34 lines
1021 B
Python
34 lines
1021 B
Python
import os.path
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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, max_len=None):
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if max_len is None:
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max_len = torch.max(lengths).item()
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ids = torch.arange(0, max_len, out=torch.LongTensor(max_len)).to(lengths.device)
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mask = (ids < lengths.unsqueeze(1)).bool()
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return mask
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def load_wav_to_torch(full_path):
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sampling_rate, data = read(full_path)
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return torch.FloatTensor(data.astype(np.float32)), sampling_rate
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def load_filepaths_and_text(filename, 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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base = os.path.dirname(filename)
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for j in range(len(filepaths_and_text)):
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filepaths_and_text[j][0] = os.path.join(base, filepaths_and_text[j][0])
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return filepaths_and_text
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def to_gpu(x):
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x = x.contiguous()
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if torch.cuda.is_available():
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x = x.cuda(non_blocking=True)
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return torch.autograd.Variable(x) |