Update README.MD

README.md

@@ -1 +1,118 @@
-hello
+---
+language: ar
+datasets:
+- common_voice 
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: XLSR Wav2Vec2 Arabic by Othmane Rifki
+  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: 46.77
+---
+
+# Wav2Vec2-Large-XLSR-53-Arabic
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the [Common Voice](https://huggingface.co/datasets/common_voice). 
+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
+import librosa
+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("kmfoda/wav2vec2-large-xlsr-arabic")
+model = Wav2Vec2ForCTC.from_pretrained("kmfoda/wav2vec2-large-xlsr-arabic")
+
+def prepare_example(example):
+    example["speech"], _ = librosa.load(example["path"], sr=16000)
+    return example
+
+test_dataset = test_dataset.map(prepare_example)
+
+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("Prediction:", processor.batch_decode(predicted_ids))
+print("Reference:", test_dataset["sentence"][:2])
+```
+
+## Evaluation
+
+The model can be evaluated as follows on the Arabic test data of Common Voice. 
+
+```python
+import librosa
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+
+test_dataset = load_dataset("common_voice", "ar", split="test") 
+wer = load_metric("wer")
+processor = Wav2Vec2Processor.from_pretrained("kmfoda/wav2vec2-large-xlsr-arabic") 
+model = Wav2Vec2ForCTC.from_pretrained("kmfoda/wav2vec2-large-xlsr-arabic")
+model.to("cuda")
+
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\؟\_\؛\ـ\—]'
+
+def prepare_example(example):
+    example["speech"], _ = librosa.load(example["path"], sr=16000)
+    return example
+
+test_dataset = test_dataset.map(prepare_example)
+
+# 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**: ??
+
+
+## Training
+
+The Common Voice `train`, `validation` datasets were used for training.
+
+The script used for training can be found [here](https://huggingface.co/othrif/wav2vec2-large-xlsr-arabic/tree/main)