sttop

Live speech-to-text monitor for the terminal — htop, but for what is being said.

Taps your microphone and your system audio output as two independent streams, transcribes both in real time, labels who is speaking, and appends every line to a Markdown file as it happens. Fully local: no network, no API keys, nothing leaves the machine.

Why two streams

Capturing the mic and the speaker output separately means you are identified for free — anything on the mic is you, no model required, never wrong. Voice embeddings then only have to split the remote side into individual participants, which is a much easier problem than diarizing a single mixed track.

Install

uvx --index https://download.pytorch.org/whl/cpu sttop

The --index flag matters. Speaker labelling needs torch, and the stock PyPI torch bundles CUDA — about 2.5GB of nvidia wheels that buy nothing here, since CTranslate2 has no ROCm backend and CPU inference keeps up with live audio fine. The flag points torch at the CPU builds and falls back to PyPI for everything else.

That is the whole install on Linux. There is nothing to apt install first: if the machine has no ffmpeg, sttop fetches a static one into its own data directory on first run, and pactl is optional — the audio server resolves the default mic and monitor itself, so pactl is only needed to pick a source by name. Run sttop doctor to see what it found.

macOS needs nothing installed either. The mic comes from AVFoundation and system audio from ScreenCaptureKit — no BlackHole, no Multi-Output Device, and your output device and volume keys keep working, because nothing is rerouted. It does need permission, granted once to the terminal you run sttop in:

System Settings → Privacy & Security → Screen & System Audio Recording

Restart the terminal afterwards; macOS only re-reads that permission at launch. Requires macOS 13 (Ventura) or newer — on anything older, or before the permission is granted, sttop records the mic only and says so rather than refusing to start.

To install it permanently rather than running it ad hoc:

uv tool install --index https://download.pytorch.org/whl/cpu sttop

From a checkout, uv sync reads the CPU index out of pyproject.toml already:

git clone https://github.com/v4rgas/sttop && cd sttop
uv sync

Use

uv run sttop                       # record with defaults
uv run sttop -t "standup"          # title the session (used in the filename)
uv run sttop --backend whisper -m small
uv run sttop devices --test        # list audio sources, record 1s from each
uv run sttop doctor                # check the audio deps, explain anything missing
uv run sttop sessions              # list past transcripts
uv run sttop config                # write ~/.config/sttop/config.toml
uv run sttop theme                 # show the detected terminal colour scheme

Keys: q quit · space pause · r rename a speaker.

A rename is retroactive — spk1=Ana relabels the live view and rewrites every line already written to the Markdown file, so you can name people once you recognise them rather than before you start.

Output

One Markdown file per session in ~/.local/share/sttop/sessions/, flushed after every line — kill it mid-meeting and the transcript so far is already on disk.

# standup

- started: 2026-08-10 14:32:01 -04
- mic: `alsa_input.pci-0000_08_00.6.analog-stereo`
- system: `alsa_output.pci-0000_08_00.6.analog-stereo.monitor`
- backend: `parakeet-tdt/cpu onnx`

## Transcript

- `03:58` **you** — so the migration lands friday?

- `04:02` **spk1** — friday is tight, monday is safer

How it works

mic     (pulse / avfoundation)  ─┐
                                 ├─ webrtcvad ─→ queue ─→ parakeet ─→ ecapa ─→ journal.md
system  (monitor / ScreenCaptureKit) ─┘

The mic is always an ffmpeg subprocess. System audio is one too on Linux, where the monitor source is just another pulse device; on macOS it is an in-process ScreenCaptureKit stream, converted to the same 16 kHz mono frames before it reaches the segmenter, so everything downstream sees one format.

Audio is cut into utterances by voice-activity detection (a segment closes after 700 ms of silence, or at 15 s for a monologue), and only speech reaches the model.

One thread boundary, and it is the model. The two capture readers and the consumer are asyncio tasks — they are blocking pipe I/O, which is what an event loop is for — while transcription and voice embedding run in a single-worker ThreadPoolExecutor. So the UI needs no cross-thread marshalling, shutdown is ordinary task cancellation, and utterances stay in the order they were spoken. The executor is single-worker on purpose: transcription is CPU-bound and already internally parallel, so a second worker would only thrash the cache. When it falls behind, the queue absorbs the lag — visible as queue N in the status bar — rather than dropping audio.

