Delete utils.py

utils.py

@@ -1,39 +0,0 @@
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-
-def get_mask_from_lengths(lengths):
-    max_len = torch.max(lengths).item()
-    ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len))
-    mask = (ids < lengths.unsqueeze(1)).byte()
-    #  mask = (ids < lengths.unsqueeze(1).cuda()).cpu()
-    #  mask = mask.byte()
-    return mask
-
-
-# probably I won't use it from here
-def load_wav_to_torch(full_path, sr):
-    sampling_rate, data = read(full_path)
-    assert sr == sampling_rate, "{} SR doesn't match {} on path {}".format(
-        sr, sampling_rate, full_path)
-    return torch.FloatTensor(data.astype(np.float32))
-
-
-# probably I won't use it from here
-def load_filepaths_and_text(filename, sort_by_length, split="|"):
-    with open(filename, encoding='utf-8') as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-
-    if sort_by_length:
-        filepaths_and_text.sort(key=lambda x: len(x[1]))
-
-    return filepaths_and_text
-
-
-def to_gpu(x):
-    x = x.contiguous()
-
-    if torch.cuda.is_available():
-        x = x.cuda(non_blocking=True)  # I understand this lets asynchronous processing
-    return torch.autograd.Variable(x)