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| # ***************************************************************************** | |
| # 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 NVIDIA CORPORATION 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 NVIDIA CORPORATION 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 argparse | |
| from python.common.text import get_symbols | |
| def parse_fastpitch_args(symbols_alphabet, parent, add_help=False): | |
| """ | |
| Parse commandline arguments. | |
| """ | |
| parser = argparse.ArgumentParser(parents=[parent], add_help=add_help, allow_abbrev=False) | |
| io = parser.add_argument_group('io parameters') | |
| io.add_argument('--n-mel-channels', default=80, type=int, help='Number of bins in mel-spectrograms') | |
| io.add_argument('--max-seq-len', default=2048, type=int, help='') | |
| global symbols | |
| from python.common.text import get_symbols | |
| len_symbols = len(get_symbols(symbols_alphabet)) | |
| symbols = parser.add_argument_group('symbols parameters') | |
| symbols.add_argument('--symbol-set', default=symbols_alphabet, type=str) | |
| symbols.add_argument('--n-symbols', default=len_symbols, type=int, help='Number of symbols in dictionary') | |
| symbols.add_argument('--n-speakers', default=1, type=int) | |
| symbols.add_argument('--padding-idx', default=0, type=int, help='Index of padding symbol in dictionary') | |
| symbols.add_argument('--symbols-embedding-dim', default=384, type=int, help='Input embedding dimension') | |
| in_fft = parser.add_argument_group('input FFT parameters') | |
| in_fft.add_argument('--in-fft-n-layers', default=6, type=int, help='Number of FFT blocks') | |
| in_fft.add_argument('--in-fft-n-heads', default=1, type=int, help='Number of attention heads') | |
| in_fft.add_argument('--in-fft-d-head', default=64, type=int, help='Dim of attention heads') | |
| in_fft.add_argument('--in-fft-conv1d-kernel-size', default=3, type=int, help='Conv-1D kernel size') | |
| in_fft.add_argument('--in-fft-conv1d-filter-size', default=1536, type=int, help='Conv-1D filter size') | |
| in_fft.add_argument('--in-fft-output-size', default=384, type=int, help='Output dim') | |
| in_fft.add_argument('--p-in-fft-dropout', default=0.1, type=float, help='Dropout probability') | |
| in_fft.add_argument('--p-in-fft-dropatt', default=0.1, type=float, help='Multi-head attention dropout') | |
| in_fft.add_argument('--p-in-fft-dropemb', default=0.0, type=float, help='Dropout added to word+positional embeddings') | |
| out_fft = parser.add_argument_group('output FFT parameters') | |
| out_fft.add_argument('--out-fft-n-layers', default=6, type=int, help='Number of FFT blocks') | |
| out_fft.add_argument('--out-fft-n-heads', default=1, type=int, help='Number of attention heads') | |
| out_fft.add_argument('--out-fft-d-head', default=64, type=int, help='Dim of attention head') | |
| out_fft.add_argument('--out-fft-conv1d-kernel-size', default=3, type=int, help='Conv-1D kernel size') | |
| out_fft.add_argument('--out-fft-conv1d-filter-size', default=1536, type=int, help='Conv-1D filter size') | |
| out_fft.add_argument('--out-fft-output-size', default=384, type=int, help='Output dim') | |
| out_fft.add_argument('--p-out-fft-dropout', default=0.1, type=float, help='Dropout probability for out_fft') | |
| out_fft.add_argument('--p-out-fft-dropatt', default=0.1, type=float, help='Multi-head attention dropout') | |
| out_fft.add_argument('--p-out-fft-dropemb', default=0.0, type=float, help='Dropout added to word+positional embeddings') | |
| dur_pred = parser.add_argument_group('duration predictor parameters') | |
| dur_pred.add_argument('--dur-predictor-kernel-size', default=3, type=int, help='Duration predictor conv-1D kernel size') | |
| dur_pred.add_argument('--dur-predictor-filter-size', default=256, type=int, help='Duration predictor conv-1D filter size') | |
| dur_pred.add_argument('--p-dur-predictor-dropout', default=0.1, type=float, help='Dropout probability for duration predictor') | |
| dur_pred.add_argument('--dur-predictor-n-layers', default=2, type=int, help='Number of conv-1D layers') | |
| pitch_pred = parser.add_argument_group('pitch predictor parameters') | |
| pitch_pred.add_argument('--pitch-predictor-kernel-size', default=3, type=int, help='Pitch predictor conv-1D kernel size') | |
| pitch_pred.add_argument('--pitch-predictor-filter-size', default=256, type=int, help='Pitch predictor conv-1D filter size') | |
| pitch_pred.add_argument('--p-pitch-predictor-dropout', default=0.1, type=float, help='Pitch probability for pitch predictor') | |
| pitch_pred.add_argument('--pitch-predictor-n-layers', default=2, type=int, help='Number of conv-1D layers') | |
| energy_pred = parser.add_argument_group('energy predictor parameters') | |
| # energy_pred.add_argument('--energy-conditioning', action='store_true') | |
| energy_pred.add_argument('--energy-conditioning', default=True) | |
| energy_pred.add_argument('--energy-predictor-kernel-size', default=3, type=int, help='Pitch predictor conv-1D kernel size') | |
| energy_pred.add_argument('--energy-predictor-filter-size', default=256, type=int, help='Pitch predictor conv-1D filter size') | |
| energy_pred.add_argument('--p-energy-predictor-dropout', default=0.1, type=float, help='Pitch probability for energy predictor') | |
| energy_pred.add_argument('--energy-predictor-n-layers', default=2, type=int, help='Number of conv-1D layers') | |
| # cond = parser.add_argument_group('conditioning parameters') | |
| parser.add_argument('--pitch-embedding-kernel-size', default=3, type=int, help='Pitch embedding conv-1D kernel size') | |
| parser.add_argument('--energy-embedding-kernel-size', default=3, type=int, help='Pitch embedding conv-1D kernel size') | |
| parser.add_argument('--speaker-emb-weight', type=float, default=1.0, help='Scale speaker embedding') | |
| # # cond = parser.add_argument_group('conditioning parameters') | |
| # parser.add_argument('--pitch-embedding-kernel-size', default=3, type=int, help='Pitch embedding conv-1D kernel size') | |
| # parser.add_argument('--speaker-emb-weight', type=float, default=1.0, help='Scale speaker embedding') | |
| return parser | |