Discussions for training / VoxSRC

• Changing --n_mels from 40 to 64 leads to a small increase in performance.
• Using --log_input also leads to a small increase in performance.
• Combining two loss functions (e.g. angleproto and softmax) sometimes has positive effect. This should be defined as a new loss function that returns the sum of two losses in the loss directory.
• Zero padding of the input causes to a significant adverse effect on performance. When there is a large variation in the length of input audio files (e.g. VoxSRC), I recommend --eval_frames 0 which uses whatever length of audio is available without padding or cropping.

For example, this configuration gives 1.98% EER using the standard train and test lists. I believe that many of you have trained better models using this trainer. I would appreciate if you are able to share your knowledge!

[ukemamaster]

@zh794390558 Did you solve your problem of slow training? I am having the same problem, one epoch takes almost 3 hours (sometimes more than that) on 8 Tesla T4 GPUs using distributed training.

But my case is a little different, explained here in detail.

If you have solved your problem, could you please share your solution?

[yy835055664]

• Using --log_input also leads to a small increase in performance.

Hello, Joonson. Thank you for your ideas. For——log_input features, what is the principle of this method? How to improve performance. Hope you can reply Thank you