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dkounadis
/
artificial-styletts2

Text-to-Speech
English
audio
speech
styletts2
mimic3
speech-emotion-recognition
dkounadis
Model card Files Community
3
artificial-styletts2
File size: 4,244 Bytes 
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# -*- coding: utf-8 -*-
import numpy as np
import re
import codecs
# IPA Phonemizer: https://github.com/bootphon/phonemizer

_pad = "$"
_punctuation = ';:,.!?¡¿—…"«»“” '
_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"

# Export all symbols:
symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)

dicts = {}
for i in range(len((symbols))):
    dicts[symbols[i]] = i

class TextCleaner:
    def __init__(self, dummy=None):
        self.word_index_dictionary = dicts
        print(len(dicts))
    def __call__(self, text):
        indexes = []
        for char in text:
            try:
                indexes.append(self.word_index_dictionary[char])
            except KeyError:
                print(text)
        return indexes


# == Sentence Splitter

alphabets= "([A-Za-z])"
prefixes = "(Mr|St|Mrs|Ms|Dr)[.]"
suffixes = "(Inc|Ltd|Jr|Sr|Co)"
starters = "(Mr|Mrs|Ms|Dr|Prof|Capt|Cpt|Lt|He\s|She\s|It\s|They\s|Their\s|Our\s|We\s|But\s|However\s|That\s|This\s|Wherever)"
acronyms = "([A-Z][.][A-Z][.](?:[A-Z][.])?)"
websites = "[.](com|net|org|io|gov|edu|me)"
digits = "([0-9])"
multiple_dots = r'\.{2,}'



def split_into_sentences(text):
    """
    Split the text into sentences.

    If the text contains substrings "<prd>" or "<stop>", they would lead
    to incorrect splitting because they are used as markers for splitting.

    :param text: text to be split into sentences
    :type text: str

    :return: list of sentences
    :rtype: list[str]

    https://stackoverflow.com/questions/4576077/how-can-i-split-a-text-into-sentences
    """
    text = " " + text + "  "
    text = text.replace("\n"," ")
    text = re.sub(prefixes,"\\1<prd>",text)
    text = re.sub(websites,"<prd>\\1",text)
    text = re.sub(digits + "[.]" + digits,"\\1<prd>\\2",text)
    text = re.sub(multiple_dots, lambda match: "<prd>" * len(match.group(0)) + "<stop>", text)
    if "Ph.D" in text: text = text.replace("Ph.D.","Ph<prd>D<prd>")
    text = re.sub("\s" + alphabets + "[.] "," \\1<prd> ",text)
    text = re.sub(acronyms+" "+starters,"\\1<stop> \\2",text)
    text = re.sub(alphabets + "[.]" + alphabets + "[.]" + alphabets + "[.]","\\1<prd>\\2<prd>\\3<prd>",text)
    text = re.sub(alphabets + "[.]" + alphabets + "[.]","\\1<prd>\\2<prd>",text)
    text = re.sub(" "+suffixes+"[.] "+starters," \\1<stop> \\2",text)
    text = re.sub(" "+suffixes+"[.]"," \\1<prd>",text)
    text = re.sub(" " + alphabets + "[.]"," \\1<prd>",text)
    if "”" in text: text = text.replace(".”","”.")
    if "\"" in text: text = text.replace(".\"","\".")
    if "!" in text: text = text.replace("!\"","\"!")
    if "?" in text: text = text.replace("?\"","\"?")
    text = text.replace(".",".<stop>")
    text = text.replace("?","?<stop>")
    text = text.replace("!","!<stop>")
    text = text.replace("<prd>",".")
    sentences = text.split("<stop>")
    sentences = [s.strip() for s in sentences]
    if sentences and not sentences[-1]: sentences = sentences[:-1]
    return sentences

def store_ssml(text=None,
               voice=None):
    '''create ssml:
           text : list of sentences
           voice: https://github.com/MycroftAI/mimic3-voices
    '''
    print('\n___________________________\n', len(text), text[0], '\n___________________________________\n')
    _s = '<speak>'
    for short_text in text:

        rate = min(max(.87, len(short_text) / 76), 1.14) #1.44)  # 1.24 for bieber
        
        
        volume = int(74 * np.random.rand() + 24)
        # text = ('<speak>'
        _s += f'<prosody volume=\'{volume}\'>'   # THe other voice does not have volume
        _s += f'<prosody rate=\'{rate}\'>'
        _s += f'<voice name=\'{voice}\'>'
        _s += '<s>'
        _s += short_text
        _s += '</s>'
        _s += '</voice>'
        _s += '</prosody>'
        _s += '</prosody>'
    _s += '</speak>'
    print(len(text),'\n\n\n\n\n\n\n', _s)
    
    with codecs.open('_tmp_ssml.txt', 'w', "utf-8-sig") as f:
        f.write(_s)