rendercheck

The worst bugs in generated media don't throw.

"the audio often cuts off the final sentence […] though the API returns success without error signals"

— a developer on the OpenAI forum, April 2026, describing production output

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, the captions that describe the audio three seconds before it happens, 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.

rendercheck makes them throw.

from rendercheck 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 service. They raise AssertionError, so they already work in pytest, in CI, or in a five-line script. Eighteen of the nineteen checks have no dependencies and make no network calls — if you have ffmpeg, you're ready.

Quickstart

You need ffmpeg on your PATH (brew install ffmpeg, apt-get install ffmpeg, or winget install ffmpeg). Then:

pip install rendercheck
rendercheck demo

Or drop a file into the playground — same checks, running on ffmpeg compiled to WebAssembly, nothing uploaded.

demo synthesises ten defective files and runs the real checks against them, so you can see it fire without owning a broken render. Verbatim, first two of eight:

Narration too fast
  A voice picked to match a presenter's face read English at machine-gun speed. Valid audio, correct timing, perfectly in sync.

  $ rendercheck check machine-gun.wav --script narration.vtt

  FAIL  pace        narration pace 300 WPM exceeds 245 (300 words in 60.0s) -- this reads as machine-gun delivery and listeners cannot follow it: machine-gun.wav
  PASS  loudness    -16.1 LUFS
  PASS  dead air    0.0 s silence
  PASS  truncation  8.9 dB of fall-off at the end
  PASS  clipping    0 samples at 0 dBFS

Levels that don't match
  Synthesised narration landed 18 dB under the footage it was cut against. Nobody noticed until viewers rode the volume knob.

  $ rendercheck check too-quiet.wav

  SKIP  pace        no --script given
  FAIL  loudness    -34.2 LUFS is 18.2 dB quieter than the -16 target -- it will sound inaudible next to correctly-levelled audio cut alongside it: too-quiet.wav
  PASS  dead air    0.0 s silence
  PASS  truncation  8.8 dB of fall-off at the end
  PASS  clipping    0 samples at 0 dBFS

…and one of the two added in 0.3.0:

Captions against the wrong clock
  A concatenation added three seconds of pre-roll after the captions were written. Both files are perfectly valid on their own.

  $ rendercheck check late-captions.wav

  SKIP  pace        no --script given
  PASS  loudness    -16.0 LUFS
  PASS  dead air    0.0 s silence
  PASS  truncation  74.3 dB of fall-off at the end
  PASS  clipping    0 samples at 0 dBFS
  FAIL  captions    late-captions.vtt runs 3.0s late against late-captions.wav, past the 0.75s limit -- every line arrives at the wrong moment, and both files are individually valid so nothing else catches it

Then point it at your own output:

rendercheck check episode-12.mp3 --script episode-12.vtt --preset podcast

Exit code is 1 if anything failed — or if nothing could be measured, because a run that looked at nothing is not a clean one. A path you typo'd exits 2. --json gives you the same report for pipelines in any language, and --strict rejects partial runs too.

Where is this file going?

"How loud should this be?" has no single answer — it depends entirely on where the file ends up, and every platform publishes a different number. --preset turns that table into something a build can enforce:

$ rendercheck presets

  preset    target     tol     peak  source
  youtube     -14L   1.0dB   -1.0TP  YouTube normalises playback to -14 LUFS
  spotify     -14L   1.0dB   -1.0TP  Spotify, including podcasts, at -14 LUFS
  tiktok      -14L   1.5dB   -1.0TP  TikTok and Instagram, measured rather than published
  podcast     -16L   1.0dB   -1.0TP  AES71 / Apple Podcasts: -16 LUFS stereo, -19 mono
  apple       -16L   1.0dB   -1.0TP  Apple Music Sound Check, -16 LUFS
  web         -16L   2.0dB       --  spoken-word web video -- rendercheck's own defaults
  ebu         -23L   1.0dB   -1.0TP  EBU R128, European broadcast
  atsc        -24L   2.0dB   -2.0TP  ATSC A/85, North American broadcast
  netflix     -27L   2.0dB   -2.0TP  Netflix delivery, dialog-gated

None of those numbers are ours. The contribution is that --preset ebu is a decision a reviewer can read, where --target-lufs -23 is a magic number the next person will not dare touch. A preset that states a ceiling also switches on the true-peak check, which catches a master measuring clean locally and distorting after upload. web exists only to name the built-in defaults, so it states none and behaves exactly like passing no preset at all.

Project-wide settings go in rendercheck.toml (or [tool.rendercheck] in pyproject.toml) so a CI step is not eight flags on one line:

preset = "podcast"
max_silence = 5.0

Flags you type still beat the file, and the file beats the built-in defaults.

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)

"Isn't this forty lines of pyloudnorm?"

For one of the nineteen checks, roughly yes. None of these measurements are novel, and it would be dishonest to imply otherwise:

Most of those hand a number to a researcher. The two that are already gates gate a different thing: MediaConch checks that a file conforms to a container policy, which is a preservation question, not a perceptual one — a file can pass every MediaConch rule and still be narrated at 300 WPM. ffsubsync will happily realign captions that were never wrong, because it has no opinion about whether they needed it.

What is actually missing, and what this is:

• A threshold that came from a defect, not from a paper. 245 WPM because a real voice narrated at 280 and shipped. −16 LUFS because narration landed at −34 against footage at −13.
• A message that says what a person would notice. "−34.0 LUFS" is a reading. "18.0 dB quieter than the −16 target — it will sound inaudible next to correctly-levelled audio cut alongside it" is a bug report.
• Fail-open on infrastructure, fail-closed on a defect, so it can sit in CI without becoming the thing that breaks the build for its own reasons.
• Exit codes and one command over a directory, rather than a notebook.

Against the LLM-eval tools the difference is structural rather than a matter of coverage. promptfoo, DeepEval and RAGAS are excellent and none of them can do this: their test case is a string. There is no assertion to add, because there is nowhere to put the file. Use them for the script; use this for what the script turned into.

And if you already run broadcast QC — Interra BATON, Telestream Vidchecker, QCTools — you have had most of this for twenty years. It just isn't in your git hooks.

In your pipeline

GitHub Actions — installs ffmpeg and fails the build on a defect:

- uses: rogermsc/rendercheck@v0
  with:
    files: out/
    preset: podcast
    strict: "true"

Node, Remotion, anything that renders in a build step:

npx rendercheck check out/