This is a fork of [faster-auto-subtitle](https://github.com/Sirozha1337/faster-auto-subtitle) using [faster-whisper](https://github.com/SYSTRAN/faster-whisper) implementation.
This script will connect to your Bazarr instance to get a list of shows that require subtitles and start processing each video to create, by default Engligh subs, these are then written to the file as Soft subtitles.
The default setting (which selects the `small` model) works well for transcribing English. You can optionally use a bigger model for better results (especially with other languages). The available models are `tiny`, `tiny.en`, `base`, `base.en`, `small`, `small.en`, `medium`, `medium.en`, `large`, `large-v1`, `large-v2`, `large-v3`.
Setting higher `no_speech_threshold` could be useful for videos with a lot of background noise to stop Whisper from "hallucinating" subtitles for it.
In my experience settings option `condition_on_previous_text` to `False` dramatically increases accurracy for videos like TV Shows with an intro song at the start.
You can use `sample_interval` parameter to generate subtitles for a portion of the video to play around with those parameters: