Updated the README

README.md

@@ -88,7 +88,81 @@ print("Reference:", test_dataset["sentence"][:2])
 
 ## Evaluation
 
-The model can be evaluated as follows on the Hindi test data of Common Voice. 
+The model can be evaluated as follows on the following two datasets: 
+1. Custom dataset created from 20% of Indic, IIITH and CV (test)
+2. CommonVoice Hindi test dataset
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+
+## Load the datasets
+test_dataset = load_dataset("common_voice", "hi", split="test") 
+
+indic = load_dataset("csv", data_files= {'train':"/workspace/data/hi2/indic_train_full.csv",
+                                        "test": "/workspace/data/hi2/indic_test_full.csv"}, download_mode="force_redownload")
+iiith = load_dataset("csv", data_files= {"train": "/workspace/data/hi2/iiit_hi_train.csv", 
+                                        "test": "/workspace/data/hi2/iiit_hi_test.csv"}, download_mode="force_redownload")
+
+## Pre-process datasets and concatenate to create test dataset
+# Drop columns of common_voice
+split = ['train', 'test', 'validation', 'other', 'invalidated']
+
+for sp in split:
+    common_voice[sp] = common_voice[sp].remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment']) 
+    
+common_voice = common_voice.rename_column('path', 'audio_path')
+common_voice = common_voice.rename_column('sentence', 'target_text')
+
+train_dataset = datasets.concatenate_datasets([indic['train'], iiith['train'], common_voice['train']])
+test_dataset = datasets.concatenate_datasets([indic['test'], iiith['test'], common_voice['test'], common_voice['validation']])
+
+## Load model from HF hub
+
+wer = load_metric("wer")
+
+processor = Wav2Vec2Processor.from_pretrained("skylord/wav2vec2-large-xlsr-hindi") 
+model = Wav2Vec2ForCTC.from_pretrained("skylord/wav2vec2-large-xlsr-hindi")
+model.to("cuda")
+
+chars_to_ignore_regex = '[\,\?\.\!\-\'\;\:\"\“\%\‘\”\�Utrnle\_]'
+unicode_ignore_regex = r'[dceMaWpmFui\xa0\u200d]' # Some unwanted unicode chars
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+
+def speech_file_to_array_fn(batch):
+  batch["target_text"] = re.sub(chars_to_ignore_regex, '', batch["target_text"])
+  batch["target_text"] = re.sub(unicode_ignore_regex, '', batch["target_text"])
+    
+  speech_array, sampling_rate = torchaudio.load(batch["audio_path"])
+  batch["speech"] = resampler(speech_array).squeeze().numpy()
+  return batch
+  
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+
+# Preprocessing the datasets.
+# We need to read the aduio 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 on custom dataset**: 19.xx % 
+
+
 
 
 ```python