I’m trying to figure out an efficient way to detect when a user switches songs on Spotify. Currently, I’m using the /v1/me/player/currently-playing endpoint to fetch details about the song that’s playing. However, this endpoint doesn’t indicate when a new song starts, whether because the current song ended or the user manually skipped to the next one.
To avoid hitting rate limits with constant polling, I’m looking for a better method. For example, here’s a modified approach:
import requests
import time
def fetch_current_song():
url = 'https://api.spotify.com/v1/me/player/currently-playing'
response = requests.get(url)
song_info = response.json()
return song_info.get('item', {}).get('name', 'Unknown')
last_song = fetch_current_song()
while True:
new_song = fetch_current_song()
if new_song != last_song:
print('Song change detected!')
last_song = new_song
time.sleep(1)
Does anyone have ideas on a more robust solution to capture song changes in real time?
hey there! i’ve been messin with this too. have u tried the /v1/me/player endpoint? it gives more info like timestamp n progress. u could compare that to track length to guess when it’ll end. might help cut down on api calls. good luck with ur project!
I’ve encountered similar challenges when working with the Spotify API. One approach that’s proven effective is utilizing WebSockets for real-time updates. By establishing a persistent connection, you can receive immediate notifications about track changes without constant polling.
Another method worth exploring is implementing a background worker that periodically checks the player state with an adaptive interval. Starting with a longer interval and decreasing it as you approach the expected end of the track can reduce API calls while still maintaining accuracy.
Remember to account for edge cases such as advertisements, podcast episodes, or user-initiated skips, as these can affect timing-based predictions. Always refer to Spotify’s updated documentation for the latest best practices and rate limit information.