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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First, make sure ffmpeg is installed. On MacOS, this looks like:
brew install ffmpeg
(Windows users: make sure ffmpeg is on your path and can be referenced
from the command line!)
Next, grab the package (compatible with Python >= 3.6):
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
ffs, subsync and ffsubsync all work as entrypoints:
ffs 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.
If the sync fails, the following recourses are available:
- Try to sync assuming identical video / subtitle framerates by passing
--no-fix-framerate; - Try passing
--gssto use golden-section search to find the optimal ratio between video and subtitle framerates (by default, only a few common ratios are evaluated); - Try a value of
--max-offset-secondsgreater than the default of 60, in the event that the subtitles are out of sync by more than 60 seconds (empirically unlikely in practice, but possible). - Try
--vad=auditoksince auditok can sometimes work better in the case of low-quality audio than WebRTC's VAD. Auditok does not specifically detect voice, but instead detects all audio; this property can yield suboptimal syncing behavior when a proper VAD can work well, but can be effective in some cases.
If the sync still fails, consider trying one of the following similar tools:
- sc0ty/subsync: does speech-to-text and looks for matching word morphemes
- kaegi/alass: rust-based subtitle synchronizer with a fancy dynamic programming algorithm
- tympanix/subsync: neural net based approach that optimizes directly for alignment when performing speech detection
- oseiskar/autosubsync: performs speech detection with bespoke spectrogram + logistic regression
- pums974/srtsync: similar approach to ffsubsync (WebRTC's VAD + FFT to maximize signal cross correlation)
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.
The synchronization algorithm operates in 3 steps:
- Discretize both the video file's audio stream and the subtitles into 10ms windows.
- 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 the audio stream, use an off-the-shelf voice activity detector (VAD) like the one built into webrtc.
- 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).
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).
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.
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).
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)
- Other excellent Python libraries like argparse, rich, and tqdm, not related to the core functionality, but which enable much better experiences for developers and users.
Code in this project is MIT licensed.
