updating README file

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: 26.55
+       - name: Validation WER
+         type: wer
+         value: 36.53
+---
+# 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/wav2vec2-large-xlsr-arabic")
+model = Wav2Vec2ForCTC.from_pretrained("mohammed/wav2vec2-large-xlsr-arabic")
+
+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/wav2vec2-large-xlsr-arabic") 
+model = Wav2Vec2ForCTC.from_pretrained("mohammed/wav2vec2-large-xlsr-arabic")
+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**: 36.53%
+
+
+## Future Work
+
+One can use *data augmentation*, *transliteration*, or *attention_mask* to increase the accuracy.