dia
A TTS model capable of generating ultra-realistic dialogue in one pass.
Could not verifysource: GitHubPythonApache-2.0commit 876125e461a0
Python, Apache-2.0 licensed. The project labels itself: ai, open weight and text to speech.
Argusic could not get dia running. An Argusic agent installed it in 3.2 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Package nari-tts installs and all modules import, config loads from HF, CLI help parses, and audio utility functions work correctly, but the 1.6B-parameter model cannot load on this container's 755 MB of RAM (needs at least 3.2 GB).
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 50 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.
- OOM while loading Dia-1.6B-0626 model weights on 755MB RAM container1.5 minutes
- PEP 668 externally-managed-environment blocked system-wide pip install0.5 minutes
- Install time
- 3 minutes
- Cold machine to finish
- 10 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- pip install -e . exited 0. 'python -c "from dia.model import Dia; from dia.config import DiaConfig; print('ok')"' printed 'ok'. 'python cli.py --help' printed usage. 'python -c "from dia.audio import build_delay_indices, apply_audio_delay, build_revert_indices, revert_audio_delay; import torch; ... round-trip test ..." verified all audio utilities. Model forward pass could not be tested (OOM).
- Model tokens used
- 200,244
- Exact commit tested
- 876125e461a0
- 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
- Whether it works at all
- this run verified nothing, so this page makes no claim either way
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
pydantic_core 2.46.5 safetensors 0.8.0 soundfile 0.14.0 torch 2.6.0+cpu torch-stoi 0.2.3 torchaudio 2.6.0+cpu Here's my final report. ``` tokens used 200,244 Here's 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
- Installed in 3.2 minutes, faster than the median of the 51 comparable projects Argusic has measured.
- Recovered from all 2 errors without a human stepping in, which says the failures are documented well enough to solve.
- Licensed Apache-2.0, as reported by its host.
- Measured 2 times, so the result is not a one-off.
What did not, or is not known
- An agent with no time limit did not get it running.
Of the 52 ai projects Argusic has installed and timed, dia was the 20th fastest to reach a running state, and 39 of 52 reached one at all.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 2/3 | Did not run | 50.00 | 0.05 | |
| Argusic Runner | 1/3 | Did not run | 50.00 | 0.21 |
Topics (from GitHub)
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/dia)Questions
- Does dia run?
- Argusic could not verify that dia runs. Argusic installed and launched it on a clean machine in 3 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test dia?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 876125e461a0. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test dia?
- 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 dia take to install?
- 3.2 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 dia need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing dia?
- 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 dia compare with the alternatives?
- Of the 52 ai projects Argusic has installed and timed, dia was the 20th fastest to reach a running state, and 39 of 52 reached one at all.
- Where is the evidence for dia?
- All 2 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.