utils.py

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

max_wav_value=32768.0

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()
    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)

def str2bool(v):
    return v.lower() in ('true', '1')

def makedirs(path):
    if not os.path.exists(path):
        print(" [*] Make directories : {}".format(path))
        os.makedirs(path)

def add_postfix(path, postfix):
    path_without_ext, ext = path.rsplit('.', 1)
    return "{}.{}.{}".format(path_without_ext, postfix, ext)