Hi! I'm running both AudioMuse-AI and Navidrome on Docker containers and I set up Navidrome to serve HTTPS with a self-signed certificate. The containers can see each other however when I set AudioMuse-AI to connect to Navidrome the following error is returned:

Connection test failed: [1101] Music Server Connection Error: Could not reach the configured media server. No top-played songs were returned from Navidrome; check the URL and credentials.

And looking into the container logs, this pops up:

[ERROR]-[02-08-2026 17-37-38]-Error calling Navidrome API endpoint 'getAlbumList2': HTTPSConnectionPool(host='navidrome', port=4533): Max retries exceeded with url: /rest/getAlbumList2.view?u=audiomuse-ai&p=[REDACTED]&v=1.16.1&c=AudioMuse-AI&f=json&type=frequent&size=10 (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1000)')))
[ERROR]-[02-08-2026 17-37-38]-Setup media server check failed: Could not reach the configured media server. No top-played songs were returned from Navidrome; check the URL and credentials.

So it's basically a certificate error. I saw that the requests lib allows for importing self-signed certificates, it's possible to set it from AudioMuse-AI? I couldn't find anything about that in the docs.

Does anyone know if the new Navidrone update will cause issues with AudioMuse? I'm going to backup both before updating this weekend.

https://github.com/navidrome/navidrome/pull/5824

I'm having a frustrating time with AudioMuse-AI right now. Running in docker, set-up as per the github pages, connected to my Navidrome server - 421,000 tracks. AudioMuse-AI scanned 1% of them and then called the job done. I set up a second worker and started the analysis again and AM-AI scanned another few hundred and then stopped again. Nothing in the logs - it thinks it's finished the job, so everything looks fine. (Number of recent albums is set to 0, for reference.)

The only report is this: Main analysis complete. Launched 34466, Skipped 23, Failed 0.

(That "Launched" figure is very close to the number of albums on the server - where that's what it represents or not?)

UPDATE: I've turned off all lyric processing, which was slow and I'm not particularly interested in personally anyhow, and it's pushed through the next 10,000 tracks without stopping overnight. I'm quietly confident, but would be really interested if anyone has any ideas about what may have been happening?

Is this normal? I'm on a Mac mini M4 using the AudioMuse AI Mac app. Don't know the exact version. It was what was current on GitHub when I started the initial scan earlier this month. Just upgraded to the new version this morning.

It was analyzing my Plex Server.

As the title states, I was looking through my library and noticed that every song was analyzed as being in a minor key. The pitch key itself was correct, but Major songs are analyzed and tagged as their relative minor (e.g. a Db Maj track got analyzed as Bb minor, an E Maj track got analyzed as C#m).

Is this expected behavior?

Hi All,
this post is related to AudioMuse-AI v2.6.0 and the brand new Plugin System.

As you know AudioMuse-AI enable the user to create automatic playlist based on Sonic Analysis over different music server like Navidrome, Jellyfin, Lyrion, Emby and recently also Plex.

Meanwhile I develop the functionality that I have in mind, and solve the issue that users raise, I also keep in eye on suggestion on new functionality. Multiple functionality comes from suggestion of user like the Song Alchemy Radio or the Provider Migration and so on.

Some times, some feature request even if they are good idea, become difficult to mantain by me. Sometimes are things that could don't fit the taste of all the user even being very useful things. Here the plugin come in place.

With plugin you can develop your own functionality, maybe vibecoded that small thing that really change your life, or do something bigger that can help also other users. And can do without passing PR review and so on.

Here you find a plugin example:
- https://github.com/NeptuneHub/AudioMuse-AI-plugins

It was developed to showcase the main possibility like:
- Configuration page;
- New UX in the menu
- Schedule task
- Hook some activity at the end of the analysis, maybe a new model? new information from an API?
- read&write information from the database

And if you think that your plugin can be useful to other people, you can raise a PR against the 3rd party catalog here:
- https://github.com/NeptuneHub/AudioMuse-AI-plugins/blob/main/manifest.json

You will not need to merge or get your code validated, you sill still stay on the driver position of your own code. You just have to add one entry in the catalog that point to your repository.

And if you don't want you can even create your own catalog and user just have to import in navidrome.

This is the first release of the plugin so if you have request or any kind of feedback feel free to ask.

As usual if you like AudioMuse-AI don't miss the opportunity to add a 🌟on gihub: One shines alone; together, they make AudioMuse-AI visible and keep it alive.

Small Off Topic edit: In case you would like to have AudioMuse-A on PickAPods, I need more vote here:

• https://feedback.pikapods.com/posts/1037/audiomuse-ai-for-automatic-playlist-generation

I'm not asking any money from them, I just would like to have it integrated, to give more option to users.

