Update README.md

README.md

@@ -132,14 +132,14 @@ test_dataset = test_dataset.map(speech_file_to_array_fn)
 # Preprocessing the datasets.
 # We need to read the audio files as arrays
 def evaluate(batch):
-\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
 
-\twith torch.no_grad():
-\t\tlogits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
+\\twith torch.no_grad():
+\\t\\tlogits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
 
-\tpred_ids = torch.argmax(logits, dim=-1)
-\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
-\treturn batch
+\\tpred_ids = torch.argmax(logits, dim=-1)
+\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
+\\treturn batch
 
 result = test_dataset.map(evaluate, batched=True, batch_size=8)
 
@@ -158,4 +158,5 @@ My model may report better scores than others because of some specificity of my
 | ------------- | ------------- | ------------- |
 | jonatasgrosman/wav2vec2-large-xlsr-53-hungarian | **31.40%** | **6.20%** |
 | anton-l/wav2vec2-large-xlsr-53-hungarian | 42.39% | 9.39% |
+| gchhablani/wav2vec2-large-xlsr-hu | 46.42% | 10.04% |
 | birgermoell/wav2vec2-large-xlsr-hungarian | 46.93% | 10.31% |