Instructions to use NightPrince/Nemo-Arabic-STT-Diacritized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use NightPrince/Nemo-Arabic-STT-Diacritized with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("NightPrince/Nemo-Arabic-STT-Diacritized") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
| import re | |
| import bw2ar | |
| import torch | |
| import xer | |
| # Diacritics | |
| FATHATAN = u'\u064b' | |
| DAMMATAN = u'\u064c' | |
| KASRATAN = u'\u064d' | |
| FATHA = u'\u064e' | |
| DAMMA = u'\u064f' | |
| KASRA = u'\u0650' | |
| SHADDA = u'\u0651' | |
| SUKUN = u'\u0652' | |
| TATWEEL = u'\u0640' | |
| HARAKAT_PAT = re.compile(u"["+u"".join([FATHATAN, DAMMATAN, KASRATAN, | |
| FATHA, DAMMA, KASRA, SUKUN, | |
| SHADDA])+u"]") | |
| class TashkeelTokenizer: | |
| def __init__(self): | |
| self.letters = [' ', '$', '&', "'", '*', '<', '>', 'A', 'D', 'E', 'H', 'S', 'T', 'Y', 'Z', | |
| 'b', 'd', 'f', 'g', 'h', 'j', 'k', 'l', 'm', 'n', 'p', 'q', 'r', 's', 't', | |
| 'v', 'w', 'x', 'y', 'z', '|', '}' | |
| ] | |
| self.letters = ['<PAD>', '<BOS>', '<EOS>'] + self.letters + ['<MASK>'] | |
| self.no_tashkeel_tag = '<NT>' | |
| self.tashkeel_list = ['<NT>', '<SD>', '<SDD>', '<SF>', '<SFF>', '<SK>', | |
| '<SKK>', 'F', 'K', 'N', 'a', 'i', 'o', 'u', '~'] | |
| self.tashkeel_list = ['<PAD>', '<BOS>', '<EOS>'] + self.tashkeel_list | |
| self.tashkeel_map = {c:i for i,c in enumerate(self.tashkeel_list)} | |
| self.letters_map = {c:i for i,c in enumerate(self.letters)} | |
| self.inverse_tags = { | |
| '~a': '<SF>', # shaddah and fatHa | |
| '~u': '<SD>', # shaddah and Damma | |
| '~i': '<SK>', # shaddah and kasra | |
| '~F': '<SFF>', # shaddah and fatHatayn | |
| '~N': '<SDD>', # shaddah and Dammatayn | |
| '~K': '<SKK>' # shaddah and kasratayn | |
| } | |
| self.tags = {v:k for k,v in self.inverse_tags.items()} | |
| self.shaddah_last = ['a~', 'u~', 'i~', 'F~', 'N~', 'K~'] | |
| self.shaddah_first = ['~a', '~u', '~i', '~F', '~N', '~K'] | |
| self.tahkeel_chars = ['F','N','K','a', 'u', 'i', '~', 'o'] | |
| def clean_text(self, text): | |
| text = re.sub(u'[%s]' % u'\u0640', '', text) # strip tatweel | |
| text = text.replace('ٱ', 'ا') | |
| return ' '.join(re.sub(u"[^\u0621-\u063A\u0640-\u0652\u0670\u0671\ufefb\ufef7\ufef5\ufef9 ]", " ", text, flags=re.UNICODE).split()) | |
| def check_match(self, text_with_tashkeel, letter_n_tashkeel_pairs): | |
| text_with_tashkeel = text_with_tashkeel.strip() | |
| # test if the reconstructed text with tashkeel is the same as the original one | |
| syn_text = self.combine_tashkeel_with_text(letter_n_tashkeel_pairs) | |
| return syn_text == text_with_tashkeel or syn_text == self.unify_shaddah_position(text_with_tashkeel) | |
| def unify_shaddah_position(self, text_with_tashkeel): | |
| # unify the order of shaddah and the harakah to make shaddah always at the beginning | |
| for i in range(len(self.shaddah_first)): | |
| text_with_tashkeel = text_with_tashkeel.replace(self.shaddah_last[i], self.shaddah_first[i]) | |
| return text_with_tashkeel | |
| def split_tashkeel_from_text(self, text_with_tashkeel, test_match=True): | |
| text_with_tashkeel = self.clean_text(text_with_tashkeel) | |
| text_with_tashkeel = bw2ar.transliterate_text(text_with_tashkeel, 'ar2bw') | |
| text_with_tashkeel = text_with_tashkeel.replace('`', '') # remove dagger 'alif | |
| # unify the order of shaddah and the harakah to make shaddah always at the beginning | |
| text_with_tashkeel = self.unify_shaddah_position(text_with_tashkeel) | |
