srt_equalizer 0.1.4

Transform subtitle line lengths, splitting into multiple subtitle fragments if necessary.

SRT Equalizer

A Python module to transform subtitle line lengths, splitting into multiple subtitle fragments if necessary. Useful to adjust automatic speech recognition outputs from e.g. Whisper to a more convenient size.

This library works for all languages where spaces separate words.

Installing

pip install srt_equalizer

Example

If the SRT file contains lines over a certain length like this:

1
00:00:00,000 --> 00:00:04,000
Good evening. I appreciate you giving me a few minutes of your time tonight

2
00:00:04,000 --> 00:00:11,000
so I can discuss with you a complex and difficult issue, an issue that is one of the most profound of our time.

Using this code to shorten the subtitles to a maximum length of 42 chars:

from srt_equalizer import srt_equalizer

srt_equalizer.equalize_srt_file("test.srt", "shortened.srt", 42)

...they are split into multiple fragments and time code is adjusted to the approximate proportional length of each segment while staying inside the time slot for the fragment.

1
00:00:00,000 --> 00:00:02,132
Good evening. I appreciate you giving me

2
00:00:02,132 --> 00:00:04,000
a few minutes of your time tonight

3
00:00:04,000 --> 00:00:06,458
so I can discuss with you a complex and

4
00:00:06,458 --> 00:00:08,979
difficult issue, an issue that is one of

5
00:00:08,979 --> 00:00:11,000
the most profound of our time.

Adjust Whisper subtitle lengths

Is is also possible to work with the subtitle items with the following utility methods:

split_subtitle(sub: srt.Subtitle, target_chars: int=42, start_from_index: int=1) -> list[srt.Subtitle]:

whisper_result_to_srt(segments: list[dict]) -> list[srt.Subtitle]:

Here is an example of how to reduce the lingth of subtitles created by Whisper. It assumes you have an audio file to transcribe called gwb.wav.

import whisper
from srt_equalizer import srt_equalizer
import srt
from datetime import timedelta

options_dict = {"task" : "transcribe", "language": "en"}
model = whisper.load_model("small")
result = model.transcribe("gwb.wav", language="en")
segments = result["segments"]
subs = srt_equalizer.whisper_result_to_srt(segments)

# Reduce line lenth in the whisper result to <= 42 chars
equalized = []
for sub in subs:
    equalized.extend(srt_equalizer.split_subtitle(sub, 42))

for i in equalized:
    print(i.content)

Contributing

This library is built with Poetry. Checkout this repo and run poetry install in the source folder. To run tests use poetry run pytest tests.

If you want to explore the library start a poetry shell.