utils.py

import numpy as np
from scipy.io.wavfile import read
import torch


from hparams import create_hparams
#hparam = create_hparams()
#hparam.cuda_enabled = False

def get_mask_from_lengths(lengths):
    max_len = torch.max(lengths).item()
    
    #if hparam.cuda_enabled :
    if create_hparams.cuda_enabled :
        ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len))
        mask = (ids < lengths.unsqueeze(1)).bool()
    else :
        ids = torch.arange(0, max_len, out=torch.LongTensor(max_len))
        mask = (ids < lengths.unsqueeze(1)).bool()
    
    return mask



def load_wav_to_torch(full_path):
    sampling_rate, data = read(full_path)
    return torch.FloatTensor(data.astype(np.float32)), sampling_rate


def load_filepaths_and_text(filename, split="|"):
    with open(filename, encoding='utf-8') as f:
        filepaths_and_text = [line.strip().split(split) for line in f]
    return filepaths_and_text


def to_gpu(x):
    x = x.contiguous()

    if torch.cuda.is_available():
        x = x.cuda(non_blocking=True)
    return torch.autograd.Variable(x)