Courtesy of LifeHacker, a headline that made my afternoon: Build Spotify Playlists from any Wikipedia Song List. Because I realised that someone finally had provided the functionality to automatically generate playlists from a list of songs, artist and title, on Spotify.
I had a look at doing this when I first signed up for Spotify over a year ago, but it was too complicated for my ancient programming skills. And now someone has done the necessary and there is Spot My Songs, which makes it possible to take a playlist off my favourite on-line radio station, France Inter Paris (FIP), and turn it into a Spotify playlist. Here's what I do:
- Go onto the FIP website, archives, and select a whole day's worth of tracks.
- Copy the list and paste it into Excel. Remove the times by using "Text to Columns" with a fixed width. Delete the first column.
- Copy the reduced list and take it to Notepad. Do a "Replace Text", replacing three consecutive spaces with a marker character. I used the tilde (~).
- Close Excel and re-open it (I had to do this to clear the "text-to-columns" from working). Paste the list back into Excel and do "Text to Columns" using the tilde as a delimiter. This splits the list into columns of Artist and Song.
- Clean this up. Sort on Column B to remove anything that doesn't have Artist|Song. Here you are removing the news. Also remove artist F.E.R. - FIP Loop, and anything else that doesn't look like Artist|Song.
- Save this list as a "tab-delimited text" file.
- Upload that text file to Spot My Songs, and drag the playlist it generates (it takes a few minutes) into Spotify as a new playlist. Et voilà! A day's worth of FIP on Spotify, minus any tracks it couldn't find.
I did a trial on yesterday's playlist and it found 71% of all the tracks - something like 145 out of 204 tracks. One thing it won't do well is to find the classical music - it tends to be listed in French on the FIP site, rather than the English titles it usually has on the GraceNote database.
Three minutes to a 10-hour FIP playlist. Can't be bad. Loving this already!