Wyoming has no authentication, so buffered audio is attacker-controlled.
self.audio grew until AudioStop with no bound: measured at ~11 MB/s over
loopback, one connection exhausts 32 GB in under an hour, and a stuck
satellite that never sends AudioStop does the same by accident. Cap it at
--max-audio-seconds (default 120), dropping the excess with a single warning
while still transcribing what was captured. Verified: a client streaming
10.8 GB now moves server RSS by 213 MB rather than 10.8 GB.
Transcripts were logged at INFO. Log files are long-lived and world-readable
under /tmp on macOS, so every voice command sat in plaintext readable by any
local account. INFO now records duration, latency and character count; the
text moved behind --debug.
Both are covered by mutation-checked tests, and the README gains a Security
section covering the unauthenticated trust boundary, the 0.0.0.0 bind that
also exposes VPN interfaces, and running the daemon as a non-admin user.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Give the exact command behind every table row, document the whisper.cpp
server setup those rows need (including why -nc matters for a fair
comparison), and state the venv/model cost plus cleanup.
Call out that bare 'python3' is the system 3.9 on macOS and fails with
'No matching distribution found for parakeet-mlx' -- verified.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Measures median latency, strict exact-match accuracy, digit formatting and
silence behaviour across 48 clips (16 Home Assistant commands x 3 macOS TTS
voices), on an M4 Mac mini.
Headline: Parakeet v2 at 102ms median is 5x faster than the best whisper.cpp
configuration and lands within one clip of it on accuracy. mlx-whisper
large-v3 is the only backend to score 48/48, at 11x the latency. Moonshine is
2x faster again but gives up real accuracy (35/48).
Also quantifies the reason this defaults to v2 over v3: v3 returned digits
for only 10 of 21 number-bearing commands, against 21/21 for v2, which is
most of the gap between their exact-match scores.
The harness feeds audio to every backend as an array rather than a path --
mlx-whisper and moonshine otherwise shell out to ffmpeg, which this project
deliberately does not require.
Clips are gitignored; bench/make_clips.sh regenerates them. Caveats are
documented in the README: this is clean synthetic TTS, so it measures latency
rigorously and accuracy only as a domain smoke test, and faster-whisper is
CPU-only on Apple Silicon because CTranslate2 has no Metal backend.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Loads parakeet-mlx in-process (no HTTP hop) and serves it over the Wyoming
protocol. On an M4 Mac mini this transcribes typical voice commands in ~110ms
versus ~1150ms for a whisper.cpp large-v3 setup, with identical accuracy on a
ten-command benchmark.
Two behaviours matter beyond speed: silence returns an empty string rather
than whisper's "Thank you." hallucination, and there is no decoder context
carried between requests.
Notable implementation details, all covered by mutation-checked regression
tests:
- MLX streams are thread-local, so the model is loaded and evaluated on a
single dedicated worker thread. Splitting those raises
"There is no Stream(cpu, 1) in current thread".
- parakeet_mlx.load_audio() shells out to ffmpeg, which is unnecessary here
since Wyoming delivers 16kHz mono PCM. The mel is built directly via
get_logmel(), whose input must be float32 -- it views the complex STFT
output as the input dtype, so anything narrower doubles the mel bin count.
- Wyoming's run loop has no except clause, so an exception escaping
handle_event closes the connection without sending a Transcript and Home
Assistant waits indefinitely. Failures are caught and returned as an empty
transcript instead.
Defaults to parakeet-tdt-0.6b-v2 rather than the newer multilingual v3
because v2 emits digits ("21 degrees") where v3 spells numbers out, and
Home Assistant's local intent matching expects digits.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
aharrison-fullerandClaude Opus 5