DL-Art-School/codes/models/tacotron2/taco_utils.py

34 lines
1021 B
Python

import os.path
import numpy as np
from scipy.io.wavfile import read
import torch
def get_mask_from_lengths(lengths, max_len=None):
if max_len is None:
max_len = torch.max(lengths).item()
ids = torch.arange(0, max_len, out=torch.LongTensor(max_len)).to(lengths.device)
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]
base = os.path.dirname(filename)
for j in range(len(filepaths_and_text)):
filepaths_and_text[j][0] = os.path.join(base, filepaths_and_text[j][0])
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)