More text cleaning stuff

kendex/leviticus.py

@@ -1,72 +1,69 @@
-"""
-text normalization functions
-"""
-
 from unidecode import unidecode
 import inflect
 import re
 
 _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]+')
-
-# 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'),
-]]
+_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]+")
+
+_whitespace_re = re.compile(r"\s+")
+_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 _remove_commas(m):
-    return m.group(1).replace(',', '')
+    return m.group(1).replace(",", "")
 
 
 def _expand_decimal_point(m):
-    return m.group(1).replace('.', ' point ')
+    return m.group(1).replace(".", " point ")
 
 
 def _expand_dollars(m):
     match = m.group(1)
-    parts = match.split('.')
+    parts = match.split(".")
     if len(parts) > 2:
-        return match + ' dollars'  # Unexpected format
+        return match + " dollars"  # Unexpected format
+
     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 '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
     elif dollars:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        return '%s %s' % (dollars, dollar_unit)
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        return "%s %s" % (dollars, dollar_unit)
     elif cents:
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s' % (cents, cent_unit)
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s" % (cents, cent_unit)
     else:
-        return 'zero dollars'
+        return "zero dollars"
 
 
 def _expand_ordinal(m):
@@ -75,22 +72,24 @@ def _expand_ordinal(m):
 
 def _expand_number(m):
     num = int(m.group(0))
-    if num > 1000 and num < 3000:
+    if 1000 < num < 3000:
         if num == 2000:
-            return 'two thousand'
-        elif num > 2000 and num < 2010:
-            return 'two thousand ' + _inflect.number_to_words(num % 100)
+            return "two thousand"
+        elif 2000 < num < 2010:
+            return "two thousand " + _inflect.number_to_words(num % 100)
         elif num % 100 == 0:
-            return _inflect.number_to_words(num // 100) + ' hundred'
+            return _inflect.number_to_words(num // 100) + " hundred"
         else:
-            return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
+            return _inflect.number_to_words(
+                num, andword="", zero="oh", group=2
+            ).replace(", ", " ")
     else:
-        return _inflect.number_to_words(num, andword='')
+        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(_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)
@@ -109,7 +108,7 @@ def expand_numbers(text):
 
 
 def collapse_whitespace(text):
-    return re.sub(_whitespace_re, ' ', text)
+    return re.sub(_whitespace_re, " ", text)
 
 
 def convert_to_ascii(text):

kendex/prep_push_to_hf.py

@@ -5,6 +5,7 @@ import librosa
 import numpy as np
 import pandas as pd
 from datasets import Audio, Dataset, DatasetDict
+from transformers import AutoTokenizer
 
 from leviticus import normalize
 
@@ -12,6 +13,10 @@ MAX_DURATION_IN_SECONDS = 10.0
 MIN_DURATION_IN_SECONDS = 1.0
 MAX_LEN = 50
 MIN_LEN = 5
+SR = 16_000
+TOKENIZER_CHECKPOINT = "distilbert-base-uncased-finetuned-sst-2-english"
+
+tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_CHECKPOINT)
 
 
 def duration_filter(item):
@@ -28,12 +33,25 @@ def text_mapper(item):
     return item
 
 
-def create_dataset(item):
-    dataset = Dataset.from_pandas(item)
-    dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
-    dataset = dataset.filter(text_filter, input_columns=["text"])
-    dataset = dataset.filter(duration_filter, input_columns=["duration"])
-    return dataset
+def create_datasets(df):
+    def create_dataset(df_slice):
+        audio_column = "audio"
+        text_column = "text"
+        duration_column = "duration"
+
+        dataset = Dataset.from_pandas(df_slice)
+        dataset = dataset.cast_column(audio_column, Audio(sampling_rate=SR))
+        dataset = dataset.filter(text_filter, input_columns=[text_column])
+        dataset = dataset.filter(duration_filter, input_columns=[duration_column])
+        dataset = dataset.map(text_mapper, batched=False)
+
+        return dataset
+
+    train, test = np.split(df, [int(0.9 * len(df))])
+    train_dataset = create_dataset(train)
+    test_dataset = create_dataset(test)
+
+    return train_dataset, test_dataset
 
 
 def main():
@@ -53,15 +71,12 @@ def main():
 
     df = pd.DataFrame(data).sample(frac=1, random_state=666).reset_index(drop=True)
 
-    train, test = np.split(df, [int(0.9 * len(df))])
+    train, test = create_datasets(df)
 
-    train_dataset = create_dataset(train)
-    train_dataset = train_dataset.map(text_mapper)
-    test_dataset = create_dataset(test)
-    test_dataset = test_dataset.map(text_mapper)
+    full_dataset = DatasetDict({"train": train, "test": test})
 
-    full_dataset = DatasetDict({"train": train_dataset, "test": test_dataset})
-    full_dataset.push_to_hub("michaelnetbiz/Kendex")
+    print(full_dataset)
+    # full_dataset.push_to_hub("michaelnetbiz/Kendex")
 
 
 if __name__ == "__main__":