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32 lines
893 B
32 lines
893 B
import numpy as np
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from scipy.io.wavfile import read
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import torch
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def get_mask_from_lengths(lengths):
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max_len = torch.max(lengths)
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ids = torch.arange(0, max_len).long().cuda()
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mask = (ids < lengths.unsqueeze(1)).byte()
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return mask
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def load_wav_to_torch(full_path, sr):
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sampling_rate, data = read(full_path)
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assert sr == sampling_rate, "{} SR doesn't match {} on path {}".format(
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sr, sampling_rate, full_path)
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return torch.FloatTensor(data.astype(np.float32))
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def load_filepaths_and_text(filename, sort_by_length, split="|"):
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with open(filename, encoding='utf-8') as f:
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filepaths_and_text = [line.strip().split(split) for line in f]
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if sort_by_length:
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filepaths_and_text.sort(key=lambda x: len(x[1]))
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return filepaths_and_text
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def to_gpu(x):
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x = x.contiguous().cuda(async=True)
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return torch.autograd.Variable(x)
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