SYSPIN
/

tts_vits_coquiai_MagahiMale

Model card Files Files and versions Community

tts_vits_coquiai_MagahiMale / extra.py

viks66

add codes

221d863 4 months ago

raw history blame

No virus

26.4 kB

	from typing import Callable, Dict, List, Union
	from dataclasses import asdict, dataclass, field


	import re
	from dataclasses import replace
	from typing import Dict
	_whitespace_re = re.compile(r"\s+")

	from dataclasses import dataclass, field
	from typing import List

	# from TTS.tts.configs.shared_configs import BaseTTSConfig
	# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig

	@dataclass
	class CharactersConfig():

	characters_class: str = None

	# using BaseVocabulary
	vocab_dict: Dict = None

	# using on BaseCharacters
	pad: str = None
	eos: str = None
	bos: str = None
	blank: str = None
	characters: str = None
	punctuations: str = None
	phonemes: str = None
	is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates
	is_sorted: bool = True


	@dataclass
	class BaseTTSConfig():

	# audio: BaseAudioConfig = field(default_factory=BaseAudioConfig)
	# phoneme settings
	use_phonemes: bool = False
	phonemizer: str = None
	phoneme_language: str = None
	compute_input_seq_cache: bool = False
	text_cleaner: str = None
	enable_eos_bos_chars: bool = False
	test_sentences_file: str = ""
	phoneme_cache_path: str = None
	# vocabulary parameters
	characters: CharactersConfig = None
	add_blank: bool = False
	# training params
	batch_group_size: int = 0
	loss_masking: bool = None
	# dataloading
	min_audio_len: int = 1
	max_audio_len: int = float("inf")
	min_text_len: int = 1
	max_text_len: int = float("inf")
	compute_f0: bool = False
	compute_energy: bool = False
	compute_linear_spec: bool = False
	precompute_num_workers: int = 0
	use_noise_augment: bool = False
	start_by_longest: bool = False
	shuffle: bool = False
	drop_last: bool = False
	# dataset
	datasets: str = None
	# optimizer
	optimizer: str = "radam"
	optimizer_params: dict = None
	# scheduler
	lr_scheduler: str = None
	lr_scheduler_params: dict = field(default_factory=lambda: {})
	# testing
	test_sentences: List[str] = field(default_factory=lambda: [])
	# evaluation
	eval_split_max_size: int = None
	eval_split_size: float = 0.01
	# weighted samplers
	use_speaker_weighted_sampler: bool = False
	speaker_weighted_sampler_alpha: float = 1.0
	use_language_weighted_sampler: bool = False
	language_weighted_sampler_alpha: float = 1.0
	use_length_weighted_sampler: bool = False
	length_weighted_sampler_alpha: float = 1.0


	@dataclass
	class VitsAudioConfig():
	fft_size: int = 1024
	sample_rate: int = 22050
	win_length: int = 1024
	hop_length: int = 256
	num_mels: int = 80
	mel_fmin: int = 0
	mel_fmax: int = None

	@dataclass
	class VitsArgs():
	num_chars: int = 100
	out_channels: int = 513
	spec_segment_size: int = 32
	hidden_channels: int = 192
	hidden_channels_ffn_text_encoder: int = 768
	num_heads_text_encoder: int = 2
	num_layers_text_encoder: int = 6
	kernel_size_text_encoder: int = 3
	dropout_p_text_encoder: float = 0.1
	dropout_p_duration_predictor: float = 0.5
	kernel_size_posterior_encoder: int = 5
	dilation_rate_posterior_encoder: int = 1
	num_layers_posterior_encoder: int = 16
	kernel_size_flow: int = 5
	dilation_rate_flow: int = 1
	num_layers_flow: int = 4
	resblock_type_decoder: str = "1"
	resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11])
	resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]])
	upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2])
	upsample_initial_channel_decoder: int = 512
	upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4])
	periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11])
	use_sdp: bool = True
	noise_scale: float = 1.0
	inference_noise_scale: float = 0.667
	length_scale: float = 1
	noise_scale_dp: float = 1.0
	inference_noise_scale_dp: float = 1.0
	max_inference_len: int = None
	init_discriminator: bool = True
	use_spectral_norm_disriminator: bool = False
	use_speaker_embedding: bool = False
	num_speakers: int = 0
	speakers_file: str = None
	d_vector_file: List[str] = None
	speaker_embedding_channels: int = 256
	use_d_vector_file: bool = False
	d_vector_dim: int = 0
	detach_dp_input: bool = True
	use_language_embedding: bool = False
	embedded_language_dim: int = 4
	num_languages: int = 0
	language_ids_file: str = None
	use_speaker_encoder_as_loss: bool = False
	speaker_encoder_config_path: str = ""
	speaker_encoder_model_path: str = ""
	condition_dp_on_speaker: bool = True
	freeze_encoder: bool = False
	freeze_DP: bool = False
	freeze_PE: bool = False
	freeze_flow_decoder: bool = False
	freeze_waveform_decoder: bool = False
	encoder_sample_rate: int = None
	interpolate_z: bool = True
	reinit_DP: bool = False
	reinit_text_encoder: bool = False
	@dataclass
	class VitsConfig(BaseTTSConfig):

