ffsubsync 0.4.0

Language-agnostic synchronization of subtitles with video.

FFsubsync

Language-agnostic automatic synchronization of subtitles with video, so that subtitles are aligned to the correct starting point within the video.

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Install

First, make sure ffmpeg is installed. On MacOS, this looks like:

brew install ffmpeg

Next, grab the script. It should work with both Python 2 and Python 3:

pip install ffsubsync

If you want to live dangerously, you can grab the latest version as follows:

pip install git+https://github.com/smacke/ffsubsync@latest

Usage

Both subsync and ffsubsync work as entrypoints:

ffsubsync video.mp4 -i unsynchronized.srt > synchronized.srt

or

ffsubsync video.mp4 -i unsynchronized.srt -o synchronized.srt

There may be occasions where you have a correctly synchronized srt file in a language you are unfamiliar with, as well as an unsynchronized srt file in your native language. In this case, you can use the correctly synchronized srt file directly as a reference for synchronization, instead of using the video as the reference:

ffsubsync reference.srt -i unsynchronized.srt -o synchronized.srt

ffsubsync uses the file extension to decide whether to perform voice activity detection on the audio or to directly extract speech from an srt file.

Speed

ffsubsync usually finishes in 20 to 30 seconds, depending on the length of the video. The most expensive step is actually extraction of raw audio. If you already have a correctly synchronized "reference" srt file (in which case audio extraction can be skipped), ffsubsync typically runs in less than a second.

How It Works

The synchronization algorithm operates in 3 steps:

1. Discretize video and subtitles by time into 10ms windows.
2. For each 10ms window, determine whether that window contains speech. This is trivial to do for subtitles (we just determine whether any subtitle is "on" during each time window); for video, use an off-the-shelf voice activity detector (VAD) like the one built into webrtc.
3. Now we have two binary strings: one for the subtitles, and one for the video. Try to align these strings by matching 0's with 0's and 1's with 1's. We score these alignments as (# video 1's matched w/ subtitle 1's) - (# video 1's matched with subtitle 0's).

The best-scoring alignment from step 3 determines how to offset the subtitles in time so that they are properly synced with the video. Because the binary strings are fairly long (millions of digits for video longer than an hour), the naive O(n^2) strategy for scoring all alignments is unacceptable. Instead, we use the fact that "scoring all alignments" is a convolution operation and can be implemented with the Fast Fourier Transform (FFT), bringing the complexity down to O(n log n).

Limitations

In most cases, inconsistencies between video and subtitles occur when starting or ending segments present in video are not present in subtitles, or vice versa. This can occur, for example, when a TV episode recap in the subtitles was pruned from video. FFsubsync typically works well in these cases, and in my experience this covers >95% of use cases. Handling breaks and splits outside of the beginning and ending segments is left to future work (see below).

Future Work

Besides general stability and usability improvements, one line of work aims to extend the synchronization algorithm to handle splits / breaks in the middle of video not present in subtitles (or vice versa). Developing a robust solution will take some time (assuming one is possible). See #10 for more details.

History

The implementation for this project was started during HackIllinois 2019, for which it received an Honorable Mention (ranked in the top 5 projects, excluding projects that won company-specific prizes).

Credits

This project would not be possible without the following libraries:

• ffmpeg and the ffmpeg-python wrapper, for extracting raw audio from video
• VAD from webrtc and the py-webrtcvad wrapper, for speech detection
• srt for operating on SRT files
• numpy and, indirectly, FFTPACK, which powers the FFT-based algorithm for fast scoring of alignments between subtitles (or subtitles and video)
• sklearn for its data pipeline API
• Other excellent Python libraries like argparse and tqdm, not related to the core functionality, but which enable much better experiences for developers and users.

License

Code in this project is MIT licensed.