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Added audio embedding extraction throughput examples to README

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emeric
2026-06-06 11:14:06 +02:00
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@@ -42,7 +42,7 @@ _LMS_ provides several ways to help you find the music you like:
__Note__: the recommendation engine supports two modes: __Note__: the recommendation engine supports two modes:
* **Tag-based**: uses metadata tags `genre` and `grouping`. * **Tag-based**: uses metadata tags `genre` and `grouping`.
* **Audio similarity**: uses MusicNN embeddings. Extracting embeddings takes roughly 10× longer than regular file metadata parsing. To speed up extraction, consider tweaking `scanner-thread-count` in `lms.conf` (defaults to half the number of logical CPUs). * **Audio similarity**: uses MusicNN embeddings. Extraction throughput depends heavily on both the number of scanner threads and the speed of your storage. Typical reported speeds range from ~1k tracks/hour on a Raspberry Pi 4 (3 threads) to ~25k tracks/hour on an Intel Core i5-13500 (10 threads). To speed up extraction, consider tweaking `scanner-thread-count` in `lms.conf` (defaults to half the number of logical CPUs).
## About tags ## About tags
_LMS_ primarily relies on tags to organize your music collection but also supports browsing by directory using the [Subsonic/OpenSubsonic API](SUBSONIC.md). _LMS_ primarily relies on tags to organize your music collection but also supports browsing by directory using the [Subsonic/OpenSubsonic API](SUBSONIC.md).