fairseq tuned_wer

README.md

@@ -23,7 +23,7 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 27.08
+         value: 24.91
 ---
 
 # Wav2Vec2-Base-760-Turkish
@@ -109,7 +109,7 @@ def evaluate(batch):
         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)
+    batch["pred_strings"] = processor.batch_decode(pred_ids,skip_special_tokens=True)
     return batch
 
 result = test_dataset.map(evaluate, batched=True, batch_size=8)
@@ -117,7 +117,7 @@ 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**: 27.08 % (in progress)
+**Test Result**: 24.91 % (in progress)
 
 
 ## Training