Why your 15-minute script comes out at 12 minutes: timing AI narration

A 15-minute script that came out at 12:36

In October 2026 a creator who makes Hindi narrated anime and manhwa recaps described the problem on r/creators: narration that looked long enough on paper finished before the visuals. A script meant for 15 minutes came out at about 12:36 with text-to-speech, 84% of the planned length. Their own lessons were to generate a sample with the final voice and speed before estimating runtime, to split the script into scene-sized sections, and to fix the section that drifts rather than slowing the whole recording. Their open question was how to keep long voiceovers in sync without the delivery sounding unnaturally slow.

The usual cause is the planning number, not the voice. Many people size scripts at a human speaking pace, and Baruch College's Tools for Clear Speech programme, citing the National Center for Voice and Speech, puts the average English speaker at about 150 words a minute. Default TTS voices tend to read faster than that. In our own renders, the Indian English voice on our site read at about 176 words a minute.

That difference alone produces the creator's gap. A script sized for 150 words a minute, read at 176, runs at 150 ÷ 176, or 85%, of its planned length: about 12:47 for 15 minutes. That is an illustration with an English voice, not a diagnosis of their Hindi render, but it shows why the shortfall is predictable and why the same fix works in any language.

Why character and word counts mislead

A word or character count measures the text you write; a voiceover's length depends on the speech that text becomes. Three things pull those apart, and all three are worse in Hindi and Hinglish scripts than in plain English prose.

Words and characters per minute by language

The figures below come from paragraphs we rendered through Pāṇini, our text-to-speech engine, on 24 October 2026: three or four everyday spoken-style paragraphs per language, one voice per language (the one its language page uses), at the default speed of 1.0. Characters per minute are measured from the audio files. Words per minute are our arithmetic from the same renders, splitting words at spaces, so treat them as estimates: the sample is small and a different voice can move them by 20%.

Two cautions apply. These are rates for one synthetic voice each, not for how fast people speak a language, and we found no reliable published words-per-minute figure for human Hindi narration to set beside them. For English, the human benchmark of about 150 words a minute is the useful comparison.

The levers that change a voiceover's length

Once you know your voice's real rate, you have five ways to land on a target length. They are listed roughly in order of how much they move the result.

Render one paragraph, measure, scale: a worked example

The method needs one paragraph of your real script, the voice and speed you will publish with, and a calculator. Here it is with numbers from our own test: a 423-character Hindi story paragraph, read by the voice on our Hindi page at speed 1.0, came out at 26.9 seconds. That is 423 ÷ 26.9 × 60, or about 943 characters a minute, and about 201 words a minute for its 90 words.

To fill 15 minutes (900 seconds) at that rate you need 423 × 900 ÷ 26.9, or about 14,150 characters. Suppose your finished script is 11,900 characters: it will run about 11,900 × 26.9 ÷ 423 = 757 seconds, or 12:37, very close to the r/creators result. You can close the gap by writing about 2,250 more characters, by adding about 2 minutes 23 seconds of pauses and music in the edit, or, least naturally, by setting the speed to about 0.84, since 757 ÷ 900 is 0.84.

For a long video, do the same per scene rather than once for the whole script. Render each scene separately, compare its length with the scene's time on screen, and fix only the scenes that drift. Creators dubbing to subtitles have asked for exactly this kind of timestamp-matched generation (r/StableDiffusion). The script below reads an SRT subtitle file and flags any line whose text will probably run longer than its slot, before you spend characters rendering it.

With our Hindi page voice, the 10,000 free characters described in what 10,000 free characters buy in Hindi come to about 10.7 minutes of audio, which is plenty for timing tests. Ready-made scripts with word counts are in our Hindi voice-over script library, and you can render a test paragraph on the Hindi text to speech page.

import re
import sys

# Characters per minute of finished audio for YOUR voice and speed.
# Render one paragraph, then: len(text) / seconds * 60.
# 937 is Hindi with the voice on the Sonex Labs Hindi page at speed 1.0 (24 October 2026).
CHARS_PER_MINUTE = 937


def to_seconds(stamp: str) -> float:
    hours, minutes, secs = stamp.strip().split()[0].replace(",", ".").split(":")
    return int(hours) * 3600 + int(minutes) * 60 + float(secs)


with open(sys.argv[1], encoding="utf-8-sig") as f:
    blocks = re.split(r"\n\s*\n", f.read().strip())

for block in blocks:
    lines = block.splitlines()
    if len(lines) < 3 or "-->" not in lines[1]:
        continue
    start, end = (to_seconds(part) for part in lines[1].split("-->"))
    text = " ".join(lines[2:])
    slot = end - start
    speech = len(text) / CHARS_PER_MINUTE * 60
    note = "TOO LONG: trim text or raise speed" if speech > slot else ""
    print(f"{lines[0]:>5}  slot {slot:5.1f}s  speech about {speech:5.1f}s  {note}")

How to put this into practice

1. 1

   Choose the voice and speed first

   Length depends on the voice more than on the language, so settle both before you size the script. Changing either later means re-timing everything.

2. 2

   Render one real paragraph

   Pick a paragraph of 300 to 500 characters that is typical of your script, numbers written the way you want them spoken. Render it with the final settings and note its length in seconds.

3. 3

   Work out your own rate

   Divide the paragraph's characters by its seconds and multiply by 60. That is your characters per minute; a whole script will run about its character count divided by that number.

4. 4

   Fix the gap with text and pauses first

   If the estimate is short, add text or plan pauses between scenes. Use the speed setting only for small corrections, and listen before applying it to the whole script.

5. 5

   Check long videos scene by scene

   Render each scene separately, compare it with the scene's time on screen, and fix only the scenes that drift. For subtitles, check every line against its SRT slot before rendering.