File size: 4,298 Bytes
d5ee97c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
# -*- 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.
"""Perform preprocessing and raw feature extraction for LJSpeech dataset."""
import os
import re
import numpy as np
import soundfile as sf
from dataclasses import dataclass
from tensorflow_tts.processor import BaseProcessor
from tensorflow_tts.utils import cleaners
from tensorflow_tts.utils.utils import PROCESSOR_FILE_NAME
_pad = "pad"
_eos = "eos"
_punctuation = "!'(),.? "
_special = "-"
_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
# Export all symbols:
THORSTEN_SYMBOLS = (
[_pad] + list(_special) + list(_punctuation) + list(_letters) + [_eos]
)
# Regular expression matching text enclosed in curly braces:
_curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
@dataclass
class ThorstenProcessor(BaseProcessor):
"""Thorsten processor."""
cleaner_names: str = "german_cleaners"
positions = {
"wave_file": 0,
"text_norm": 1,
}
train_f_name: str = "metadata.csv"
def create_items(self):
if self.data_dir:
with open(
os.path.join(self.data_dir, self.train_f_name), encoding="utf-8"
) as f:
self.items = [self.split_line(self.data_dir, line, "|") for line in f]
def split_line(self, data_dir, line, split):
parts = line.strip().split(split)
wave_file = parts[self.positions["wave_file"]]
text_norm = parts[self.positions["text_norm"]]
wav_path = os.path.join(data_dir, "wavs", f"{wave_file}.wav")
speaker_name = "thorsten"
return text_norm, wav_path, speaker_name
def setup_eos_token(self):
return _eos
def save_pretrained(self, saved_path):
os.makedirs(saved_path, exist_ok=True)
self._save_mapper(os.path.join(saved_path, PROCESSOR_FILE_NAME), {})
def get_one_sample(self, item):
text, wav_path, speaker_name = item
# normalize audio signal to be [-1, 1], soundfile already norm.
audio, rate = sf.read(wav_path)
audio = audio.astype(np.float32)
# convert text to ids
text_ids = np.asarray(self.text_to_sequence(text), np.int32)
sample = {
"raw_text": text,
"text_ids": text_ids,
"audio": audio,
"utt_id": os.path.split(wav_path)[-1].split(".")[0],
"speaker_name": speaker_name,
"rate": rate,
}
return sample
def text_to_sequence(self, text):
sequence = []
# Check for curly braces and treat their contents as ARPAbet:
while len(text):
m = _curly_re.match(text)
if not m:
sequence += self._symbols_to_sequence(
self._clean_text(text, [self.cleaner_names])
)
break
sequence += self._symbols_to_sequence(
self._clean_text(m.group(1), [self.cleaner_names])
)
sequence += self._arpabet_to_sequence(m.group(2))
text = m.group(3)
# add eos tokens
sequence += [self.eos_id]
return sequence
def _clean_text(self, text, cleaner_names):
for name in cleaner_names:
cleaner = getattr(cleaners, name)
if not cleaner:
raise Exception("Unknown cleaner: %s" % name)
text = cleaner(text)
return text
def _symbols_to_sequence(self, symbols):
return [self.symbol_to_id[s] for s in symbols if self._should_keep_symbol(s)]
def _arpabet_to_sequence(self, text):
return self._symbols_to_sequence(["@" + s for s in text.split()])
def _should_keep_symbol(self, s):
return s in self.symbol_to_id and s != "_" and s != "~"
|