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import torch |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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import tnkeeh as tn |
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test_dataset = load_dataset("common_voice", "ar", split="test") |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec2-large-xlsr-arabic") |
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model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec2-large-xlsr-arabic") |
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model.to("cuda") |
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chars_to_ignore_regex = '[\؛\—\_get\«\»\ـ\ـ\,\?\.\!\-\;\:\"\“\%\‘\”\�\#\،\☭,\؟\'ۚ\چ\ڨ\ﺃ\ھ\ﻻ\'ۖ]' |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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def speech_file_to_array_fn(batch): |
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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cleander = tn.Tnkeeh(remove_diacritics=True) |
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test_dataset = cleander.clean_hf_dataset(test_dataset, 'sentence') |
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def evaluate(batch): |
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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_strings"] = processor.batch_decode(pred_ids) |
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return batch |
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result = test_dataset.map(evaluate, batched=True, batch_size=32) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