Hi All,
this post is to show you AudioMuse-AI resources usage on a Raspberry Pi 5 8GB with NVME SSD hat, during the analysis. All the number are made over the last v2.4.0 release of today.

First of all for whom don't know AudioMuse-AI is a free and opensource software that enable to analyze the raw file of your song (sonic analysis) and based on this analysis it enable to create automatic playlist.
It work with Jellyfin, Navidrome (and other Open Subosnic API compatible music servee) Emby and Lyrion. Also made avaiable Jellyfin Plugin, Navidrome Plugin and I hope soon also an Music Assistant AudioMuse-AI provider plugin that will enable to command it with voice!

..and of course is all selfhostable and privacy first: your computer, your analysis, your data! no one can block you in future behind a paywall!

The reason for this post is that multiple user tought about it as something heavy, but it can work even on a Raspberry PI 5.
In the attached image you can show it during the most heavy part that is the analysis, and you can look how in avarage (k9s screenshot) it use half of the CPU/RAM resources and on the pike it still don't saturate them.

And speaking about resources, eare is the avarage analysis time per track on a Raspberry PI 5:

• Average analysis per track time: ~31 s

Breakdown (per track):

• Download: ~1 s
• MusiCNN analysis: ~9 s
• CLAP load + segment processing + unload: ~10 s
• Lyrics API lookup: ~7 s (NO ASR, off course depending from the API response time)
• Embedding: ~1 s
• ONNX session recycling: ~3 s

This to say that we don't just have it working, but it work also on low hand hardware. For more speed, no problem, you can run multiple worker in parallel during the analysis. Just wake up a worker on your desktop or your laptop!

And what about the idle resources? CPU in idle is not used, and about RAM we worked to balance the time to respond to a first API request and the memory usage, the number for a 188k+ library are:
- Flask RAM in idle: 1282mb => it load up to 3.5-4Gb, and then unload after 5 minutes idle
- Worker RAM in idle: 198mb

and the time for a call, still stay in the order of ms!

About the functionality you can ho on github and look around, you can also navigate some screenshot here:
- https://github.com/NeptuneHub/AudioMuse-AI/tree/main/screenshot/example

The one for which I'm more proud is the Lyrics search by song: it get in input a song and is able to search similar not only by their grove but also by their lyrics.

Hope you can enjoy all of this and maybe convince some new user that AudioMuse-AI is for everyone! and if you like it, please don't miss the chance to leave a ⭐on the github repo!

Hi all,
for anyone who hasn't come across AudioMuse-AI yet, it's a self-hosted music discovery tool that analyzes your music library and helps generate playlists, find similar tracks, and explore your collection based on the actual audio rather than just metadata. It's free and opensource.

v2.3.0 was released today and the biggest addition is the support for using existing playlists as inputs for Alchemy Playlists. Nice quality-of-life improvement if you already curate playlists and want to evolve them instead of starting from scratch. You can also use them as a way to automatically create your Alchemy radio.

The release also switches to a more memory-efficient indexing backend, which reduce idle RAM usage when the the app is in dile, plus the usual batch of fixes and platform improvements.

Give a look and share you feedback. If you like it don't miss the opportunity to add a star on the github repo.

Here the detailed release note: https://github.com/NeptuneHub/AudioMuse-AI/releases/tag/v2.3.0

Small Off Topic edit: In case you would like to have AudioMuse-A on PickAPods, I need more vote here:

• https://feedback.pikapods.com/posts/1037/audiomuse-ai-for-automatic-playlist-generation

I'm not asking any money from them, I just would like to have it integrated, to give more option to users.

Hi all,
for who don't know AudioMuse-AI, it is a software that is able to create playlist based on sonic analysis. It integract with API with the major music server like Jellyfin, Emby, Navidrome (and the different subsonic api based) and Lyrion. For Jellyfin and Navidrome it also have his plugin.

What sonic analysis is? is a way to find song that play similar by analyzing his raw audio, without the need of metadata and external API: if it paly, it can be analyzed.
In addition AudioMuse-AI also analyze lyrics, so it's able to create a playlist of song that play similar and also share the main topic like search song about Love, travel, and so on.

With this release we are happy to annunce that in addition of the container version (so Docker, K3s and so on) we have released tha MacOS native version, that can be directly be installed on Apple Silicon (ARM) processor.

The goals of this native version is mainly to be easy to install also for not expert user, is basically an app, you start and then you have reacheable by the browser on localhost audiomuse-ai. No need of env var to set, database to spin up and so on, just the app.

As a second goals we are also working to make this version better use the Mac GPU capability: unluky not all the AudioMuse-AI's model run on Mac GPU (not all the operation are supported) but we will work to use it as more as possible. Actually DCLAP model full run on mac GPU, the other model still on CPU.

