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Model: othrif/wav2vec2-large-xlsr-arabic
Source: Original Platform

evaluate.py

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+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+import tnkeeh as tn
+
+test_dataset = load_dataset("common_voice", "ar", split="test")
+wer = load_metric("wer")
+
+processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec2-large-xlsr-arabic")
+model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec2-large-xlsr-arabic")
+model.to("cuda")
+
+#chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\<5C>]'
+chars_to_ignore_regex = '[\؛\—\_get\«\»\ـ\ـ\,\?\.\!\-\;\:\"\“\%\‘\”\<EFBFBD>\#\،\☭,\؟\'ۚ\چ\ڨ\ﺃ\ھ\ﻻ\'ۖ]'
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def speech_file_to_array_fn(batch):
+    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler(speech_array).squeeze().numpy()
+    return batch
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+# For arabic diacritics
+cleander = tn.Tnkeeh(remove_diacritics=True)
+test_dataset = cleander.clean_hf_dataset(test_dataset, 'sentence')
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def evaluate(batch):
+    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+
+    with torch.no_grad():
+        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
+
+    pred_ids = torch.argmax(logits, dim=-1)
+    batch["pred_strings"] = processor.batch_decode(pred_ids)
+    return batch
+
+result = test_dataset.map(evaluate, batched=True, batch_size=32)
+
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))