| # remove duplicated harakat | |
| for i in range(len(self.tahkeel_chars)): | |
| text_with_tashkeel = text_with_tashkeel.replace(self.tahkeel_chars[i]*2, self.tahkeel_chars[i]) | |
| letter_n_tashkeel_pairs = [] | |
| for i in range(len(text_with_tashkeel)): # go over the whole text | |
| # check if the first character is a normal letter and the second character is a tashkeel | |
| if i < (len(text_with_tashkeel) - 1) and not text_with_tashkeel[i] in self.tashkeel_list and text_with_tashkeel[i+1] in self.tashkeel_list: | |
| # IMPORTANT: check if tashkeel is Shaddah, then there might be another Tashkeel char associated with it. If so, | |
| # replace both Shaddah and the Tashkeel chars with the appropriate tag | |
| if text_with_tashkeel[i+1] == '~': | |
| # IMPORTANT: the following if statement depends on the concept of short circuit!! | |
| # The first condition checks if there are still more chars before it access position i+2 | |
| # "text_with_tashkeel[i+2]" since it causes "index out of range" exception. Notice that | |
| # Shaddah here is put in the first position before the Harakah. | |
| if i+2 < len(text_with_tashkeel) and f'~{text_with_tashkeel[i+2]}' in self.inverse_tags: | |
| letter_n_tashkeel_pairs.append((text_with_tashkeel[i], self.inverse_tags[f'~{text_with_tashkeel[i+2]}'])) | |
| else: | |
| # if it is only Shaddah, just add it to the list | |
| letter_n_tashkeel_pairs.append((text_with_tashkeel[i], '~')) | |
| else: | |
| letter_n_tashkeel_pairs.append((text_with_tashkeel[i], text_with_tashkeel[i+1])) | |
| # if the character at position i is a normal letter and has no Tashkeel, then add | |
| # it with the tag "self.no_tashkeel_tag" | |
| # IMPORTANT: this elif block ensures also that there is no two or more consecutive tashkeel other than shaddah | |
| elif not text_with_tashkeel[i] in self.tashkeel_list: | |
| letter_n_tashkeel_pairs.append((text_with_tashkeel[i], self.no_tashkeel_tag)) | |
| if test_match: | |
| # test if the split is done correctly by ensuring that we can retrieve back the original text | |
| assert self.check_match(text_with_tashkeel, letter_n_tashkeel_pairs) | |
| return [('<BOS>', '<BOS>')] + letter_n_tashkeel_pairs + [('<EOS>', '<EOS>')] | |
| def combine_tashkeel_with_text(self, letter_n_tashkeel_pairs): | |
| combined_with_tashkeel = [] | |
| for letter, tashkeel in letter_n_tashkeel_pairs: | |
| combined_with_tashkeel.append(letter) | |
| if tashkeel in self.tags: | |
| combined_with_tashkeel.append(self.tags[tashkeel]) | |
| elif tashkeel != self.no_tashkeel_tag: | |
| combined_with_tashkeel.append(tashkeel) | |
| text = ''.join(combined_with_tashkeel) | |
| return text | |
| def encode(self, text_with_tashkeel, test_match=True): | |
| letter_n_tashkeel_pairs = self.split_tashkeel_from_text(text_with_tashkeel, test_match) | |
| text, tashkeel = zip(*letter_n_tashkeel_pairs) | |
| input_ids = [self.letters_map[c] for c in text] | |
| target_ids = [self.tashkeel_map[c] for c in tashkeel] | |
| return torch.LongTensor(input_ids), torch.LongTensor(target_ids) | |
| def filter_tashkeel(self, tashkeel): | |
| tmp = [] | |
| for i, t in enumerate(tashkeel): | |
| if i != 0 and t == '<BOS>': | |
| t = self.no_tashkeel_tag | |
| elif i != (len(tashkeel) - 1) and t == '<EOS>': | |
| t = self.no_tashkeel_tag | |
| tmp.append(t) | |
| tashkeel = tmp | |
| return tashkeel | |
| def decode(self, input_ids, target_ids): | |
| # print('input_ids.shape:', input_ids.shape) | |