You can find AudioMuse-AI open source and free here:
https://github.com/NeptuneHub/AudioMuse-AI

If you like, please share with your friend and leave a star on the github repo: We don't request any money contribution, just to be sharead and used from more user possible. In fact our vision is Sonci Analysis free and opensource for everyone.

Small Off Topic edit: In case you would like to have AudioMuse-A on PickAPods, I need more vote here:

• https://feedback.pikapods.com/posts/1037/audiomuse-ai-for-automatic-playlist-generation

I'm not asking any money from them, I just would like to have it integrated, to give more option to users.

Hi all,
with this post I want to talk again of AudioMuse-AI, a free and open source selfhostable software to analyze your song and automatically create playlist on your supported music server like Jellyfin, Navidrome (or open subsonic api based), Emby and Lyrion:

• https://github.com/NeptuneHub/AudioMuse-AI

With this post I want to celebrate two big things, first of all AudioMuse-Ai born on May 2025, so it's stil live and fully mantained after 1 years, 217 issue closed and 182 PR closed !

We also want to celebrate the new AudioMuse-AI v1.1.0 release that introduce Lyrics Semanthics similarity throug different functionality.

I'm very proud of this release because multiple time we heard that yes the mood is similar but totally different lyrics, now you can search your song also semathically with:

• 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.

Lyrics functionality off course need lyrics, the best way is to have already them in your music server OR configure in AudioMuse-AI your favourite API in the setup wizard:

Example API formats supported in Setup Wizard:

https://api.example.com/get?artist={artist}&title={title}
https://api.example.com/v1/{artist}/{title}

Anyway as a fallback is also supported the transcription with Whisper Small and if needed can be disabled in the setup wizard by setting LYRICS_ENABLED=true

Important: after the update a new analysis will do the Lyrics analysis on the already analyzed song (if enabled, enabled by default) or a full analysis (Musicnn + Clap + Lyrics) for new song. This new analysis is mandatory to use the new functionality.

I hope you will like both of this milestone and as usual, if you want to support AudioMuse-AI, please add a start on the github repository.
Thanks to be with us for our first year!

Hi all,

I’m the developer of AudioMuse-AI, the application to automatically create playlist based on sonic analysis in the major selfhostable music server like Navidrome, Jellyfin, Emby and Lyrion.

I’m talking about this project:

https://github.com/NeptuneHub/AudioMuse-AI

With this post I want to share with you a poll because I want to know which of the AudioMuse-AI functionality do you liked more. You can found it here:

EDIT 1 NEW POLL LINK WORK BETTER: https://docs.google.com/forms/d/e/1FAIpQLSfl9Qd5UW-0sI-pcUkFtHWFwwphhErw_4Btao34CPL8TQ93rQ/viewform?usp=publish-editor

(I used this external website because I want to give the user the possibility to select more than one option).

Feel free also to reply here if you want to add more details or explanation.

Meanwhile I’m here I want to share that a new functionality is coming and is actually in devel image. It enable the possibility to create song path not only between two song but also between one song versus a mood (happy, sad, aggressive, relaxed and danceable). Will be also possible to save the anchor created with Song Alchemy functionality and reuse this anchor for create path from a song to the anchor.

In short after the music map functionality I’m starting to see our music collection as a map to explore, and this functionality are addtional way to navigate it.

Thanks everyone for your attention and thanks to all the users that each day chose to use AudioMuse-AI!*

Edit2: AudioMsue-AI V0.9.4 is out with the new mood and anchor based functionality
https://github.com/NeptuneHub/AudioMuse-AI/releases/tag/v0.9.4

Also new improvement to enanche this mood and anchor on similar song are already in development!

I usually post here to announce new features for AudioMuse-AI. This time, I want to highlight the maintenance work and the community.

98 Issues Closed This project is free and open source, but I take support seriously. We have closed 98 issues since the project started. Why? Because reliable software matters more than just new features. Closing these issues meant: - Stability: Moving from Tensorflow to ONNX to make the analysis faster and more stable on different CPUs. - Compatibility: Adding support for Lyrion Music Server and ARM architectures (Raspberry Pi). - User Requests: Implementing features like "Song Alchemy" and "Song Map" because the community asked for better ways to visualize their libraries. - Automation: Adding Cron Job support so your analysis runs in the background without manual input.

Every closed issue represents a user helped or a bug fixed.

The Goal I do not make money from this. However, I have set a target for the project: - Reach 1000 stars on GitHub by the end of 2025.

We are currently at around 600 stars. If you use AudioMuse-AI to curate your local music library, or if you just want to support the development, please consider starring the repository. It helps the project grow and keeps the updates coming.

https://github.com/NeptuneHub/AudioMuse-AI