dejavu
beets
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dejavu | beets | |
---|---|---|
15 | 186 | |
6,289 | 12,374 | |
- | 0.6% | |
0.0 | 9.7 | |
8 months ago | 3 days ago | |
Python | Python | |
MIT License | MIT License |
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dejavu
- Audio Fingerprinting and Recognition in Python
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Contacting Collectors or Creating API to help with searching
This doesn't seem hard, you can use something like this to dwoanload the songs: https://stackoverflow.com/a/27481870/6151784 and something like this to calculate how much they match: https://github.com/worldveil/dejavu The question is would you create a (dedicated) server to do your work? Or your own pc? You could also create a very simple page where someone would paste you a YouTube profile URL and you would check all songs of this URL. Also to have a db and save information about the matching and which youtube profiles have alsready been checked. Something like that could work.
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Tiny bit of experience but need to compile a Github program. What is the best video / resource to learn to do this quickly?
If you read the installation.md file it clearly states that it has only been tested on UNIX systems, so you might be on your own trying to get it to wor in windows.
- Help needed with school project
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Identification of all usages of OSTs in Made in Abyss (S1)
Using neural networks seems complicated, did you tried audio fingerprinting? I have been using this audio fingerprinting library to power this anime song synchronization script. You can check Panako and dejavu too.
- Dejavu โ Audio fingerprinting and recognition algorithm
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fingerprinting sections of audio from file
I want to say these few seconds match these few seconds from a different audio track. Using dejavu raw has overhead I do not need/want and hence I've been fiddling around with the fingerprint script. When modifying the global variables I can get better hits or worse hits, I will admit that even after reading there recommended article and many other sources, I can't find some good explanations about the mathematics behind the filtering after the specgram has been applied. As far as a I am aware we first apply filters to find/make fine points across the spectrogram after that we only check the distance between points along the time axis not the frequency or a hypotenuse (weird).
- Some information and advice about DDoS, from someone who was there during #opPayback
- List of resources
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Uploading an audio dataset into a database for comparison
I used a repo called https://github.com/worldveil/dejavu to compare audio hashed fingerprints and distinguish the difference between them.
beets
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Show HN: Synced lyrics database with a free, easy-to-use API
I was always frustrated that there is no solid source for synced lyrics that also offers decent API support. There is good ol' Crintsoft's MiniLyrics that is thankfully free software, was what I used a lot in my childhood, but unfortunately the API is highly obfuscated. Another popular choice is the Musixmatch API, which has a very large database of synced lyrics, but with "free" API that are reverse-engineered from their app, you will quickly run into rate-limit.
That's why I created LRCLIB. It's aimed to provide completely free synchronized lyrics for everyone, especially for FOSS music players, with zero profit intention. It currently has nearly 3,000,000 (not deduplicated) lyrics in database. You can also contribute to the database by adding and syncing lyrics for your favorite songs using the LRCGET client.
I'm trying my best to make LRCLIB server-side code open-source as soon as possible. But right now, full LRCLIB's database dumps have already been uploaded regularly and publicly, which are simply sqlite3 files. Feel free to download, look at or do anything you want with the database at https://lrclib.net/db-dumps.
Many open-source projects have already begun integrating LRCLIB, including:
- beets - music library metadata management (https://github.com/beetbox/beets)
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Finally moving to Navidrome... but how to best manage files and metadata?
I just ssh onto my server and use beets to remotely organize my navidrome collection and edit metadata. Beets has lots of auto-tagging features and I rarely need to edit anything manually. Works great if you are ok with using the command line.
- Beets: The music geek's media organizer
- Manage offline music?
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Musicserver that works with folders, not albums
You could try https://github.com/beetbox/beets but it seemed very manual and extremely slow. I had better luck with Picard.
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Ask HN: Flac/MP3 listeners: How do you store/play your music?
Honestly? I use https://beets.io/ to organise all my FLAC on my NAS.
I expose the /Music directory over NFC.
I use https://kodi.tv/ to stream music to my amp. I manually pick the album I want to listen to.
Kodi also has a fairly reasonable web UI.
Keep it simple.
- How do you keep your music library organized?
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Library Organiser?
If you're technically inclined, there's beets.
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anyone else wish this was still a thing?? scrolling album art - ios 6.1.3
You should check out beets.
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Is there a faster way to organize music torrents into a specific folder?
Yes, you have the torrent client call beets.io on the folder and have beets configured.
What are some alternatives?
django-elastic-transcoder - Django + AWS Elastic Transcoder
Lidarr - Looks and smells like Sonarr but made for music.
m3u8 - Python m3u8 Parser for HTTP Live Streaming (HLS) Transmissions
Navidrome Music Server - ๐งโ๏ธ Modern Music Server and Streamer compatible with Subsonic/Airsonic
audiolazy - Expressive Digital Signal Processing (DSP) package for Python
picard - A cross-platform music tagger powered by the MusicBrainz database. Picard organizes your music collection by updating your tags, renaming your files, and sorting them into a folder structure, exactly the way you want it.
speech-to-text-websockets-python
Airsonic - :satellite: :cloud: :notes:Airsonic, a Free and Open Source community driven media server (fork of Subsonic and Libresonic)
pyechonest - Python client for the Echo Nest API
Ampache - A web based audio/video streaming application and file manager allowing you to access your music & videos from anywhere, using almost any internet enabled device.
pyAudioAnalysis - Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
librosa - Python library for audio and music analysis