	model: str = "vits"
	# model specific params
	model_args: VitsArgs = field(default_factory=VitsArgs)
	audio: VitsAudioConfig = field(default_factory=VitsAudioConfig)

	# optimizer
	grad_clip: List[float] = field(default_factory=lambda: [1000, 1000])
	lr_gen: float = 0.0002
	lr_disc: float = 0.0002
	lr_scheduler_gen: str = "ExponentialLR"
	lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
	lr_scheduler_disc: str = "ExponentialLR"
	lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
	scheduler_after_epoch: bool = True
	optimizer: str = "AdamW"
	optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01})

	# loss params
	kl_loss_alpha: float = 1.0
	disc_loss_alpha: float = 1.0
	gen_loss_alpha: float = 1.0
	feat_loss_alpha: float = 1.0
	mel_loss_alpha: float = 45.0
	dur_loss_alpha: float = 1.0
	speaker_encoder_loss_alpha: float = 1.0

	# data loader params
	return_wav: bool = True
	compute_linear_spec: bool = True

	# sampler params
	use_weighted_sampler: bool = False # TODO: move it to the base config
	weighted_sampler_attrs: dict = field(default_factory=lambda: {})
	weighted_sampler_multipliers: dict = field(default_factory=lambda: {})

	# overrides
	r: int = 1 # DO NOT CHANGE
	add_blank: bool = True

	# testing
	test_sentences: List[List] = field(
	default_factory=lambda: [
	["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."],
	["Be a voice, not an echo."],
	["I'm sorry Dave. I'm afraid I can't do that."],
	["This cake is great. It's so delicious and moist."],
	["Prior to November 22, 1963."],
	]
	)

	# multi-speaker settings
	# use speaker embedding layer
	num_speakers: int = 0
	use_speaker_embedding: bool = False
	speakers_file: str = None
	speaker_embedding_channels: int = 256
	language_ids_file: str = None
	use_language_embedding: bool = False

	# use d-vectors
	use_d_vector_file: bool = False
	d_vector_file: List[str] = None
	d_vector_dim: int = None

	def __post_init__(self):
	pass
	# for key, val in self.model_args.items():
	# if hasattr(self, key):
	# self[key] = val





	def parse_symbols():
	return {
	"pad": _pad,
	"eos": _eos,
	"bos": _bos,
	"characters": _characters,
	"punctuations": _punctuations,
	"phonemes": _phonemes,
	}


	# DEFAULT SET OF GRAPHEMES
	_pad = "<PAD>"
	_eos = "<EOS>"
	_bos = "<BOS>"
	_blank = "<BLNK>" # TODO: check if we need this alongside with PAD
	_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
	_punctuations = "!'(),-.:;? "


	# DEFAULT SET OF IPA PHONEMES
	# Phonemes definition (All IPA characters)
	_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ"
	_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ"
	_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ"
	_suprasegmentals = "ˈˌːˑ"
	_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ"
	_diacrilics = "ɚ˞ɫ"
	_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics


	class BaseVocabulary:
	"""Base Vocabulary class.

	This class only needs a vocabulary dictionary without specifying the characters.

