TeamNewPipe / TeamNewPipe/NewPipeExtractor
YouTube Music recommendations and audio streams come from normal YouTube
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- Dominant language
- Java
- Stars
- 2k
- Forks
- 602
- Avg merge
- 3d 6h
- Merged PRs (30d)
- 6
Description
This information was provided by a user via email.
The auto-play queue appears to be pulling recommendations from the standard YouTube algorithm, not the YouTube Music recommendation algorithm. As a result, it sometimes includes random or unrelated YouTube videos that are not music at all, instead of staying within a similar mood or genre. This is especially noticeable with all genres of music, where the queue may suddenly switch to unrelated genres or non-music content.
This is because YouTube Music tracks are being fetched and treated as regular YouTube videos rather than as music entities, which likely prevents access to YouTube Music’s mood- and genre-aware recommendation logic.
Moreover, the audio quality of music streams remains similar to standard YouTube / NewPipe audio, and does not match the richer sound quality available in the free tier of the official YouTube Music app or website. The YouTube Music app (even without a subscription) clearly delivers better audio quality compared to the YouTube app—and this difference is noticeable when compared to NewPipe as well.
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no source file, test, or entry point; begin by reproducing the recommendation queue and audio-quality differences described for YouTube Music tracks. Done would require the queue to use music-appropriate recommendations and the extracted streams to provide the reported higher quality, with tests or reproduction steps added to verify both behaviors.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- java
- Domain
- backend
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100