291 lines
12 KiB
Python
291 lines
12 KiB
Python
# *****************************************************************************
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# Copyright (c) 2020, 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 argparse
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import json
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import time
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from pathlib import Path
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import parselmouth
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import torch
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import dllogger as DLLogger
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import numpy as np
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from dllogger import StdOutBackend, JSONStreamBackend, Verbosity
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from torch.utils.data import DataLoader
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from common import utils
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from inference import load_and_setup_model
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from tacotron2.data_function import TextMelLoader, TextMelCollate, batch_to_gpu
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def parse_args(parser):
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"""
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Parse commandline arguments.
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"""
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parser.add_argument('--tacotron2-checkpoint', type=str,
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help='full path to the generator checkpoint file')
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parser.add_argument('-b', '--batch-size', default=32, type=int)
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parser.add_argument('--log-file', type=str, default='nvlog.json',
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help='Filename for logging')
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# Mel extraction
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parser.add_argument('-d', '--dataset-path', type=str,
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default='./', help='Path to dataset')
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parser.add_argument('--wav-text-filelist', required=True,
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type=str, help='Path to file with audio paths and text')
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parser.add_argument('--text-cleaners', nargs='*',
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default=['english_cleaners'], type=str,
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help='Type of text cleaners for input text')
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parser.add_argument('--max-wav-value', default=32768.0, type=float,
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help='Maximum audiowave value')
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parser.add_argument('--sampling-rate', default=22050, type=int,
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help='Sampling rate')
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parser.add_argument('--filter-length', default=1024, type=int,
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help='Filter length')
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parser.add_argument('--hop-length', default=256, type=int,
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help='Hop (stride) length')
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parser.add_argument('--win-length', default=1024, type=int,
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help='Window length')
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parser.add_argument('--mel-fmin', default=0.0, type=float,
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help='Minimum mel frequency')
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parser.add_argument('--mel-fmax', default=8000.0, type=float,
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help='Maximum mel frequency')
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# Duration extraction
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parser.add_argument('--extract-mels', action='store_true',
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help='Calculate spectrograms from .wav files')
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parser.add_argument('--extract-mels-teacher', action='store_true',
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help='Extract Taco-generated mel-spectrograms for KD')
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parser.add_argument('--extract-durations', action='store_true',
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help='Extract char durations from attention matrices')
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parser.add_argument('--extract-attentions', action='store_true',
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help='Extract full attention matrices')
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parser.add_argument('--extract-pitch-mel', action='store_true',
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help='Extract pitch')
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parser.add_argument('--extract-pitch-char', action='store_true',
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help='Extract pitch averaged over input characters')
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parser.add_argument('--extract-pitch-trichar', action='store_true',
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help='Extract pitch averaged over input characters')
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parser.add_argument('--train-mode', action='store_true',
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help='Run the model in .train() mode')
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parser.add_argument('--cuda', action='store_true',
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help='Extract mels on a GPU using CUDA')
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return parser
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class FilenamedLoader(TextMelLoader):
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def __init__(self, filenames, *args, **kwargs):
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super(FilenamedLoader, self).__init__(*args, **kwargs)
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self.filenames = filenames
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def __getitem__(self, index):
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mel_text = super(FilenamedLoader, self).__getitem__(index)
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return mel_text + (self.filenames[index],)
