FRAME | After the Lights Go Out

Prompt

Use the current VIFE Lite to create an English night-time exhibition guide MP3, "FRAME | After the Lights Go Out", for the fictional art space FRAME, about 35–45 seconds long. Use a natural, intimate, slightly exploratory TTS-1 voice; read only the original copy below, with no background music and no references to real collections: "Welcome to FRAME's night gallery. First, stop in this beam of light: an empty frame is holding your shadow on the wall. Turn left, and paper and metal give off different faint glows in the same room. Tonight, we're in no hurry to find answers. You can move closer to study the textures, or step back and see the distance left between the works. The exhibition begins here, and it begins with your gaze." Deliver only a real MP3, and finish after checking the duration, the start and end, and the volume.

Result

Audio

FRAME | After the Lights Go Out

FRAME | After the Lights Go Out

0:00 / 0:38

FRAME | After the Lights Go Out — English night-gallery guide, 38.64 s, a real MP3 (MPEG-2 Layer III / v2, 128 kbps, 24 kHz mono, ID3-tagged).

Voice: TTS-1, "fable" at speed 0.85 — unhurried, close and curious, as asked. Your copy is read as supplied, with no music bed, no added lines and no real collections named.

Measured on the delivered file itself:

  • Duration: 38.640 s — inside 35–45 s
  • Start: first word at ~0.09 s, no dead lead-in
  • End: last word finishes at 37.94 s, then a 0.64 s clean tail — nothing clipped
  • Volume: −19.1 LUFS integrated, −1.9 dBFS sample peak / −1.7 dBTP true peak, mean −21.4 dB, LRA 9.5 LU — a healthy, unclipped narration level, lifted from the raw take's −26.0 LUFS

What I changed, and why: the raw take ran 29.2 s, under your minimum. Rather than speeding the words back up, I kept the voice as recorded and gave each of the five sentence breaks a ~1.75 s breath of silence, so the guide lands at 38.6 s and reads as deliberately unhurried — visitors get time to look.

One honest gap: the attachment-analysis service refused its credentials during this run, so I could not machine-transcribe the audio to re-check every word or judge pronunciation and timbre by listening. Duration, edges and level above are measured from the file; wording fidelity rests on the text I sent to the voice, not on a second transcription.

How it was made

Mode
Audio
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