silentfail

The worst bugs in generated media don't throw.

If you generate speech or video with a model — TTS, voice agents, podcasts, avatars, AI video — your tests catch the exception that never happens. They do not catch the narration that reads at 300 words per minute, the voice track sitting 18 dB below the footage it's cut against, the clip that rendered at 42% length and got cached as a success, or the file whose audio track is missing entirely.

The 2026 state of the art for catching these is a person listening to the output. That works, and it costs more than everything else in your pipeline combined.

silentfail makes them throw.

from silentfail import assert_pace, assert_loudness, looks_ok

assert_pace("episode-12.mp3", "episode-12.vtt")
assert_loudness("episode-12.mp3")
looks_ok("slide-14.png", ["the title fits on one line"])

Plain assert functions. No framework, no runner, no config file, no service. They raise AssertionError, so they already work in pytest, in CI, or in a five-line script. The five deterministic checks have no dependencies and make no network calls — if you have ffmpeg, you're ready.

Quickstart

pip install silentfail
silentfail check episode-12.mp3 --script episode-12.vtt

  FAIL  pace      narration pace 300 WPM exceeds 245 (300 words in 60.0s) -- …
  PASS  loudness  -16.0 LUFS
  FAIL  dead air  6.2s of silence starting at 0:41 exceeds the 3s limit -- …

  1 passed, 2 failed, 0 skipped

Exit code is 1 if anything failed, so it drops straight into CI. --json gives you the same report for pipelines in any language.

In pytest they're just asserts — no plugin, no fixtures:

@pytest.mark.parametrize("episode", EPISODES)
def test_episode_is_shippable(episode):
    assert_pace(episode.audio, episode.vtt)
    assert_loudness(episode.audio)
    assert_no_dead_air(episode.audio)

Six checks, six incidents

Each default is a threshold set by a defect that actually shipped, not a number chosen for symmetry. All output below is verbatim. Every threshold is an argument — see the reference for tuning.

Narration too fast. A voice picked to match a presenter's face narrated English at ~280 WPM. The audio was valid, correctly timed, perfectly in sync. It just sounded like a machine gun.

assert_pace("episode-12.mp3", "episode-12.vtt", max_wpm=245)

narration pace 300 WPM exceeds 245 (300 words in 60.0s) -- this reads as
machine-gun delivery and listeners cannot follow it: episode-12.mp3

Levels that don't match. Synthesised narration landed at −34 LUFS and was concatenated with footage at −13. Same file, a 20 dB step in the middle. Nobody noticed until viewers spent 45 minutes riding the volume knob.

assert_loudness("episode-12.mp3", target_lufs=-16, tol=2.0)

-34.0 LUFS is 18.0 dB quieter than the -16 target -- it will sound inaudible
next to correctly-levelled audio cut alongside it: episode-12.mp3

Truncated renders cached as successes. Encode failures produced clips a fraction of their intended length, which the pipeline cached as succeeded. Retries only re-ran the ones that had errored — and these hadn't.

assert_duration("segment-07.mp4", expected_seconds=24.0)

segment-07.mp4 is 10.0s -- 42% of the expected 24.0s. A render this short is a
silent encode failure, not a short take; re-render rather than retry

Holes in the middle. Compositing failed transiently and silently, leaving dead stretches mid-file. Right length, right average loudness. The hole only existed in the middle.

assert_no_dead_air("episode-12.mp3", max_silence=3.0)

6.2s of silence starting at 0:41 exceeds the 3s limit -- a gap this long
mid-file is a dropped segment, not a pause (2 found in total): episode-12.mp3

The wrong person speaking. A script said "I'm Jordan" while the system had assigned Alex. A whole module rendered with the wrong face and the wrong voice. Every other gate passed.

assert_speaker(script, expected="Alex", known_names=["Alex", "Jordan", "Sam"])

the script introduces the presenter as "Jordan" but Alex is assigned -- the
rendered avatar would introduce itself with someone else's name. Fix whichever
is wrong: the assigned presenter, or the name in the script

The known_names roster is required, and it's the whole trick: without it, a character in a scenario saying "I'm Rosa, a nurse" trips the check on every script that tells a story. Only a name belonging to someone who could actually have been cast counts as a claim about the speaker.

Things you can only see. Overflowing titles, colliding logos, half-empty canvases, figures cropped mid-caption. All rendered without error.

looks_ok("slide-14.png", ["the title fits on one line", "no text is clipped"])

[major] slide-14.png: the title wraps to three lines and overlaps the logo in
the top-right corner -- failed rubric item: 'the title fits on one line'

This is the only check that needs a key: pip install "silentfail[vision]".

Found on real files

Not a synthetic benchmark. Pointed at the output of a production pipeline that renders narrated video at scale:

It drew the line exactly where the fix landed, on files it was never told anything about, agreeing with a conclusion humans had reached months earlier — and produced no false positives across the clean set.

Two promises

It fails open on infrastructure. No ffmpeg, no key, no network, no measurement → it warns and passes. A gate that blocks your pipeline because of its own breakage gets deleted within a week, and then it protects nothing. A defect fails closed; the checker fails open. (A missing file is your typo, not infrastructure — that still raises.)

Silence is never mistaken for success. A check that couldn't run says so, as a silentfail.Skipped warning and in the CLI output. An empty run never reads as a clean one.

We had to earn the second one. The first cut of this library returned PASS for a file with no audio track at all — silencedetect reports nothing when there is nothing to analyse, and that read as "no silence found". A silent failure inside silentfail. It is now the loudest failure in the suite, with a regression test named after it, and the line it taught us is the rule everything else follows: if we measured and it is wrong, fail closed; if we could not measure, fail open.

What it does not check

Being explicit, because a QA tool that implies more coverage than it has is worse than none:

• Lip sync, A/V drift, and video quality. Nothing here decodes video frames.
• Whether the narration is correct — only how fast it's read. Groundedness and factual accuracy are a different problem, well covered by the LLM-eval tools.
• Speech intelligibility. Loudness is not clarity; a correctly-levelled track can still be mumbled.
• Music, mixing, or anything non-speech. The defaults assume spoken word.
• The rubric you didn't write. looks_ok only checks what you ask it to.

More

• Reference and tuning — every threshold, and when to turn a check off
• Contributing — the bar for a new check
• Changelog

MIT.