初始化项目，由ModelHub XC社区提供模型

Model: m3hrdadfi/wav2vec2-large-xlsr-persian-v3
Source: Original Platform

normalizer.py

@@ -0,0 +1,203 @@
+from parsivar import Normalizer
+
+import num2fawords
+import re
+import string
+
+
+_normalizer = Normalizer(half_space_char="\u200c", statistical_space_correction=True)
+chars_to_ignore = [
+    ",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "<EFBFBD>",
+    "#", "!", "؟", "?", "«", "»", "،", "(", ")", "؛", "'ٔ", "٬", 'ٔ', ",", "?",
+    ".", "!", "-", ";", ":", '"', "“", "%", "‘", "”", "<EFBFBD>", "–", "…", "_", "”", '“', '„',
+    'ā', 'š', 'ّ', 'ْ',
+]
+chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits)
+chars_to_ignore = f"""[{"".join(chars_to_ignore)}]"""
+zwnj = "\u200c"
+silent_chars = ["ا", "د", "ذ", "ر", "ز", "و", "آ"] + [zwnj] + [" "]
+
+
+def multiple_replace(text, chars_to_mapping):
+    pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
+    return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
+
+
+def remove_special_characters(text, chars_to_ignore_regex):
+    text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
+    return text
+
+
+def convert_word_nums_to_text(word):
+    try:
+        word = int(word)
+        word = num2fawords.words(word)
+    except:
+        word = word
+
+    return word
+
+
+def normalizer_at_word_level(text):
+    words = text.split()
+    _text = []
+
+    for word in words:
+        word = convert_word_nums_to_text(word)
+        word = fixator_dictionary.get(word, word)
+
+        _text.append(word)
+
+    return " ".join(_text) + " "
+
+
+def finder(ss, s, starter=False):
+    found = []
+    for m in re.finditer(ss, s):
+        if starter:
+            found.append(m.start())
+        else:
+            found.append((m.start(), m.end()))
+
+    return found
+
+
+def substring_replace(ss, s, start, end, stripped=True):
+    s_start = s[:start]
+    s_end = s[end:]
+
+    counter = 0
+    if stripped:
+        counter = 1 if s_start.endswith(" ") else counter
+        s_start = s_start.rstrip()
+
+    return s_start + ss + s_end, counter
+
+
+def normalizer(
+        batch,
+        is_normalize=True,
+        return_dict=True,
+        filter_trivials=False,
+        remove_extra_space=False
+):
+    text = batch["sentence"].lower().strip()
+
+    # Parsivar normalizer
+    if is_normalize:
+        text = _normalizer.normalize(text)
+
+    # Dictionary mapping
+    text = multiple_replace(text, dictionary_mapping)
+    text = re.sub(" +", " ", text)
+
+    # Remove specials
+    text = remove_special_characters(text, chars_to_ignore)
+    text = re.sub(" +", " ", text)
+
+    # Replace connected آ
+    special, pointer = "آ", int("0")
+    for f in sorted(finder(special, text, True)):
+        index = f + pointer - 1
+        if len(text) >= index:
+            if text[index] not in silent_chars:
+                new_text, extra_pointer = substring_replace(
+                    f"{text[index]}{zwnj}", text, index, index + 1, stripped=True)
+                text = new_text
+                pointer += 1 + 1 - 1 - extra_pointer
+
+    # Replace connected ها
+    pointer = int("0")
+    special_list = [
+        # "ام", "ای", "است", "ایم", "اید", "اند",
+        "هایمان", "هایم", "هایت", "هایش",
+        "هایتان", "هایشان", "هام", "هات",
+        "هاتان", "هامون", "هامان", "هاش",
+        "هاتون", "هاشان", "هاشون",
+        "هایی", "های", "هاس", "ها"
+    ]
+    for special in special_list:
+        pointer = 0
+        text = text
+        for f in sorted(finder(special, text, False)):
+            start, end = f[0] + pointer - 1, f[1] + pointer - 1
+            if len(text) >= (end + 1):
+                if len(text) == (end + 1):
+                    new_text, extra_pointer = substring_replace(
+                        f"{zwnj}{special}",
+                        text,
+                        start + 1,
+                        end + 1,
+                        stripped=True)
+                    text = new_text
+                    pointer += 1 + 1 - 1 - extra_pointer
+                else:
+                    if text[end + 1] == " ":
+                        new_text, extra_pointer = substring_replace(
+                            f"{zwnj}{special}",
+                            text,
+                            start + 1,
+                            end + 1,
+                            stripped=True)
+                        text = new_text
+                        pointer += 1 + 1 - 1 - extra_pointer
+
+    special, pointer = "افزار", int("0")
+    for f in sorted(finder(special, text, False)):
+        start, end = f[0] + pointer - 1, f[1] + pointer - 1
+
+        if len(text) >= (end + 1):
+            new_text, extra_pointer = substring_replace(f"{zwnj}{special}", text, start + 1, end + 1, stripped=True)
+            text = new_text
+            pointer += 1 + 1 - 1 - extra_pointer
+
+    # Replace connected ها
+    pointer = int("0")
+    special_list = [
+        "ترین", "تر"
+    ]
+    for special in special_list:
+        pointer = 0
+        text = text
+        for f in sorted(finder(special, text, False)):
+            start, end = f[0] + pointer - 1, f[1] + pointer - 1
+            if len(text) >= (end + 1):
+                if len(text) == (end + 1):
+                    new_text, extra_pointer = substring_replace(
+                        f"{zwnj}{special}",
+                        text,
+                        start + 1,
+                        end + 1,
+                        stripped=True)
+                    text = new_text
+                    pointer += 1 + 1 - 1 - extra_pointer
+                else:
+                    if text[end + 1] == " ":
+                        new_text, extra_pointer = substring_replace(
+                            f"{zwnj}{special}",
+                            text,
+                            start + 1,
+                            end + 1,
+                            stripped=True)
+                        text = new_text
+                        pointer += 1 + 1 - 1 - extra_pointer
+
+    # Normalizer at word level
+    text = normalizer_at_word_level(text)
+    text = re.sub(" +", " ", text)
+
+    if remove_extra_space:
+        text = text.strip()
+    else:
+        text = text.strip() + " "
+
+    if filter_trivials:
+        if not len(text) > 2:
+            text = None
+
+    if not return_dict:
+        return text
+
+    batch["sentence"] = text
+    return batch
+