Comparative-Analysis-of-Speech-Synthesis-Models
/
TensorFlowTTS
/examples
/mfa_extraction
/txt_grid_parser.py
# -*- coding: utf-8 -*- | |
# Copyright 2020 TensorFlowTTS Team. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""Create training file and durations from textgrids.""" | |
import os | |
from dataclasses import dataclass | |
from pathlib import Path | |
import click | |
import numpy as np | |
import textgrid | |
import yaml | |
from tqdm import tqdm | |
import logging | |
import sys | |
logging.basicConfig( | |
level=logging.DEBUG, | |
stream=sys.stdout, | |
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", | |
) | |
class TxtGridParser: | |
sample_rate: int | |
multi_speaker: bool | |
txt_grid_path: str | |
hop_size: int | |
output_durations_path: str | |
dataset_path: str | |
training_file: str = "train.txt" | |
phones_mapper = {"sil": "SIL", "sp": "SIL", "spn": "SIL", "": "END"} | |
""" '' -> is last token in every cases i encounter so u can change it for END but there is a safety check | |
so it'll fail always when empty string isn't last char in ur dataset just chang it to silence then | |
""" | |
sil_phones = set(phones_mapper.keys()) | |
def parse(self): | |
speakers = ( | |
[ | |
i | |
for i in os.listdir(self.txt_grid_path) | |
if os.path.isdir(os.path.join(self.txt_grid_path, i)) | |
] | |
if self.multi_speaker | |
else [] | |
) | |
data = [] | |
if speakers: | |
for speaker in speakers: | |
file_list = os.listdir(os.path.join(self.txt_grid_path, speaker)) | |
self.parse_text_grid(file_list, data, speaker) | |
else: | |
file_list = os.listdir(self.txt_grid_path) | |
self.parse_text_grid(file_list, data, "") | |
with open(os.path.join(self.dataset_path, self.training_file), "w") as f: | |
f.writelines(data) | |
def parse_text_grid(self, file_list: list, data: list, speaker_name: str): | |
logging.info( | |
f"\n Parse: {len(file_list)} files, speaker name: {speaker_name} \n" | |
) | |
for f_name in tqdm(file_list): | |
text_grid = textgrid.TextGrid.fromFile( | |
os.path.join(self.txt_grid_path, speaker_name, f_name) | |
) | |
pha = text_grid[1] | |
durations = [] | |
phs = [] | |
for iterator, interval in enumerate(pha.intervals): | |
mark = interval.mark | |
if mark in self.sil_phones: | |
mark = self.phones_mapper[mark] | |
if mark == "END": | |
assert iterator == pha.intervals.__len__() - 1 | |
# check if empty ph is always last example in your dataset if not fix it | |
dur = interval.duration() * (self.sample_rate / self.hop_size) | |
durations.append(round(dur)) | |
phs.append(mark) | |
full_ph = " ".join(phs) | |
assert full_ph.split(" ").__len__() == durations.__len__() # safety check | |
base_name = f_name.split(".TextGrid")[0] | |
np.save( | |
os.path.join(self.output_durations_path, f"{base_name}-durations.npy"), | |
np.array(durations).astype(np.int32), | |
allow_pickle=False, | |
) | |
data.append(f"{speaker_name}/{base_name}|{full_ph}|{speaker_name}\n") | |
def main( | |
yaml_path: str, | |
dataset_path: str, | |
text_grid_path: str, | |
output_durations_path: str, | |
sample_rate: int, | |
multi_speakers: int, | |
train_file: str, | |
): | |
with open(yaml_path) as file: | |
attrs = yaml.load(file) | |
hop_size = attrs["hop_size"] | |
Path(output_durations_path).mkdir(parents=True, exist_ok=True) | |
txt_grid_parser = TxtGridParser( | |
sample_rate=sample_rate, | |
multi_speaker=bool(multi_speakers), | |
txt_grid_path=text_grid_path, | |
hop_size=hop_size, | |
output_durations_path=output_durations_path, | |
training_file=train_file, | |
dataset_path=dataset_path, | |
) | |
txt_grid_parser.parse() | |
if __name__ == "__main__": | |
main() | |