youssefavx
youssefavx
Never mind! Sorry for the bother. I wrote this function: ``` import eng_to_ipa as ipa def get_homophones(word): words_that_sound_the_same = [] the_way_this_word_looks = word the_way_this_word_sounds = ipa.convert(word) words_that_contain_that_sound = ipa.contains(the_way_this_word_sounds) for...
Just reopening to ask for any tips on this problem: I'm trying to create a function that'll basically give you a homophone that is made up of multiple words. For...
It also says "aperture quit unexpectedly" and similar error happens when clicking three dots on the menu
After looking at the code more closely, I wonder if this solves it: https://github.com/openai/jukebox/blob/08efbbc1d4ed1a3cef96e08a931944c8b4d63bb3/jukebox/prior/autoregressive.py#L177
@heewooj Thank you so much! I'll try training a top-level prior. How much data is recommended? And would a free Colab GPU be sufficient for this or is this something...
@prafullasd @heewooj I'll read more on this and give it a shot. Thanks guys!
@heewooj Wonderful! You guys are amazing.
Awesome to hear, thank you! Will do.
Here is what I have done so far: ``` from encoder import Model import operator from functools import reduce import numpy as np from numba import jit @jit(nopython=True) def cosine_similarity_numba(u:np.ndarray,...
Okay instead of using the vector from sentiment neuron, when I use SentenceBERT I get much higher accuracy it seems. I think a combination of these 2 models might get...