	Args:
	vocab (Dict): A dictionary of characters and their corresponding indices.
	"""

	def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None):
	self.vocab = vocab
	self.pad = pad
	self.blank = blank
	self.bos = bos
	self.eos = eos

	@property
	def pad_id(self) -> int:
	"""Return the index of the padding character. If the padding character is not specified, return the length
	of the vocabulary."""
	return self.char_to_id(self.pad) if self.pad else len(self.vocab)

	@property
	def blank_id(self) -> int:
	"""Return the index of the blank character. If the blank character is not specified, return the length of
	the vocabulary."""
	return self.char_to_id(self.blank) if self.blank else len(self.vocab)

	@property
	def bos_id(self) -> int:
	"""Return the index of the bos character. If the bos character is not specified, return the length of the
	vocabulary."""
	return self.char_to_id(self.bos) if self.bos else len(self.vocab)

	@property
	def eos_id(self) -> int:
	"""Return the index of the eos character. If the eos character is not specified, return the length of the
	vocabulary."""
	return self.char_to_id(self.eos) if self.eos else len(self.vocab)

	@property
	def vocab(self):
	"""Return the vocabulary dictionary."""
	return self._vocab

	@vocab.setter
	def vocab(self, vocab):
	"""Set the vocabulary dictionary and character mapping dictionaries."""
	self._vocab, self._char_to_id, self._id_to_char = None, None, None
	if vocab is not None:
	self._vocab = vocab
	self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)}
	self._id_to_char = {
	idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension
	}

	@staticmethod
	def init_from_config(config, **kwargs):
	"""Initialize from the given config."""
	if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict:
	return (
	BaseVocabulary(
	config.characters.vocab_dict,
	config.characters.pad,
	config.characters.blank,
	config.characters.bos,
	config.characters.eos,
	),
	config,
	)
	return BaseVocabulary(**kwargs), config

	def to_config(self):
	return CharactersConfig(
	vocab_dict=self._vocab,
	pad=self.pad,
	eos=self.eos,
	bos=self.bos,
	blank=self.blank,
	is_unique=False,
	is_sorted=False,
	)

	@property
	def num_chars(self):
	"""Return number of tokens in the vocabulary."""
	return len(self._vocab)

	def char_to_id(self, char: str) -> int:
	"""Map a character to an token ID."""
	try:
	return self._char_to_id[char]
	except KeyError as e:
	raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e

	def id_to_char(self, idx: int) -> str:
	"""Map an token ID to a character."""
	return self._id_to_char[idx]


	class BaseCharacters:


	def __init__(
	self,
	characters: str = None,
	punctuations: str = None,
	pad: str = None,
	eos: str = None,
	bos: str = None,
	blank: str = None,
	is_unique: bool = False,
	is_sorted: bool = True,
	) -> None:
	self._characters = characters
	self._punctuations = punctuations
	self._pad = pad
	self._eos = eos
	self._bos = bos
	self._blank = blank
	self.is_unique = is_unique
	self.is_sorted = is_sorted
	self._create_vocab()

	@property
	def pad_id(self) -> int:
	return self.char_to_id(self.pad) if self.pad else len(self.vocab)

	@property
	def blank_id(self) -> int:
	return self.char_to_id(self.blank) if self.blank else len(self.vocab)

	@property
	def eos_id(self) -> int:
	return self.char_to_id(self.eos) if self.eos else len(self.vocab)

	@property
	def bos_id(self) -> int:
	return self.char_to_id(self.bos) if self.bos else len(self.vocab)

	@property
	def characters(self):
	return self._characters

	@characters.setter
	def characters(self, characters):
	self._characters = characters
	self._create_vocab()

	@property
	def punctuations(self):
	return self._punctuations

	@punctuations.setter
	def punctuations(self, punctuations):
	self._punctuations = punctuations
	self._create_vocab()

	@property
	def pad(self):
	return self._pad

	@pad.setter
	def pad(self, pad):
	self._pad = pad
	self._create_vocab()

	@property
	def eos(self):
	return self._eos

	@eos.setter
	def eos(self, eos):
	self._eos = eos
	self._create_vocab()

	@property
	def bos(self):
	return self._bos

	@bos.setter
	def bos(self, bos):
	self._bos = bos
	self._create_vocab()

	@property
	def blank(self):
	return self._blank

	@blank.setter
	def blank(self, blank):
	self._blank = blank
	self._create_vocab()

	@property
	def vocab(self):
	return self._vocab

	@vocab.setter
	def vocab(self, vocab):
	self._vocab = vocab
	self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)}
	self._id_to_char = {
	idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension
	}

