text: # -*- 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 g2p_en import G2p
  
  valid_symbols = [
      "AA",
      "AA0",
      "AA1",
      "AA2",
      "AE",
      "AE0",
      "AE1",
      "AE2",
      "AH",
      "AH0",
      "AH1",
      "AH2",
      "AO",
      "AO0",
      "AO1",
      "AO2",
      "AW",
      "AW0",
      "AW1",
      "AW2",
      "AY",
      "AY0",
      "AY1",
      "AY2",
      "B",
      "CH",
      "D",
      "DH",
      "EH",
      "EH0",
      "EH1",
      "EH2",
      "ER",
      "ER0",
      "ER1",
      "ER2",
      "EY",
      "EY0",
      "EY1",
      "EY2",
      "F",
      "G",
      "HH",
      "IH",
      "IH0",
      "IH1",
      "IH2",
      "IY",
      "IY0",
      "IY1",
      "IY2",
      "JH",
      "K",
      "L",
      "M",
      "N",
      "NG",
      "OW",
      "OW0",
      "OW1",
      "OW2",
      "OY",
      "OY0",
      "OY1",
      "OY2",
      "P",
      "R",
      "S",
      "SH",
      "T",
      "TH",
      "UH",
      "UH0",
      "UH1",
      "UH2",
      "UW",
      "UW0",
      "UW1",
      "UW2",
      "V",
      "W",
      "Y",
      "Z",
      "ZH",
      "SIL",#Silence
      "END", #padding token
  ]
  
  _pad = "pad"
  _eos = "eos"
  _punctuation = "!'(),.:;?"
  _special = "-"
  _letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
  _singlesil = [",",";","?",".","..."]
  
  # Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
  _arpabet = ["@" + s for s in valid_symbols]
  
  # Export all symbols:
  LJSPEECH_SYMBOLS = (
      [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet + [_eos]
  )
  
  # Regular expression matching text enclosed in curly braces:
  _curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
  
  _symbol_to_id = {s: i for i, s in enumerate(LJSPEECH_SYMBOLS)}
  _id_to_symbol = {i: s for i, s in enumerate(LJSPEECH_SYMBOLS)}
  
  
  @dataclass
  class LJSpeechProcessor(BaseProcessor):
      """LJSpeech processor."""
  
      cleaner_names: str = "english_cleaners"
      positions = {
          "wave_file": 0,
          "text": 1,
          "text_norm": 2,
      }
      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 = "ljspeech"
          return text_norm, wav_path, speaker_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
          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 processtxtph(self,intxt):
        g2p = G2p()
        ptext =  self._clean_text(intxt,[self.cleaner_names])
        phs = _g2p2synth(g2p(ptext))
  
        arpatxt = " ".join(phs)
        ids = self._arpabet_to_sequence(arpatxt)
        
        return ids, arpatxt
  
      def setup_eos_token(self):
          return None # because we don't use this 
  
      def _symbols_to_sequence(self, symbols):
          return [_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 _symbol_to_id and s != "_" and s != "~"
  
  def _g2p2synth(inseq):
    phseq = list()
    for idx, itm in enumerate(inseq):
      if itm == ' ':
        continue
      
      if idx < len(inseq) - 1: #Prevent it from appending SILs due to end periods
        if itm in _singlesil:
         phseq.append("SIL")
         continue
      else:
        if itm in _singlesil:
          continue # Skip ending dots
  
      phseq.append(itm)
      
    phseq.append("SIL")
    return phseq