RealtimeSTT
A robust, efficient, low-latency speech-to-text library with advanced voice activity detection, wake word activation and instant transcription.
Runssource: GitHubPythonMITcommit 777727553eed
Python, MIT licensed. The project labels itself: python, realtime and speech to text.
RealtimeSTT runs. An Argusic agent installed it in 25 minutes and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Installed RealtimeSTT v1.1.2 in a Python 3.12 venv with PyAudio 0.2.13 (from Debian package), torch 2.14.0+cpu, torchaudio 2.11.0+cpu, faster-whisper 1.2.1, silero-vad 6.2.3, openwakeword 0.4.0, fastapi 0.141.1 and uvicorn 0.54.0; 591 unit tests pass and the real-model golden transcription test produces correct output.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- PyAudio 0.2.14 source build failed because python3-dev and portaudio19-dev are not installed (no root privileges to apt-get install)8 minutes
- Install time
- 25 minutes
- Cold machine to finish
- 8 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- How the result was proved
- python -m unittest discover -s tests/unit -p test_*.py ran 591 tests with 0 failures, 14 skipped (opt-in golden only); REALTIMESTT_RUN_GOLDEN_TRANSCRIPTION=1 golden test with tiny model transcribed LJ001-0002 as 'In being comparatively modern.' (expected 'in being comparatively modern.'), the minor capitalization difference is the only delta; uvicorn production_server:create_app --factory started and answered /health with a full 503 JSON payload (expected while models load on CPU)
- Model tokens used
- 256,018
- Exact commit tested
- 777727553eed
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
time.sleep(0.05)
time.sleep(0.2)
recorder.stop()
actual = recorder.text()
print_test_message(f"golden {sample['id']} expected: {expected}")
print_test_message(f"golden {sample['id']} actual: {actual}")
self.assertTrue(actual.strip())
Now I have all the data I need. Let me compile the final result.
2026-09-27T04:31:47.195651Z ERROR codex_core::session: failed to record rollout items: thread 01a0e…
tokens used
256,018
Now I have all the data I need. Let me compile the final result.Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 25 minutes.
- Recovered from all 1 error without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
Run history
Topics (from GitHub)
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/realtimestt)Questions
- Does RealtimeSTT run?
- Yes. RealtimeSTT runs. Argusic installed and launched it on a clean machine in 25 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test RealtimeSTT?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 777727553eed. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test RealtimeSTT?
- The run that produced this verdict cost $0.05: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does RealtimeSTT take to install?
- 25 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does RealtimeSTT need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing RealtimeSTT?
- 1 thing broke in the recorded run, and 1 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for RealtimeSTT?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.