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) if data.dtype == np.int16: norm_fix = 32768 elif data.dtype == np.float16 or data.dtype == np.float32: norm_fix = 1. else: raise NotImplemented(f"Provided data dtype not supported: {data.dtype}") return (torch.FloatTensor(data.astype(np.float32)) / norm_fix, 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)