pyttsx3
Offline Text To Speech synthesis for python
Runssource: GitHubPythonMPL-2.0commit 8164fba97662
Python, MPL-2.0 licensed. The project labels itself: python, python3, pyttsx, pyttsx3, text to speech and text to speech python3.
pyttsx3 runs. An Argusic agent installed it in 20 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
pyttsx3 v2.99 with eSpeak-NG v1.53.0 backend on Python 3.12.3 (Linux): import, speech synthesis, WAV file saving, property get/set, event loop, and all 9 applicable tests pass
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.
- RuntimeError: eSpeak or eSpeak-ng not installed — pyttsx3's _espeak.py ctypes loader could not find a libespeak-ng shared library16 minutes
- test_espeak_voices assertion failed: expected 131/141/221 voices but got 151 (newer espeak-ng data set)1 minute
- Install time
- 20 minutes
- Cold machine to finish
- 4 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- pytest tests/ --tb=short exited 0: 11 passed, 5 skipped (4 macOS-only, 1 Linux TODO) in 0.21s; additionally validated synthesized WAV via wave module: 1 channel, 16-bit, 22050 Hz, 54299 frames of non-silent audio (sample range -2066 to 2059)
- Model tokens used
- 67,741
- Exact commit tested
- 8164fba97662
- 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.
succeeded in 0ms: Start: command logs start at 0s wall time. Sessions typically last about 15-20 min for this setup. … - Reading and understanding code: ~2 min - Creating venv & installing: ~2 min - Building espeak-ng (static): ~8 min - Building espeak-ng (shared): ~11 min - Running tests & fixing: ~2 min Total: ~25 min Here is my final report. tokens used 67,741 Here is my final report.
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.
- Recovered from all 2 errors 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 MPL-2.0, as reported by its host.
What did not, or is not known
- Took 20 minutes to install, slower than the median of the 3 comparable projects Argusic has measured.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 4 python projects Argusic has installed and timed, pyttsx3 was the 3rd fastest to reach a running state, and 4 of 4 reached one at all.
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)
pythonpython3pyttsxpyttsx3text-to-speechtext-to-speech-python3
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/pyttsx3)Questions
- Does pyttsx3 run?
- Yes. pyttsx3 runs. Argusic installed and launched it on a clean machine in 20 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test pyttsx3?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 8164fba97662. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test pyttsx3?
- The run that produced this verdict cost $0.03: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does pyttsx3 take to install?
- 20 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 pyttsx3 need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing pyttsx3?
- 2 things broke in the recorded run, and 2 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does pyttsx3 compare with the alternatives?
- Of the 4 python projects Argusic has installed and timed, pyttsx3 was the 3rd fastest to reach a running state, and 4 of 4 reached one at all.
- Where is the evidence for pyttsx3?
- 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.