diff --git a/TTS/tts/utils/text/characters.py b/TTS/tts/utils/text/characters.py
index aae6844f..f6c04370 100644
--- a/TTS/tts/utils/text/characters.py
+++ b/TTS/tts/utils/text/characters.py
@@ -1,4 +1,6 @@
+from abc import ABC
 from dataclasses import replace
+from typing import Dict
 
 from TTS.tts.configs.shared_configs import CharactersConfig
 
@@ -79,6 +81,71 @@ _phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprase
 # DEF_PHONEMES = create_phonemes(_phonemes, _punctuations, _pad, _eos, _bos, _blank)
 
 
+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 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 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
+        }
+
+    @staticmethod
+    def init_from_config(config, **kwargs):
+        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
+
+    @property
+    def num_chars(self):
+        return max(self._vocab.values()) + 1
+
+    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]
+
+
 class BaseCharacters:
     """🐸BaseCharacters class
 
@@ -116,12 +183,12 @@ class BaseCharacters:
 
     def __init__(
         self,
-        characters: str,
-        punctuations: str,
-        pad: str,
-        eos: str,
-        bos: str,
-        blank: str,
+        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:
@@ -135,6 +202,14 @@ class BaseCharacters:
         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 characters(self):
         return self._characters
@@ -193,6 +268,14 @@ class BaseCharacters:
     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)
@@ -208,11 +291,7 @@ class BaseCharacters:
         _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)
-        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
-        }
+        self.vocab = _vocab + list(self._punctuations)
         if self.is_unique:
             duplicates = {x for x in self.vocab if self.vocab.count(x) > 1}
             assert (
@@ -248,7 +327,13 @@ class BaseCharacters:
 
         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(
diff --git a/TTS/utils/training.py b/TTS/utils/training.py
index 9f01b310..e69fb2b4 100644
--- a/TTS/utils/training.py
+++ b/TTS/utils/training.py
@@ -30,20 +30,6 @@ def check_update(model, grad_clip, ignore_stopnet=False, amp_opt_params=None):
     return grad_norm, skip_flag
 
 
-# pylint: disable=protected-access
-class NoamLR(torch.optim.lr_scheduler._LRScheduler):
-    def __init__(self, optimizer, warmup_steps=0.1, last_epoch=-1):
-        self.warmup_steps = float(warmup_steps)
-        super().__init__(optimizer, last_epoch)
-
-    def get_lr(self):
-        step = max(self.last_epoch, 1)
-        return [
-            base_lr * self.warmup_steps**0.5 * min(step * self.warmup_steps**-1.5, step**-0.5)
-            for base_lr in self.base_lrs
-        ]
-
-
 def gradual_training_scheduler(global_step, config):
     """Setup the gradual training schedule wrt number
     of active GPUs"""