Adding code for evaluation

README.md

@@ -50,6 +50,65 @@ The specific dataset used to create the model is called "Ravnursson Faroese Spee
 	
 The fine-tuning process was perform during November (2022) in the servers of the Language and Voice Lab (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.
 
+# Evaluation
+```python
+import torch
+from transformers import Wav2Vec2Processor
+from transformers import Wav2Vec2ForCTC
+
+#Load the processor and model.
+MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h"
+processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
+model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)
+
+#Load the dataset
+from datasets import load_dataset, load_metric, Audio
+ds=load_dataset("carlosdanielhernandezmena/ravnursson_asr",split='test')
+
+#Downsample to 16kHz
+ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
+
+#Process the dataset
+def prepare_dataset(batch):
+    audio = batch["audio"]
+    #Batched output is "un-batched" to ensure mapping is correct
+    batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
+    with processor.as_target_processor():
+        batch["labels"] = processor(batch["normalized_text"]).input_ids
+    return batch
+ds = ds.map(prepare_dataset, remove_columns=ds.column_names,num_proc=1)
+
+#Define the evaluation metric
+import numpy as np
+wer_metric = load_metric("wer")
+def compute_metrics(pred):
+    pred_logits = pred.predictions
+    pred_ids = np.argmax(pred_logits, axis=-1)
+    pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
+    pred_str = processor.batch_decode(pred_ids)
+    #We do not want to group tokens when computing the metrics
+    label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
+    wer = wer_metric.compute(predictions=pred_str, references=label_str)
+    return {"wer": wer}
+
+#Do the evaluation (with batch_size=1)
+model = model.to(torch.device("cuda"))
+def map_to_result(batch):
+    with torch.no_grad():
+        input_values = torch.tensor(batch["input_values"], device="cuda").unsqueeze(0)
+        logits = model(input_values).logits
+    pred_ids = torch.argmax(logits, dim=-1)
+    batch["pred_str"] = processor.batch_decode(pred_ids)[0]
+    batch["sentence"] = processor.decode(batch["labels"], group_tokens=False)
+    return batch
+results = ds.map(map_to_result,remove_columns=ds.column_names)
+
+#Compute the overall WER now.
+print("Test WER: {:.3f}".format(wer_metric.compute(predictions=results["pred_str"], references=results["sentence"])))
+
+```
+**Test Result**: 0.076
+
 # BibTeX entry and citation info
 *When publishing results based on these models please refer to:*
 ```bibtex