	@property
	def num_chars(self):
	return len(self._vocab)

	def _create_vocab(self):
	_vocab = self._characters
	if self.is_unique:
	_vocab = list(set(_vocab))
	if self.is_sorted:
	_vocab = sorted(_vocab)
	_vocab = list(_vocab)
	_vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab
	_vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab
	_vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab
	_vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab
	self.vocab = _vocab + list(self._punctuations)
	if self.is_unique:
	duplicates = {x for x in self.vocab if self.vocab.count(x) > 1}
	assert (
	len(self.vocab) == len(self._char_to_id) == len(self._id_to_char)
	), f" [!] There are duplicate characters in the character set. {duplicates}"

	def char_to_id(self, char: str) -> int:
	try:
	return self._char_to_id[char]
	except KeyError as e:
	raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e

	def id_to_char(self, idx: int) -> str:
	return self._id_to_char[idx]

	def print_log(self, level: int = 0):
	"""
	Prints the vocabulary in a nice format.
	"""
	indent = "\t" * level
	print(f"{indent}\| > Characters: {self._characters}")
	print(f"{indent}\| > Punctuations: {self._punctuations}")
	print(f"{indent}\| > Pad: {self._pad}")
	print(f"{indent}\| > EOS: {self._eos}")
	print(f"{indent}\| > BOS: {self._bos}")
	print(f"{indent}\| > Blank: {self._blank}")
	print(f"{indent}\| > Vocab: {self.vocab}")
	print(f"{indent}\| > Num chars: {self.num_chars}")

	@staticmethod
	def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument
	"""Init your character class from a config.

	Implement this method for your subclass.
	"""
	# use character set from config
	if config.characters is not None:
	return BaseCharacters(**config.characters), config
	# return default character set
	characters = BaseCharacters()
	new_config = replace(config, characters=characters.to_config())
	return characters, new_config

	def to_config(self) -> "CharactersConfig":
	return CharactersConfig(
	characters=self._characters,
	punctuations=self._punctuations,
	pad=self._pad,
	eos=self._eos,
	bos=self._bos,
	blank=self._blank,
	is_unique=self.is_unique,
	is_sorted=self.is_sorted,
	)


	class IPAPhonemes(BaseCharacters):


	def __init__(
	self,
	characters: str = _phonemes,
	punctuations: str = _punctuations,
	pad: str = _pad,
	eos: str = _eos,
	bos: str = _bos,
	blank: str = _blank,
	is_unique: bool = False,
	is_sorted: bool = True,
	) -> None:
	super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted)

	@staticmethod
	def init_from_config(config: "Coqpit"):
	"""Init a IPAPhonemes object from a model config

	If characters are not defined in the config, it will be set to the default characters and the config
	will be updated.
	"""
	# band-aid for compatibility with old models
	if "characters" in config and config.characters is not None:
	if "phonemes" in config.characters and config.characters.phonemes is not None:
	config.characters["characters"] = config.characters["phonemes"]
	return (
	IPAPhonemes(
	characters=config.characters["characters"],
	punctuations=config.characters["punctuations"],
	pad=config.characters["pad"],
	eos=config.characters["eos"],
	bos=config.characters["bos"],
	blank=config.characters["blank"],
	is_unique=config.characters["is_unique"],
	is_sorted=config.characters["is_sorted"],
	),
	config,
	)
	# use character set from config
	if config.characters is not None:
	return IPAPhonemes(**config.characters), config
	# return default character set
	characters = IPAPhonemes()
	new_config = replace(config, characters=characters.to_config())
	return characters, new_config


	class Graphemes(BaseCharacters):


	def __init__(
	self,
	characters: str = _characters,
	punctuations: str = _punctuations,
	pad: str = _pad,
	eos: str = _eos,
	bos: str = _bos,
	blank: str = _blank,
	is_unique: bool = False,
	is_sorted: bool = True,
	) -> None:
	super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted)

	@staticmethod
	def init_from_config(config: "Coqpit"):
	"""Init a Graphemes object from a model config

