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The current algorithm is not optimized when the input document both generates a large number of regions with indefinite temporal intervals and a large number of captions/subtitles: all regions must be visited for each captions/subtitle (NxM problem).
Couple of options come to mind:
reduce the number of regions generated when reading an SCC document by coalescing regions with similar dimensions (probably a good idea in any event)
optimize the ISD generation algorithm (probably following a pattern similar to that at sandflow/imscJS@b728b68)
add multi-processing support (not sure it is entirely worth the effort)
Hi,
Source is an SCC with 1033 captions.
source.scc.zip
Top 3 Time-Consuming Operations:
_process_element (isd.py:413)
Consumed 35.644s cumulative time
Called 2,165,274 times
Recursive function (note the ncalls format: 2165274/48445)
_compute_styles (isd.py:400)
Consumed 11.755s cumulative time
Called 150,222 times
set_style (model.py:339)
Consumed 5.424s cumulative time
Called 10,642,028 times
Please let me know if you need more details.
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