Code to evaluate the quality of SRT files using the multilingual multimodal SONAR sentence embedding model.

The evaluation accounts for the semantic similarity (computed as a cosine similarity) between each subtitle block and the corresponding audio to which the block is assigned to (through the timestamps in the SRT). The returned scores range in [-1, 1] where the higher, the better.

Ensure that you have libsndfile installed in you environment. Then, run:

pip install SubSONAR

or, in the source root of this repository:

pip install -e .

The installation has been tested with python 3.8 and 3.10.

Example usage for Italian SRTs and English audios of two files (1 and 2):

subsonar \
  --srt-files 1.srt 2.srt \
  --audio-files 1.wav 2.wav \
  --text-lang ita_Latn --audio-lang eng \
  -bs 32

Please set the batch size bs according to your GPU capacity.

The available languages for the speech encoder (--audio-lang) can be found in the SONAR repository, while the text encoder (--text-lang) supports the 200 languages of NLLB.

SONAR Subtitling Evaluator is licensed under Apache Version 2.0.

However, the SONAR encoders have a dedicated license that can be found in their repository LICENSE. Please check the license for the encoders you are using.

If you find this project useful, please cite:

@inproceedings{gaido-et-al-2024-sbaam,
title = {{SBAAM! Eliminating Transcript Dependency in Automatic Subtitling}},
author = {Gaido, Marco and Papi, Sara and Negri, Matteo and Cettolo, Mauro and Bentivogli, Luisa},
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
year = "2024",
address = "Bangkok, Thailand",
}