Spaces:
Build error
Build error
| """ | |
| BSD 3-Clause License | |
| Copyright (c) 2018, NVIDIA Corporation | |
| All rights reserved. | |
| Redistribution and use in source and binary forms, with or without | |
| modification, are permitted provided that the following conditions are met: | |
| * Redistributions of source code must retain the above copyright notice, this | |
| list of conditions and the following disclaimer. | |
| * Redistributions in binary form must reproduce the above copyright notice, | |
| this list of conditions and the following disclaimer in the documentation | |
| and/or other materials provided with the distribution. | |
| * Neither the name of the copyright holder nor the names of its | |
| contributors may be used to endorse or promote products derived from | |
| this software without specific prior written permission. | |
| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
| AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | |
| IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | |
| DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE | |
| FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL | |
| DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR | |
| SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | |
| CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | |
| OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | |
| OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | |
| """ | |
| import numpy as np | |
| from scipy.io.wavfile import read | |
| import torch | |
| def get_mask_from_lengths(lengths, device, max_len=None): | |
| if not max_len: | |
| max_len = torch.max(lengths).item() | |
| ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len)).to(device) | |
| mask = (ids < lengths.to(device).unsqueeze(1)).bool() | |
| return mask | |
| def load_wav_to_torch(full_path): | |
| sampling_rate, data = read(full_path) | |
| return torch.FloatTensor(data.astype(np.float32)), 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] | |
| return filepaths_and_text | |
| def to_gpu(x): | |
| x = x.contiguous().cuda() | |
| return torch.autograd.Variable(x) | |
| def get_sizes(data): | |
| _, input_lengths, _, _, output_lengths = data | |
| output_length_size = torch.max(output_lengths.data).item() | |
| input_length_size = torch.max(input_lengths.data).item() | |
| return input_length_size, output_length_size | |
| def get_y(data): | |
| _, _, mel_padded, gate_padded, _ = data | |
| mel_padded = to_gpu(mel_padded).float() | |
| gate_padded = to_gpu(gate_padded).float() | |
| return mel_padded, gate_padded | |
| def get_x(data): | |
| text_padded, input_lengths, mel_padded, _, output_lengths = data | |
| text_padded = to_gpu(text_padded).long() | |
| input_lengths = to_gpu(input_lengths).long() | |
| mel_padded = to_gpu(mel_padded).float() | |
| output_lengths = to_gpu(output_lengths).long() | |
| return text_padded, input_lengths, mel_padded, output_lengths | |
| def process_batch(batch, model): | |
| input_length_size, output_length_size = get_sizes(batch) | |
| y = get_y(batch) | |
| y_pred = model(batch, mask_size=output_length_size, alignment_mask_size=input_length_size) | |
| return y, y_pred | |