🚀 Wav2Vec2-Large-XLSR-53-Arabic
This model is a fine - tuned version of facebook/wav2vec2-large-xlsr-53 on Arabic, aiming to provide high - quality automatic speech recognition.
📋 Metadata
Property |
Details |
Language |
Arabic |
Datasets |
common_voice, arabic_speech_corpus |
Metrics |
wer |
Tags |
audio, automatic - speech - recognition, speech, xlsr - fine - tuning - week |
License |
apache - 2.0 |
📊 Model Index
Model Name |
Task |
Dataset |
Metrics |
Mohammed XLSR Wav2Vec2 Large 53 |
Speech Recognition (automatic - speech - recognition) |
Common Voice ar (common_voice, args: ar) |
Test WER: 36.69%, Validation WER: 36.69% |
🚀 Quick Start
This model is a fine - tuned version of facebook/wav2vec2-large-xlsr-53 on Arabic, using the train
splits of Common Voice and Arabic Speech Corpus. When using this model, ensure that your speech input is sampled at 16kHz.
💻 Usage Examples
Basic Usage
%%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)
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 on the Arabic test data of Common Voice as follows:
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
dict = {
'ِ': '',
'ُ': '',
'ٓ': '',
'ٰ': '',
'ْ': '',
'ٌ': '',
'ٍ': '',
'ً': '',
'ّ': '',
'َ': '',
'~': '',
',': '',
'ـ': '',
'—': '',
'.': '',
'!': '',
'-': '',
';': '',
':': '',
'\'': '',
'"': '',
'☭': '',
'«': '',
'»': '',
'؛': '',
'ـ': '',
'_': '',
'،': '',
'“': '',
'%': '',
'‘': '',
'”': '',
'�': '',
'_': '',
',': '',
'?': '',
'#': '',
'‘': '',
'.': '',
'؛': '',
'get': '',
'؟': '',
' ': ' ',
'\'ۖ ': '',
'\'': '',
'\'ۚ' : '',
' \'': '',
'31': '',
'24': '',
'39': ''
}
def remove_special_characters(batch):
regex = re.compile("(%s)" % "|".join(map(re.escape, dict.keys())))
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)
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)
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.