Speaker labels come from online clustering of ECAPA-TDNN voice embeddings: each utterance is embedded as overlapping windows, averaged, and matched against running centroids by cosine similarity. A confident match (≥ threshold) joins that speaker; a near miss (within margin below it) joins too, but without touching a settled centroid; only a clearly distant voice opens a new speaker. That hysteresis matters — without it a single noisy embedding mints a phantom participant, and one person ends up spread across spk1/spk2/spk3. A speaker's first warmup utterances are treated as provisional: the centroid is still one or two noisy vectors, so it accepts matches down to the bottom of the grey zone rather than judging voice two against noise.

Assignments are greedy, but they are not final. Once two settled speakers look like the same person (≥ merge_threshold) they are merged, and the label that loses is rewritten throughout the transcript — the file on disk is corrected, and the TUI notes the relabelling rather than silently disagreeing with it. Deciding now and revising when the evidence arrives is what keeps real-time labels from freezing an early mistake. Segments under min_speech_s are too short to embed reliably and inherit the previous speaker, or show as spk?.

Backends

parakeet (default) — NVIDIA Parakeet TDT 0.6b v3 through onnxruntime. Multilingual across 25 European languages with autodetection, punctuated output, and roughly 19× real time on CPU. Needs neither torch nor the NeMo toolkit, since onnx-asr runs the exported graph directly. On the same 11 s clip where whisper tiny produced a hallucinated lead-in and lost its punctuation, Parakeet returned the sentence verbatim.

whisper — faster-whisper/CTranslate2, if you want Whisper's language coverage. CTranslate2 ships CUDA and CPU backends only — there is no ROCm build, so on an AMD GPU this runs on CPU no matter what torch reports. The device is detected at startup (cuda if CTranslate2 sees one, else cpu) and shown in the status bar.

To push a Radeon card at the diarization half, resync torch against the ROCm index (see the comment in pyproject.toml).

Config

~/.config/sttop/config.toml. Run sttop config to write a default with every knob and its documentation in it; the comments come from the source, so the file never drifts from the code. Anything you leave out keeps its default, and blank means "you decide" wherever a default is picked for you.

sessions_dir = "~/.local/share/sttop/sessions"

[audio]
mic_source = ""        # substring match against source names; blank = default
system_source = ""     # blank = the default monitor; ignored on macOS
save_wav = false

[vad]
aggressiveness = 2     # 0 permissive .. 3 strict
silence_ms = 700
max_segment_s = 15.0

[stt]
backend = "parakeet"   # parakeet | whisper
model = ""             # blank = the backend's default model
device = "auto"        # whisper only
language = ""          # blank = autodetect

[ui]
theme = "auto"         # auto follows your terminal; or gruvbox, nord, ...

[diarize]
enabled = true
threshold = 0.30       # lower = fewer, broader speakers
margin = 0.10          # grey zone that attaches instead of opening a speaker
merge_threshold = 0.45 # two settled speakers this alike are one person
warmup = 3             # utterances before a speaker's centroid is trusted

Theming

By default sttop paints with the ansi-dark / ansi-light Textual themes, which use only the terminal's own 16 ANSI colours — so it inherits whatever palette you already have rather than imposing its own. Which of the two is picked by reading COLORFGBG, and failing that by asking the terminal for its background colour over OSC 11 (supported by ghostty, kitty, alacritty, wezterm, foot, xterm). If nothing answers, it assumes dark. Run sttop theme to see what was detected.

Set ui.theme to any Textual theme name (gruvbox, nord, catppuccin-mocha, solarized-light, …) to override the terminal-following behaviour.

Tests

uv run --extra dev pytest

The audio-dependent path is exercised by tests/test_pipeline.py, which plays a speech sample into the default sink and reads it back off the monitor. It needs real audio hardware and downloads a model, so it is opt-in:

STTOP_INTEGRATION=1 uv run --extra dev pytest

Screenshots

The images above are rendered from the real widgets by

uv run --extra dev python scripts/screenshots.py

which drives sttop.tui with a scripted transcript instead of a live engine, so docs/*.svg cannot drift from the UI it documents.

License

MIT