I have been trying out some of the Torchaudio functionalities and I can't seem to figure out why lowpass_biquad is running slower on the GPU than on the CPU. And this is true for other effects like, phaser, flanger, overdrive, which are even slower. Here I am pasting the example for the lowpass filter, but I applay the other effectes the same way to obtain the measurements. The example code is taken from this issue, which seems to have been resolved, but it's still slower on the GPU. import time import torch from torchaudio.functional import lowpass_biquad gpu_device = torch.device('cuda:0') cpu_device = torch.device('cpu') seconds = 1000 sample_rate = 44100 cutoff_freq = 1000. Q = .7 # Run in cpu x = torch.rand(sample_rate * seconds, device=cpu_device) begin = time.time() y = lowpass_biquad(x, sample_rate, cutoff_freq, Q) print(f'Run in cpu: {time.time() - begin}') # Run in gpu x = torch.rand(sample_rate * seconds, device=gpu_device) begin = time.time() y = lowpass_biquad(x, sample_rate, cutoff_freq, Q) torch.cuda.synchronize() print(f'Run in gpu: {time.time() - begin}') Run in cpu: 1.6084413528442383 Run in gpu: 6.183292865753174 For example for the overdrive effect, the GPU is more than 1000x slower. It would be understandable, if the Torchaudio doesn't have the GPU implementation of the said effects, but their documentation seems to suggest they do. Am I doing something wrong? Continue reading...
