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@ -4,29 +4,26 @@ 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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max_len = torch.max(lengths).item() |
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ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len)) |
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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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def load_wav_to_torch(full_path): |
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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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return torch.FloatTensor(data.astype(np.float32)), sampling_rate |
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def load_filepaths_and_text(filename, sort_by_length, split="|"): |
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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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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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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) |