Do you have a music album as a single file (locally or on YouTube), with all its tracks joined together? Do you want to split that album in its single tracks? Do you want to tag these tracks so your music player can get all the required info from them?

This script is for you!

0. Install ffmpeg
1. Install Python 3
2. Install pip3
3. Install virtualenv (optional)
4. Fork/Clone/Download this repository
5. virtualenv -p /usr/bin/python3 venv (optional)
   • source venv/bin/activate
6. Install the required packages via pip
   • pip install -r requirements/prod.txt

1. Create a copy of the tracks.txt.example, rename it as tracks.txt and delete the lines starting with #.
2. Open tracks.txt
3. Add tracks info in this format:
   • <start-time> - <title>
   • A track on each line
   • See Examples section, many other formats supported
4. Run the script
   • Basic usage: python split.py -mp3 <path/to/your/album.mp3>
   • More in the Examples section
5. Wait for the splitting process to complete
6. You will find your tracks in the splits folder

1. Copy the YouTube URL of the album you want to download and split
2. Find in the YouTube comments the tracklist with start-time and title
3. Create a copy of the tracks.txt.example, rename it as tracks.txt and delete the lines starting with #.
4. Open tracks.txt
5. Copy the tracklist in the file, adjusting for the supported formats
   • <start-time> - <title>
   • A track on each line
6. Run the script
   • Basic usage: python split.py -yt <youtube_url>
   • More in the Examples section
7. Wait for the Download and for the conversion
8. Wait for the splitting process to complete
9. You will find your tracks in the splits folder

python upload_s3.py -b "s3bucketname" -p "chicken - farm" --album "Test Album" --artist "artist name" -ak "UNK" -bk "noalbum"
  -h, --help            show this help message and exit
  -a ARTIST, --artist ARTIST
                        Specify the artist that the json is tagged with.
                        Default: no tag
  -A ALBUM, --album ALBUM
                        Specify the album that the json is tagged with .
                        Default: no tag
  -p PATH, --path PATH  Specify the relative path to tracks folder. Default:
                        tracks.txt
  -ak ARTISTKEY, --artistkey ARTISTKEY
                        Specify the artist key value. Default: UNK
  -bk ALBUMKEY, --albumkey ALBUMKEY
                        Specify the album key value. Default: noalbum
  -b BUCKET, --bucket BUCKET
                        Specify the S3 bucket name.
  -t TRACKS, --tracks TRACKS
                        Specify the tracks file. Default: tracks.json

1. This read trakcs.json generated from split.py by default
2. It will build a playlist_ARTISTKEY.json
3. Upload the playlist to S3 bucket inside ARTISTKEY/ALBUMKEY/ subdirectory
4. Upload all the mp3 to S3 bucket inside ARTISTKEY/ALBUMKEY/ subdirectory
5. Add or append this album to existing dharmaCast_ARTISTKEY.json in ARTISTKEY subdirectory
6. Append new ARTISTKEY to existing dharmaCast.json in root directory

1. This is the album I want to download and split: https://www.youtube.com/watch?v=p_uqD4ng9hw
2. I find the tracklist in the comments and I copy that in tracks.txt, eventually adjusting it to a supported format for the tracklist

00:06 - When I Was Young
...
14:48 - Pretty Little Girl
<start-time> - <title> <--- remove this, just as a reminder of the format --->

3. I execute python split.py -yt https://www.youtube.com/watch?v=p_uqD4ng9hw and wait
4. Once the process is complete I open splits and I find all my songs:

    When I Was Young.mp3
    ...
    Pretty Little Girl.mp3

These songs are already mp3-tagged with their track name and track number, but not their author or their album, since we have not specified it.

1. I somehow got the file DogsEatingDogsAlbum.mp3 that I want to split
2. I set the tracklist in tracks.txt (same tracks as before)
3. I execute python split.py -mp3 DogsEatingDogsAlbum.mp3 --album "Dogs Eating Gods" --artist "blink-182" --folder "2012 - Dogs Eating Dogs"
4. The program will execute, it will split the album, and mp3-tag each track with the author and the album name I passed as a parameter (as well as track number and name). It will also put the files in the folder passed as an argument (instead of putting them in the default splits folder)

1. This is the album I want to download and split: https://www.youtube.com/watch?v=p_uqD4ng9hw
2. This is the Wikipedia page that has the album tracklist: https://en.wikipedia.org/wiki/Dogs\_Eating\_Dogs
3. I execute python split.py -yt https://www.youtube.com/watch?v=p_uqD4ng9hw --metadata https://en.wikipedia.org/wiki/Dogs\_Eating\_Dogs and wait
4. Once the process is complete I open splits and I find all my songs:

    When I Was Young.mp3
    ...
    Pretty Little Girl.mp3

These songs are already mp3-tagged with their track name and track number, but not their author or their album, since we have not specified it (specify them using the --author and --album flags).

The same thing works if you provide a link to the Amazon page for that album.

python split.py -h

These are just some examples.

• [hh:]mm:ss - Title
• Title - [hh:]mm:ss
• Title [hh:]mm:ss
• TrackNumber. Title [hh:]mm:ss
• TrackNumber - Title [hh:]mm:ss

To see which data would be extracted from the tracklist use the option --dry-run. It also works if you pass any external --metadata url.

Example:

python split.py
    --dry-run
    -yt https://www.youtube.com/watch?v=T44T8JdMOak
    --metadata https://www.amazon.com/Enema-State-Blink-182/dp/B00000IPAX

will output

**** DRY RUN ****
Matched with a metadata provider...
Parsing tracks.txt
Dumpweed [Explicit] *** 00:00:00
...
Anthem *** 00:31:40

process_dharma.py load ablum.csv file and upload/delete based on the flag in the file, x for upload, d for delete --dry-run is the only optional argument

./process_dharma.py

Check the Issues page

If you need any help just create an Issue or send me an email at the address you can find on my profile.

If you want to improve the code and submit a pull request feel free to do so.

GPL v3