ar / README.md

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	---
	language: ar
	datasets:
	- common_voice
	- arabic_speech_corpus
	metrics:
	- wer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: Mohammed XLSR Wav2Vec2 Large 53
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice ar
	type: common_voice
	args: ar
	metrics:
	- name: Test WER
	type: wer
	value: 36.69
	- name: Validation WER
	type: wer
	value: 36.69
	---
	# Wav2Vec2-Large-XLSR-53-Arabic

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
	on Arabic using the `train` splits of [Common Voice](https://huggingface.co/datasets/common_voice)
	and [Arabic Speech Corpus](https://huggingface.co/datasets/arabic_speech_corpus).
	When using this model, make sure that your speech input is sampled at 16kHz.

	## Usage

	The model can be used directly (without a language model) as follows:

	```python
	%%capture
	!pip install datasets
	!pip install transformers==4.4.0
	!pip install torchaudio
	!pip install jiwer
	!pip install tnkeeh

	import torch
	import torchaudio
	from datasets import load_dataset
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

	test_dataset = load_dataset("common_voice", "ar", split="test[:2%]")


	processor = Wav2Vec2Processor.from_pretrained("mohammed/ar")
	model = Wav2Vec2ForCTC.from_pretrained("mohammed/ar")

	resampler = torchaudio.transforms.Resample(48_000, 16_000)

	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	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)
	inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

	with torch.no_grad():
	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

	predicted_ids = torch.argmax(logits, dim=-1)

	print("The predicted sentence is: ", processor.batch_decode(predicted_ids))
	print("The original sentence is:", test_dataset["sentence"][:2])
	```

	The output is:

	```
	The predicted sentence is : ['ألديك قلم', 'ليست نارك مكسافة على هذه الأرض أبعد من يوم أمس']
	The original sentence is: ['ألديك قلم ؟', 'ليست هناك مسافة على هذه الأرض أبعد من يوم أمس.']
	```

	## Evaluation

	The model can be evaluated as follows on the Arabic test data of Common Voice:

	```python

	import torch
	import torchaudio
	from datasets import load_dataset, load_metric
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
	import re
	# creating a dictionary with all diacritics
	dict = {
	'ِ': '',
	'ُ': '',
	'ٓ': '',
	'ٰ': '',
	'ْ': '',
	'ٌ': '',
	'ٍ': '',
	'ً': '',
	'ّ': '',
	'َ': '',
	'~': '',
	',': '',
	'ـ': '',
	'—': '',
	'.': '',
	'!': '',
	'-': '',
	';': '',
	':': '',
	'\'': '',
	'"': '',
	'☭': '',
	'«': '',
	'»': '',
	'؛': '',
	'ـ': '',
	'_': '',
	'،': '',
	'“': '',
	'%': '',
	'‘': '',
	'”': '',
	'�': '',
	'_': '',
	',': '',
	'?': '',
	'#': '',
	'‘': '',
	'.': '',
	'؛': '',
	'get': '',
	'؟': '',
	' ': ' ',
	'\'ۖ ': '',
	'\'': '',
	'\'ۚ' : '',
	' \'': '',
	'31': '',
	'24': '',
	'39': ''
	}

	# replacing multiple diacritics using dictionary (stackoverflow is amazing)
	def remove_special_characters(batch):
	# Create a regular expression from the dictionary keys
	regex = re.compile("(%s)" % "\|".join(map(re.escape, dict.keys())))
	# For each match, look-up corresponding value in dictionary
	batch["sentence"] = regex.sub(lambda mo: dict[mo.string[mo.start():mo.end()]], batch["sentence"])
	return batch


	test_dataset = load_dataset("common_voice", "ar", split="test")
	wer = load_metric("wer")

	processor = Wav2Vec2Processor.from_pretrained("mohammed/ar")
	model = Wav2Vec2ForCTC.from_pretrained("mohammed/ar")
	model.to("cuda")


	resampler = torchaudio.transforms.Resample(48_000, 16_000)

	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	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)
	test_dataset = test_dataset.map(remove_special_characters)
	# Preprocessing the datasets.
	# We need to read the audio 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=8)

	print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
	```

	Test Result: 36.69%


	## Future Work

	One can use data augmentation, transliteration, or attention_mask to increase the accuracy.