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| import gradio as gr | |
| gr.load("models/m3hrdadfi/wav2vec2-large-xlsr-persian").launch() | |
| import librosa | |
| import torch | |
| import torchaudio | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
| from datasets import load_dataset | |
| import numpy as np | |
| import hazm | |
| import re | |
| import string | |
| import IPython.display as ipd | |
| _normalizer = hazm.Normalizer() | |
| chars_to_ignore = [ | |
| ",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", | |
| "#", "!", "؟", "?", "«", "»", "ء", "،", "(", ")", "؛", "'ٔ", "٬",'ٔ', ",", "?", | |
| ".", "!", "-", ";", ":",'"',"“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„' | |
| ] | |
| # In case of farsi | |
| chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits) | |
| chars_to_mapping = { | |
| 'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی', | |
| 'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی", | |
| "ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع", | |
| "ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", "ئ": "ی", 'ﺍ': "ا", 'ة': "ه", | |
| 'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش", | |
| 'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ", | |
| "\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ", | |
| } | |
| 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 normalizer(batch, chars_to_ignore, chars_to_mapping): | |
| chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]""" | |
| text = batch["sentence"].lower().strip() | |
| text = _normalizer.normalize(text) | |
| text = multiple_replace(text, chars_to_mapping) | |
| text = remove_special_characters(text, chars_to_ignore_regex) | |
| batch["sentence"] = text | |
| return batch | |
| def speech_file_to_array_fn(batch): | |
| speech_array, sampling_rate = torchaudio.load(batch["path"]) | |
| speech_array = speech_array.squeeze().numpy() | |
| speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000) | |
| batch["speech"] = speech_array | |
| return batch | |
| def predict(batch): | |
| features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) | |
| input_values = features.input_values.to(device) | |
| attention_mask = features.attention_mask.to(device) | |
| with torch.no_grad(): | |
| logits = model(input_values, attention_mask=attention_mask).logits | |
| pred_ids = torch.argmax(logits, dim=-1) | |
| batch["predicted"] = processor.batch_decode(pred_ids)[0] | |
| return batch | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian") | |
| model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian").to(device) | |
| dataset = load_dataset("common_voice", "fa", split="test[:1%]") | |
| dataset = dataset.map( | |
| normalizer, | |
| fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping}, | |
| remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])) | |
| ) | |
| dataset = dataset.map(speech_file_to_array_fn) | |
| result = dataset.map(predict) | |
| max_items = np.random.randint(0, len(result), 20).tolist() | |
| for i in max_items: | |
| reference, predicted = result["sentence"][i], result["predicted"][i] | |
| print("reference:", reference) | |
| print("predicted:", predicted) | |
| print('---') | |