Release Date: May 4, 2026
AudioMuse AI v1.1.0 introduces a new Lyrics Semantic Search ecosystem, enabling deep lyric-based discovery powered by semantic vectors.
When LYRICS_ENABLED=true (default enabled, configurable in setup wizard; not supported in -noavx2 builds), the system analyzes song lyrics and builds semantic embeddings for advanced search and similarity matching. Run a new analysis when enabled will analyze the lyrics for the already analyzed song, or do a full analysis (Musicnn + DCLAP + Lyrics) for the new one.
This release also introduce different bugfix.
New Search Capabilities
- Axis-based search: Explore songs across 5 defined semantic axes, selecting one or more values that best describe the target mood or meaning.
- Text search: Simple natural language queries (e.g., “love”, “run”) focused on lyrical meaning, not musical groove (distinct from DCLAP search).
- Song similarity search: Use a reference track to find similar songs, weighted by default as 75% lyrical meaning and 25% audio similarity to preserve genre consistency.
Search quality depends on the completeness of your music library metadata and lyrics availability.
Lyrics Analysis Pipeline (Required)
To enable this feature, a full lyrics analysis is mandatory:
- Retrieve lyrics from the local music server
- Jellyfin: GET /Items/{id}/Lyrics (built-in since 10.8, no plugin needed)
- Emby: GET /emby/Items/{id}/Lyrics (same structure as Jellyfin)
- Navidrome: GET /rest/getLyricsBySongId?id={id} (requires Navidrome 0.49+ with OpenSubsonic)
- Lyrion/LMS: POST /jsonrpc.js with songinfo + tags (standard LMS)
- ELSE If configured, fetch lyrics via external API
- ELSE If unavailable, fallback to Whisper-based transcription
Example API formats supported in Setup Wizard:
https://api.example.com/get?artist={artist}&title={title}
https://api.example.com/v1/{artist}/{title}
Performance Note
Lyrics analysis is computationally intensive and require (on top of the normal Musicnn/DCLAP analysis):
- Raspberry Pi 5: ~3–4 minutes per track (Whisper fallback)
- Intel i7 14th gen: ~30 seconds per track
On large music libraries and lower-end hardware, analysis can take considerable time if lyrics are not available locally or via external APIs. This is expected behavior rather than a performance issue.
Multilingual Lyrics Support
AudioMuse AI is optimized for English lyrics, as the embedding model is English-based. For non-English tracks, lyrics are first transcribed using Whisper (when not available from external sources) and then translated into English using MarianMT (Helsinki-NLP opus-mt-{lang}-en models) before embedding. Only supported language pairs (~60+ languages on HuggingFace) are processed; otherwise, lyrics are skipped to preserve embedding quality.
Whisper: https://github.com/openai/whisper#available-models-and-languages
MarianMT: https://huggingface.co/Helsinki-NLP
What's Changed
- Lyrics Analysis and Search by @NeptuneHub in #469
- fix automatic build on pr by @NeptuneHub in #473
- Add MUSIC_LIBRARIES checkbox UI to setup wizard and migration assistant by @rendyhd in #459
- Lyrics transcriber fix by @NeptuneHub in #474
- Lyrics API optional setup by @NeptuneHub in #475
- Fix internal mapping of POSTGRES_PORT that incorrectly look at external port set in .env by @knowgoodcoffee in #455
- Semantic Grove search by @NeptuneHub in #478
New Contributors
- @knowgoodcoffee made their first contribution in #455
Full Changelog: v1.0.4...v1.1.0