diff --git a/egs/ljspeech/TTS/matcha/text/__init__.py b/egs/ljspeech/TTS/matcha/text/__init__.py
deleted file mode 100644
index 78c8b1f18..000000000
--- a/egs/ljspeech/TTS/matcha/text/__init__.py
+++ /dev/null
@@ -1,64 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from matcha.text import cleaners
-from matcha.text.symbols import symbols
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {
-    i: s for i, s in enumerate(symbols)
-}  # pylint: disable=unnecessary-comprehension
-
-
-def text_to_sequence(text, cleaner_names):
-    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-    """
-    sequence = []
-
-    clean_text = _clean_text(text, cleaner_names)
-    for symbol in clean_text:
-        try:
-            if symbol in "_()[]# ̃":
-                continue
-            symbol_id = _symbol_to_id[symbol]
-        except Exception as ex:
-            print(text)
-            print(clean_text)
-            raise RuntimeError(
-                f"text: {text}, clean_text: {clean_text}, ex: {ex}, symbol: {symbol}"
-            )
-        sequence += [symbol_id]
-    return sequence, clean_text
-
-
-def cleaned_text_to_sequence(cleaned_text):
-    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-    """
-    sequence = [_symbol_to_id[symbol] for symbol in cleaned_text]
-    return sequence
-
-
-def sequence_to_text(sequence):
-    """Converts a sequence of IDs back to a string"""
-    result = ""
-    for symbol_id in sequence:
-        s = _id_to_symbol[symbol_id]
-        result += s
-    return result
-
-
-def _clean_text(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
diff --git a/egs/ljspeech/TTS/matcha/text/cleaners.py b/egs/ljspeech/TTS/matcha/text/cleaners.py
deleted file mode 100644
index 0a1979afe..000000000
--- a/egs/ljspeech/TTS/matcha/text/cleaners.py
+++ /dev/null
@@ -1,130 +0,0 @@
-""" from https://github.com/keithito/tacotron
-
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-"""
-
-import logging
-import re
-
-import phonemizer
-from unidecode import unidecode
-
-# To avoid excessive logging we set the log level of the phonemizer package to Critical
-critical_logger = logging.getLogger("phonemizer")
-critical_logger.setLevel(logging.CRITICAL)
-
-# Intializing the phonemizer globally significantly reduces the speed
-# now the phonemizer is not initialising at every call
-# Might be less flexible, but it is much-much faster
-global_phonemizer = phonemizer.backend.EspeakBackend(
-    language="en-us",
-    preserve_punctuation=True,
-    with_stress=True,
-    language_switch="remove-flags",
-    logger=critical_logger,
-)
-
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r"\s+")
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [
-    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
-    for x in [
-        ("mrs", "misess"),
-        ("mr", "mister"),
-        ("dr", "doctor"),
-        ("st", "saint"),
-        ("co", "company"),
-        ("jr", "junior"),
-        ("maj", "major"),
-        ("gen", "general"),
-        ("drs", "doctors"),
-        ("rev", "reverend"),
-        ("lt", "lieutenant"),
-        ("hon", "honorable"),
-        ("sgt", "sergeant"),
-        ("capt", "captain"),
-        ("esq", "esquire"),
-        ("ltd", "limited"),
-        ("col", "colonel"),
-        ("ft", "fort"),
-    ]
-]
-
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def lowercase(text):
-    return text.lower()
-
-
-def collapse_whitespace(text):
-    return re.sub(_whitespace_re, " ", text)
-
-
-def remove_parentheses(text):
-    text = text.replace("(", "")
-    text = text.replace(")", "")
-    text = text.replace("[", "")
-    text = text.replace("]", "")
-    return text
-
-
-def convert_to_ascii(text):
-    return unidecode(text)
-
-
-def basic_cleaners(text):
-    """Basic pipeline that lowercases and collapses whitespace without transliteration."""
-    text = lowercase(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def transliteration_cleaners(text):
-    """Pipeline for non-English text that transliterates to ASCII."""