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def maybe_pad(vec, l):
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assert np.abs(vec.shape[0] - l) <= 3
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vec = vec[:l]
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if vec.shape[0] < l:
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vec = np.pad(vec, pad_width=(0, l - vec.shape[0]))
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return vec
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def dur_chunk_sizes(n, ary):
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"""Split a single duration into almost-equally-sized chunks
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Examples:
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dur_chunk(3, 2) --> [2, 1]
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dur_chunk(3, 3) --> [1, 1, 1]
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dur_chunk(5, 3) --> [2, 2, 1]
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"""
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ret = np.ones((ary,), dtype=np.int32) * (n // ary)
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ret[:n % ary] = n // ary + 1
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assert ret.sum() == n
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return ret
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def calculate_pitch(wav, durs):
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mel_len = durs.sum()
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durs_cum = np.cumsum(np.pad(durs, (1, 0)))
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snd = parselmouth.Sound(wav)
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pitch = snd.to_pitch(time_step=snd.duration / (mel_len + 3)
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).selected_array['frequency']
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assert np.abs(mel_len - pitch.shape[0]) <= 1.0
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# Average pitch over characters
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pitch_char = np.zeros((durs.shape[0],), dtype=np.float)
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for idx, a, b in zip(range(mel_len), durs_cum[:-1], durs_cum[1:]):
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values = pitch[a:b][np.where(pitch[a:b] != 0.0)[0]]
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pitch_char[idx] = np.mean(values) if len(values) > 0 else 0.0
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# Average to three values per character
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pitch_trichar = np.zeros((3 * durs.shape[0],), dtype=np.float)
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durs_tri = np.concatenate([dur_chunk_sizes(d, 3) for d in durs])
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durs_tri_cum = np.cumsum(np.pad(durs_tri, (1, 0)))
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for idx, a, b in zip(range(3 * mel_len), durs_tri_cum[:-1], durs_tri_cum[1:]):
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values = pitch[a:b][np.where(pitch[a:b] != 0.0)[0]]
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pitch_trichar[idx] = np.mean(values) if len(values) > 0 else 0.0
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pitch_mel = maybe_pad(pitch, mel_len)
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pitch_char = maybe_pad(pitch_char, len(durs))
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pitch_trichar = maybe_pad(pitch_trichar, len(durs_tri))
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return pitch_mel, pitch_char, pitch_trichar
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def normalize_pitch_vectors(pitch_vecs):
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nonzeros = np.concatenate([v[np.where(v != 0.0)[0]]
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for v in pitch_vecs.values()])
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mean, std = np.mean(nonzeros), np.std(nonzeros)
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for v in pitch_vecs.values():
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zero_idxs = np.where(v == 0.0)[0]
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v -= mean
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v /= std
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v[zero_idxs] = 0.0
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return mean, std
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def save_stats(dataset_path, wav_text_filelist, feature_name, mean, std):
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fpath = utils.stats_filename(dataset_path, wav_text_filelist, feature_name)
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with open(fpath, 'w') as f:
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json.dump({'mean': mean, 'std': std}, f, indent=4)
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def main():
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parser = argparse.ArgumentParser(description='PyTorch TTS Data Pre-processing')
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parser = parse_args(parser)
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args, unk_args = parser.parse_known_args()
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if len(unk_args) > 0:
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raise ValueError(f'Invalid options {unk_args}')
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if args.extract_pitch_char:
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assert args.extract_durations, "Durations required for pitch extraction"
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DLLogger.init(backends=[JSONStreamBackend(Verbosity.DEFAULT, args.log_file),
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StdOutBackend(Verbosity.VERBOSE)])
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for k,v in vars(args).items():
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DLLogger.log(step="PARAMETER", data={k:v})
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model = load_and_setup_model(
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'Tacotron2', parser, args.tacotron2_checkpoint, amp=False,
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device=torch.device('cuda' if args.cuda else 'cpu'),
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forward_is_infer=False, ema=False)
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if args.train_mode:
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model.train()
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# n_mel_channels arg has been consumed by model's arg parser
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args.n_mel_channels = model.n_mel_channels
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for datum in ('mels', 'mels_teacher', 'attentions', 'durations',
