training on voxceleb1+2 is very slow?
See original GitHub issueDear all: I noticed that when training on voxceleb1+2, it will take me up to 25 hours for single epoch. and even with ddp on 4 gpu cards, the training speed does not reduce at all. I guess the cpu is the bottleneck? anyone has the same phenomena? thank you.
7%|████████▎ | 16569/241547 [1:45:07<25:09:56, 2.48it/s, train_loss=13
Issue Analytics
- State:
- Created 2 years ago
- Comments:35 (4 by maintainers)
Top Results From Across the Web
Build a SRE Challenge System: Lessons from VoxSRC 2022 ...
Abstract. Different speaker recognition challenges have been held to assess the speaker verification system in the wild and probe the performance limit.
Read more >VoxCeleb
We host a VoxCeleb Speaker Recognition Challenge (VoxSRC) at Interspeech every year. This is a speaker recognition challenge held on the VoxCeleb datasets!...
Read more >VoxCeleb-PT – a dataset for a speech processing course
PDF | This paper introduces VoxCeleb-PT, a small dataset of voices of Portuguese celebrities that can be used as a language-specific ...
Read more >CNN WITH PHONETIC ATTENTION FOR TEXT ... - Microsoft
ABSTRACT. Text-independent speaker verification imposes no constraints on the spoken content and usually needs long observations.
Read more >arXiv:2009.14153v1 [eess.AS] 29 Sep 2020
Clova Baseline System for the VoxCeleb Speaker Recognition Challenge 2020 ... During training, we use a fixed length 2-second temporal seg-.
Read more >Top Related Medium Post
No results found
Top Related StackOverflow Question
No results found
Troubleshoot Live Code
Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start FreeTop Related Reddit Thread
No results found
Top Related Hackernoon Post
No results found
Top Related Tweet
No results found
Top Related Dev.to Post
No results found
Top Related Hashnode Post
No results found
Top GitHub Comments
On LIA clusters you can increase this value as our nfs is pretty terrible. (4-8) to be tested. It’s the number of threads used to load the data. Having 100% usage is pretty normal as well, but the training should not be that long.
Hello,
It seems that the issue has been answered. Therefore, I am closing this issue. If you have any other questions, please feel free to reopen the issue! Thanks. 🙂