About the time complexity of attach_memory_bank.py

Hi,

I'm trying to reproduce the results on ljspeech dataset, and I noticed that the code in this section (

Line 132 in ca2f814

kmeans = KMeans(n_clusters=1000, random_state=0, n_init="auto").fit(X)

) takes a very long time to execute. It appears that the shape of X is (7066362, 192), which leads to a high computational complexity when calculating the distances.

I was wondering if you could share with me how much time it takes for this code to run on your machine?
Thanks

Yeah got same issue, for me it had taken around around 10 hours on machine. It runs on CPU that's why it is more slower. And this time is for n_init=1 which is running 1 iteration equivalent to n_init="auto".

Hi,do you have follow question:

12399 torch.Size([192, 651])
(7066362, 192)
Traceback (most recent call last):
File "attach_memory_bank.py", line 174, in
centers = k_means(zs)
File "attach_memory_bank.py", line 132, in k_means
kmeans = KMeans(n_clusters=1000, random_state=0, n_init="auto").fit(X)
File "/home/WeiWB/anaconda3/envs/tts/lib/python3.7/site-packages/sklearn/cluster/_kmeans.py", line 1146, in fit
self._check_params(X)
File "/home/WeiWB/anaconda3/envs/tts/lib/python3.7/site-packages/sklearn/cluster/_kmeans.py", line 937, in _check_params
if self.n_init <= 0:
TypeError: '<=' not supported between instances of 'str' and 'int'