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'pitch_mel', 'pitch_char', 'pitch_trichar'):
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if getattr(args, f'extract_{datum}'):
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Path(args.dataset_path, datum).mkdir(parents=False, exist_ok=True)
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filenames = [Path(l.split('|')[0]).stem
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for l in open(args.wav_text_filelist, 'r')]
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dataset = FilenamedLoader(filenames, args.dataset_path, args.wav_text_filelist,
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args, load_mel_from_disk=False)
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# TextMelCollate supports only n_frames_per_step=1
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data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False,
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sampler=None, num_workers=0,
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collate_fn=TextMelCollate(1),
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pin_memory=False, drop_last=False)
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pitch_vecs = {'mel': {}, 'char': {}, 'trichar': {}}
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for i, batch in enumerate(data_loader):
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tik = time.time()
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fnames = batch[-1]
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x, _, _ = batch_to_gpu(batch[:-1])
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_, text_lens, mels_padded, _, mel_lens = x
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for j, mel in enumerate(mels_padded):
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fpath = Path(args.dataset_path, 'mels', fnames[j] + '.pt')
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torch.save(mel[:, :mel_lens[j]].cpu(), fpath)
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with torch.no_grad():
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out_mels, out_mels_postnet, _, alignments = model.forward(x)
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if args.extract_mels_teacher:
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for j, mel in enumerate(out_mels_postnet):
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fpath = Path(args.dataset_path, 'mels_teacher', fnames[j] + '.pt')
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torch.save(mel[:, :mel_lens[j]].cpu(), fpath)
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if args.extract_attentions:
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for j, ali in enumerate(alignments):
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ali = ali[:mel_lens[j],:text_lens[j]]
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fpath = Path(args.dataset_path, 'attentions', fnames[j] + '.pt')
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torch.save(ali.cpu(), fpath)
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durations = []
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if args.extract_durations:
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for j, ali in enumerate(alignments):
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text_len = text_lens[j]
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ali = ali[:mel_lens[j],:text_len]
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dur = torch.histc(torch.argmax(ali, dim=1), min=0,
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max=text_len-1, bins=text_len)
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durations.append(dur)
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fpath = Path(args.dataset_path, 'durations', fnames[j] + '.pt')
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torch.save(dur.cpu().int(), fpath)
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if args.extract_pitch_mel or args.extract_pitch_char or args.extract_pitch_trichar:
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for j, dur in enumerate(durations):
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fpath = Path(args.dataset_path, 'pitch_char', fnames[j] + '.pt')
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wav = Path(args.dataset_path, 'wavs', fnames[j] + '.wav')
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p_mel, p_char, p_trichar = calculate_pitch(str(wav), dur.cpu().numpy())
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pitch_vecs['mel'][fnames[j]] = p_mel
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pitch_vecs['char'][fnames[j]] = p_char
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pitch_vecs['trichar'][fnames[j]] = p_trichar
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nseconds = time.time() - tik
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DLLogger.log(step=f'{i+1}/{len(data_loader)} ({nseconds:.2f}s)', data={})
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if args.extract_pitch_mel:
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normalize_pitch_vectors(pitch_vecs['mel'])
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for fname, pitch in pitch_vecs['mel'].items():
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fpath = Path(args.dataset_path, 'pitch_mel', fname + '.pt')
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torch.save(torch.from_numpy(pitch), fpath)
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if args.extract_pitch_char:
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mean, std = normalize_pitch_vectors(pitch_vecs['char'])
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for fname, pitch in pitch_vecs['char'].items():
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fpath = Path(args.dataset_path, 'pitch_char', fname + '.pt')
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torch.save(torch.from_numpy(pitch), fpath)
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save_stats(args.dataset_path, args.wav_text_filelist, 'pitch_char',
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mean, std)
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if args.extract_pitch_trichar:
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normalize_pitch_vectors(pitch_vecs['trichar'])
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for fname, pitch in pitch_vecs['trichar'].items():
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fpath = Path(args.dataset_path, 'pitch_trichar', fname + '.pt')
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torch.save(torch.from_numpy(pitch), fpath)
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DLLogger.flush()
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if __name__ == '__main__':
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main()
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