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def english_cleaners2(text):
-    """Pipeline for English text, including abbreviation expansion. + punctuation + stress"""
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = expand_abbreviations(text)
-    text = remove_parentheses(text)
-    phonemes = global_phonemizer.phonemize([text], strip=True, njobs=1)[0]
-    phonemes = collapse_whitespace(phonemes)
-    return phonemes
-
-
-# I am removing this due to incompatibility with several version of python
-# However, if you want to use it, you can uncomment it
-# and install piper-phonemize with the following command:
-# pip install piper-phonemize
-
-# import piper_phonemize
-# def english_cleaners_piper(text):
-#     """Pipeline for English text, including abbreviation expansion. + punctuation + stress"""
-#     text = convert_to_ascii(text)
-#     text = lowercase(text)
-#     text = expand_abbreviations(text)
-#     phonemes = "".join(piper_phonemize.phonemize_espeak(text=text, voice="en-US")[0])
-#     phonemes = collapse_whitespace(phonemes)
-#     return phonemes
diff --git a/egs/ljspeech/TTS/matcha/text/numbers.py b/egs/ljspeech/TTS/matcha/text/numbers.py
deleted file mode 100644
index 49c21d4e9..000000000
--- a/egs/ljspeech/TTS/matcha/text/numbers.py
+++ /dev/null
@@ -1,73 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-import re
-
-import inflect
-
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
-_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
-_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
-_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
-_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
-_number_re = re.compile(r"[0-9]+")
-
-
-def _remove_commas(m):
-    return m.group(1).replace(",", "")
-
-
-def _expand_decimal_point(m):
-    return m.group(1).replace(".", " point ")
-
-
-def _expand_dollars(m):
-    match = m.group(1)
-    parts = match.split(".")
-    if len(parts) > 2:
-        return match + " dollars"
-    dollars = int(parts[0]) if parts[0] else 0
-    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-    if dollars and cents:
-        dollar_unit = "dollar" if dollars == 1 else "dollars"
-        cent_unit = "cent" if cents == 1 else "cents"
-        return f"{dollars} {dollar_unit}, {cents} {cent_unit}"
-    elif dollars:
-        dollar_unit = "dollar" if dollars == 1 else "dollars"
-        return f"{dollars} {dollar_unit}"
-    elif cents:
-        cent_unit = "cent" if cents == 1 else "cents"
-        return f"{cents} {cent_unit}"
-    else:
-        return "zero dollars"
-
-
-def _expand_ordinal(m):
-    return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
-    num = int(m.group(0))
-    if num > 1000 and num < 3000:
-        if num == 2000:
-            return "two thousand"
-        elif num > 2000 and num < 2010:
-            return "two thousand " + _inflect.number_to_words(num % 100)
-        elif num % 100 == 0:
-            return _inflect.number_to_words(num // 100) + " hundred"
-        else:
-            return _inflect.number_to_words(
-                num, andword="", zero="oh", group=2
-            ).replace(", ", " ")
-    else:
-        return _inflect.number_to_words(num, andword="")
-
-
-def normalize_numbers(text):
-    text = re.sub(_comma_number_re, _remove_commas, text)
-    text = re.sub(_pounds_re, r"\1 pounds", text)
-    text = re.sub(_dollars_re, _expand_dollars, text)
-    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-    text = re.sub(_ordinal_re, _expand_ordinal, text)
-    text = re.sub(_number_re, _expand_number, text)
-    return text
diff --git a/egs/ljspeech/TTS/matcha/text/symbols.py b/egs/ljspeech/TTS/matcha/text/symbols.py
deleted file mode 100644
index b32c12430..000000000
--- a/egs/ljspeech/TTS/matcha/text/symbols.py
+++ /dev/null
@@ -1,15 +0,0 @@
-""" from https://github.com/keithito/tacotron
-
-Defines the set of symbols used in text input to the model.
-"""
-_pad = "_"
-_punctuation = ';:,.!?¡¿—…"«»“” '
-_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
-_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")