46 lines
1.8 KiB
Python
46 lines
1.8 KiB
Python
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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 = '[\,\?\.\!\-\;\:\"\“\'\<5C>]'
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chars_to_ignore_regex = '[\؛\—\_get\«\»\ـ\ـ\,\?\.\!\-\;\:\"\“\%\‘\”\<EFBFBD>\#\،\☭,\؟\'ۚ\چ\ڨ\ﺃ\ھ\ﻻ\'ۖ]'
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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# We need to read the aduio files as arrays
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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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# For arabic diacritics
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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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# Preprocessing the datasets.
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# We need to read the aduio files as arrays
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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"])))
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