| # print('target_ids.shape:', target_ids.shape) | |
| input_ids = input_ids.cpu().tolist() | |
| target_ids = target_ids.cpu().tolist() | |
| ar_texts = [] | |
| for j in range(len(input_ids)): | |
| letters = [self.letters[i] for i in input_ids[j]] | |
| tashkeel = [self.tashkeel_list[i] for i in target_ids[j]] | |
| letters = list(filter(lambda x: x != '<BOS>' and x != '<EOS>' and x != '<PAD>', letters)) | |
| tashkeel = self.filter_tashkeel(tashkeel) | |
| tashkeel = list(filter(lambda x: x != '<BOS>' and x != '<EOS>' and x != '<PAD>', tashkeel)) | |
| # VERY IMPORTANT NOTE: zip takes min(len(letters), len(tashkeel)) and discard the reset of letters / tashkeels | |
| letter_n_tashkeel_pairs = list(zip(letters, tashkeel)) | |
| bw_text = self.combine_tashkeel_with_text(letter_n_tashkeel_pairs) | |
| ar_text = bw2ar.transliterate_text(bw_text, 'bw2ar') | |
| ar_texts.append(ar_text) | |
| return ar_texts | |
| def get_tashkeel_with_case_ending(self, text, case_ending=True): | |
| text_split = self.split_tashkeel_from_text(text, test_match=False) | |
| text_spaces_indecies = [i for i, el in enumerate(text_split) if el == (' ', '<NT>')] | |
| new_text_split = [] | |
| for i, el in enumerate(text_split): | |
| if not case_ending and (i+1) in text_spaces_indecies: | |
| el = (el[0], '<NT>') # no case ending | |
| new_text_split.append(el) | |
| letters, tashkeel = zip(*new_text_split) | |
| return letters, tashkeel | |
| def compute_der(self, ref, hyp, case_ending=True): | |
| _, ref_tashkeel = self.get_tashkeel_with_case_ending(ref, case_ending=case_ending) | |
| _, hyp_tashkeel = self.get_tashkeel_with_case_ending(hyp, case_ending=case_ending) | |
| ref_tashkeel = ' '.join(ref_tashkeel) | |
| hyp_tashkeel = ' '.join(hyp_tashkeel) | |
| return xer.wer(ref_tashkeel, hyp_tashkeel) | |
| def compute_wer(self, ref, hyp, case_ending=True): | |
| ref_letters, ref_tashkeel = self.get_tashkeel_with_case_ending(ref, case_ending=case_ending) | |
| hyp_letters, hyp_tashkeel = self.get_tashkeel_with_case_ending(hyp, case_ending=case_ending) | |
| ref_text_combined = self.combine_tashkeel_with_text(zip(ref_letters, ref_tashkeel)) | |
| hyp_text_combined = self.combine_tashkeel_with_text(zip(hyp_letters, hyp_tashkeel)) | |
| return xer.wer(ref_text_combined, hyp_text_combined) | |
| def remove_tashkeel(self, text): | |
| text = HARAKAT_PAT.sub('', text) | |
| text = re.sub(u"[\u064E]", "", text, flags=re.UNICODE) # fattha | |
| text = re.sub(u"[\u0671]", "", text, flags=re.UNICODE) # waSla | |
| return text | |
| if __name__ == '__main__': | |
| import utils | |
| from tqdm import tqdm | |
| tokenizer = TashkeelTokenizer() | |
| txt_folder_path = 'dataset/train' | |
| prepared_lines = [] | |
| for filepath in utils.get_files(txt_folder_path, '*.txt'): | |
| print(f'Reading file: {filepath}') | |
| with open(filepath) as f1: | |
| for line in f1: | |
| clean_line = tokenizer.clean_text(line) | |
| if clean_line != '': | |
| prepared_lines.append(clean_line) | |
| print(f'completed file: {filepath}') | |
| good_sentences = [] | |
| bad_sentences = [] | |
| tokenized_sentences = [] | |
| for line in tqdm(prepared_lines): | |
| try: | |
| letter_n_tashkeel_pairs = tokenizer.split_tashkeel_from_text(line, test_match=True) | |
| tokenized_sentences.append(letter_n_tashkeel_pairs) | |
| good_sentences.append(line) | |
| except AssertionError as e: | |
| bad_sentences.append(line) | |
| print('len(good_sentences), len(bad_sentences):', len(good_sentences), len(bad_sentences)) | |