is also written to <file>.benchmark.txt and <file>.benchmark.json next to the source (or in --outdir). Each model is loaded fresh so download/warm-up time is reflected honestly; a model that fails to run is recorded and the rest continue.

---

Supported formats

Format	Extension	Description
txt	.txt	Plain transcript, one segment per line
md	.md	Markdown document with metadata header and timestamps
srt	.srt	SubRip subtitles
vtt	.vtt	WebVTT subtitles
json	.json	Structured JSON with full metadata and per-segment timings

---

Model guide

Model	Speed	Accuracy	Notes
tiny / base	Fastest	Lower	Quick drafts, low-resource machines
small / medium	Balanced	Good	Solid everyday choice
large-v3	Slowest	Best	Highest accuracy
large-v3-turbo	Fast	Great	Recommended default
distil-large-v3	Fast	Great	English-focused, compact

All models are free and run entirely on-device.

---

Supported input formats

.mp3 · .wav · .m4a · .ogg · .opus · .flac · .aac · .wma · .mp4 · .mkv · .mov · .avi · .webm

---

Project structure

File	Purpose
src/parrotia/app.py	customtkinter GUI
src/parrotia/transcriber.py	Whisper engine (model cache, progress callbacks, cancellation, GPU fallback)
src/parrotia/formats.py	txt / md / srt / vtt / json writers
src/parrotia/cli.py	Headless command-line interface
src/parrotia/benchmark.py	Model speed benchmarking (CLI)

---

Use as a library

The transcription engine is UI-agnostic and importable:

from parrotia import Transcriber, WRITERS

result = Transcriber().transcribe("talk.mp3", model="large-v3-turbo", language="en")
print(WRITERS["srt"][1](result))   # render SRT subtitles as a string

---

Releases

Tagged versions are built into a wheel + source distribution and published as a GitHub Release automatically by the Release workflow. To cut a release, bump __version__ in src/parrotia/__init__.py, update CHANGELOG.md, then push a matching tag:

git tag v1.0.0
git push origin v1.0.0

---

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

---

License

AGPL-3.0