	If characters are not defined in the config, it will be set to the default characters and the config
	will be updated.
	"""
	if config.characters is not None:
	# band-aid for compatibility with old models
	if "phonemes" in config.characters:
	return (
	Graphemes(
	characters=config.characters["characters"],
	punctuations=config.characters["punctuations"],
	pad=config.characters["pad"],
	eos=config.characters["eos"],
	bos=config.characters["bos"],
	blank=config.characters["blank"],
	is_unique=config.characters["is_unique"],
	is_sorted=config.characters["is_sorted"],
	),
	config,
	)
	return Graphemes(**config.characters), config
	characters = Graphemes()
	new_config = replace(config, characters=characters.to_config())
	return characters, new_config


	if __name__ == "__main__":
	gr = Graphemes()
	ph = IPAPhonemes()
	gr.print_log()
	ph.print_log()


	class VitsCharacters(BaseCharacters):
	"""Characters class for VITs model for compatibility with pre-trained models"""

	def __init__(
	self,
	graphemes: str = _characters,
	punctuations: str = _punctuations,
	pad: str = _pad,
	ipa_characters: str = _phonemes,
	) -> None:
	if ipa_characters is not None:
	graphemes += ipa_characters
	super().__init__(graphemes, punctuations, pad, None, None, "<BLNK>", is_unique=False, is_sorted=True)

	def _create_vocab(self):
	self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank]
	self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)}
	# pylint: disable=unnecessary-comprehension
	self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)}

	@staticmethod
	def init_from_config(config):
	_pad = config.characters.pad
	_punctuations = config.characters.punctuations
	_letters = config.characters.characters
	_letters_ipa = config.characters.phonemes
	return (
	VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad),
	config,
	)

	def to_config(self) -> "CharactersConfig":
	return CharactersConfig(
	characters=self._characters,
	punctuations=self._punctuations,
	pad=self._pad,
	eos=None,
	bos=None,
	blank=self._blank,
	is_unique=False,
	is_sorted=True,
	)

	class TTSTokenizer:
	def __init__(
	self,
	text_cleaner: Callable = None,
	characters: "BaseCharacters" = None,
	):
	self.text_cleaner = text_cleaner
	self.characters = characters
	self.not_found_characters = []

	@property
	def characters(self):
	return self._characters

	@characters.setter
	def characters(self, new_characters):
	self._characters = new_characters
	self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None
	self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None

	def encode(self, text: str) -> List[int]:
	"""Encodes a string of text as a sequence of IDs."""
	token_ids = []
	for char in text:
	try:
	idx = self.characters.char_to_id(char)
	token_ids.append(idx)
	except KeyError:
	# discard but store not found characters
	if char not in self.not_found_characters:
	self.not_found_characters.append(char)
	print(text)
	print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.")
	return token_ids

	def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument
	text = self.text_cleaner(text)
	text = self.encode(text)
	text = self.intersperse_blank_char(text, True)
	return text

	def pad_with_bos_eos(self, char_sequence: List[str]):
	"""Pads a sequence with the special BOS and EOS characters."""
	return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id]

	def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False):
	"""Intersperses the blank character between characters in a sequence.

	Use the ```blank``` character if defined else use the ```pad``` character.
	"""
	char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad
	result = [char_to_use] * (len(char_sequence) * 2 + 1)
	result[1::2] = char_sequence
	return result

	@staticmethod
	def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None):
	text_cleaner = multilingual_cleaners
	CharactersClass = VitsCharacters
	characters, new_config = CharactersClass.init_from_config(config)
	# new_config.characters.characters_class = get_import_path(characters)
	new_config.characters.characters_class = VitsCharacters
	return (
	TTSTokenizer(text_cleaner, characters),new_config)


	def multilingual_cleaners(text):
	"""Pipeline for multilingual text"""
	text = lowercase(text)
	text = replace_symbols(text, lang=None)
	text = remove_aux_symbols(text)
	text = collapse_whitespace(text)
	return text

	def lowercase(text):
	return text.lower()

	def collapse_whitespace(text):
	return re.sub(_whitespace_re, " ", text).strip()

	def replace_symbols(text, lang="en"):

	text = text.replace(";", ",")
	text = text.replace("-", " ") if lang != "ca" else text.replace("-", "")
	text = text.replace(":", ",")
	if lang == "en":
	text = text.replace("&", " and ")
	elif lang == "fr":
	text = text.replace("&", " et ")
	elif lang == "pt":
	text = text.replace("&", " e ")
	elif lang == "ca":
	text = text.replace("&", " i ")
	text = text.replace("'", "")
	return text

	def remove_aux_symbols(text):
	text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text)
	return text