diff --git a/tts/text/pipeline/legacy_fakeyou_pipeline.py b/tts/text/pipeline/legacy_fakeyou_pipeline.py
index 4903559..c6756ad 100644
--- a/tts/text/pipeline/legacy_fakeyou_pipeline.py
+++ b/tts/text/pipeline/legacy_fakeyou_pipeline.py
@@ -1,18 +1,26 @@
+import re
 from typing import List
 from tts.text.pipeline.base_pipeline import BasePipeline
+from tts.text.pipeline_components.normalization.common_cleaners import collapse_whitespace, convert_to_ascii, lowercase
+from tts.text.pipeline_components.normalization.english_cleaners import expand_abbreviations, expand_numbers
+from tts.text.pipeline_components.sequencing.english_symbols_v1 import arpabet_to_sequence, symbols_to_sequence
+
+# Regular expression matching text enclosed in curly braces:
+_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
 
 class LegacyFakeYouPipeline(BasePipeline):
     """
     This pipeline was in use from June 2021 to July 2022.
     In contrast to legacy "vo.codes" (2020 - 2021), it does not 
-    leverage Arpabet even when models were trained with Arpabet support.
+    leverage CMUDict to look up arpabet even when models were known to
+    be trained with arpabet support. (It did support curly braces for 
+    manual arpabet inclusion.)
     """
 
-
     def user_input_to_sequence(self, input_text: str) -> List[int]:
         """Convert raw user input into a numerical sequence for ML inference."""
         pre_sequence = self._preprocess_text(input_text)
-        return text_to_sequence(pre_sequence, ['english_cleaners'])
+        return self._text_to_sequence(pre_sequence, ['english_cleaners'])
 
     def _preprocess_text(self, input_text: str, is_raw_input: bool = True) -> str:
         pre_sequence = ""
@@ -25,4 +33,42 @@ class LegacyFakeYouPipeline(BasePipeline):
             else:
                 pass
             pre_sequence += line
-        return pre_sequence
\ No newline at end of file
+        return pre_sequence
+
+
+    def _text_to_sequence(self, text, cleaner_names):
+        '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+
+            The text can optionally have ARPAbet sequences enclosed in curly braces embedded
+            in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
+
+            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 = []
+
+        # Check for curly braces and treat their contents as ARPAbet:
+        while len(text):
+            m = _curly_re.match(text)
+            if not m:
+                sequence += symbols_to_sequence(english_cleaners(text))
+                break
+            sequence += symbols_to_sequence(english_cleaners(m.group(1)))
+            sequence += arpabet_to_sequence(m.group(2))
+            text = m.group(3)
+
+        return sequence
+
+
+def english_cleaners(text):
+  '''Pipeline for English text, including number and abbreviation expansion.'''
+  text = convert_to_ascii(text)
+  text = lowercase(text)
+  text = expand_numbers(text)
+  text = expand_abbreviations(text)
+  text = collapse_whitespace(text)
+  return text