Update README.md

README.md

@@ -15,24 +15,34 @@ language:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# w2v2-bert-urdu
+# Wav2Vec-Bert-2.0-Urdu
 
-This model is a fine-tuned version of [UmarRamzan/w2v2-bert-urdu](https://huggingface.co/UmarRamzan/w2v2-bert-urdu) on an unknown dataset.
+This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the Urdu split of the [Common Voice 17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.3681
 - Wer: 0.2929
 
 ## Model description
 
-More information needed
+## Usage Instructions
 
-## Intended uses & limitations
+```python
+from transformers import AutoFeatureExtractor, Wav2Vec2BertModel
+import torch
+from datasets import load_dataset
 
-More information needed
+dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
+dataset = dataset.sort("id")
+sampling_rate = dataset.features["audio"].sampling_rate
 
-## Training and evaluation data
+processor = AutoProcessor.from_pretrained("UmarRamzan/w2v2-bert-urdu")
+model = Wav2Vec2BertModel.from_pretrained("UmarRamzan/w2v2-bert-urdu")
 
-More information needed
+# audio file is decoded on the fly
+inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+```
 
 ## Training procedure