66 lines
2.8 KiB
Python
66 lines
2.8 KiB
Python
# *****************************************************************************
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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# * Neither the name of the NVIDIA CORPORATION nor the
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# names of its contributors may be used to endorse or promote products
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# derived from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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# *****************************************************************************
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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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import os
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def get_mask_from_lengths(lengths):
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max_len = torch.max(lengths).item()
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ids = torch.arange(0, max_len, device=lengths.device, dtype=lengths.dtype)
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mask = (ids < lengths.unsqueeze(1)).byte()
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mask = torch.le(mask, 0)
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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(dataset_path, filename, split="|"):
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with open(filename, encoding='utf-8') as f:
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def split_line(root, line):
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parts = line.strip().split(split)
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if len(parts) > 2:
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raise Exception(
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"incorrect line format for file: {}".format(filename))
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path = os.path.join(root, parts[0])
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text = parts[1]
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return path,text
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filepaths_and_text = [split_line(dataset_path, line) for line in f]
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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 x
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