adjust README

README.md

@@ -24,10 +24,10 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 12.18
+         value: 12.51
        - name: Test CER
          type: cer
-         value: 11.01
+         value: 13.59
 ---
 
 # Wav2Vec2-Large-XLSR-53-portuguese
@@ -49,8 +49,9 @@ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
 LANG_ID = "pt"
 MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-portuguese"
+SAMPLES = 5
 
-test_dataset = load_dataset("common_voice", LANG_ID, split="test[:2%]")
+test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
 
 processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
 model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
@@ -64,17 +65,29 @@ def speech_file_to_array_fn(batch):
     return batch
 
 test_dataset = test_dataset.map(speech_file_to_array_fn)
-inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
 
 with torch.no_grad():
     logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
 
 predicted_ids = torch.argmax(logits, dim=-1)
+predicted_sentences = processor.batch_decode(predicted_ids)
 
-print("Prediction:", processor.batch_decode(predicted_ids))
-print("Reference:", test_dataset[:2]["sentence"])
+for i, predicted_sentence in enumerate(predicted_sentences):
+    print("-" * 100)
+    print("Reference:", test_dataset[i]["sentence"])
+    print("Prediction:", predicted_sentence)
 ```
 
+| Reference  | Prediction |
+| ------------- | ------------- |
+| NEM O RADAR NEM OS OUTROS INSTRUMENTOS DETECTARAM O BOMBARDEIRO STEALTH. | NEM UM VADA ME OS OUTOS INSTRUMENTOS DE TETERAM UM BAMBEDER OSTAU |
+| PEDIR DINHEIRO EMPRESTADO ÀS PESSOAS DA ALDEIA | PEDIAR DINHEIRO EMPRESTADO DÀS PESSOAS DA ALDEIA |
+| PEDIR DINHEIRO EMPRESTADO ÀS PESSOAS DA ALDEIA | PEDIAR DINHEIRO EMPRESTADO DÀS PESSOAS DA ALDEIA |
+| OITO | OITO |
+| TRANCÁ-LOS | TRAM CALDOS |
+| REALIZAR UMA INVESTIGAÇÃO PARA RESOLVER O PROBLEMA | REALIZARAMA INVESTIGAÇÃO PARA RESOLVER O PROBLEMA |
+
 ## Evaluation
 
 The model can be evaluated as follows on the Portuguese test data of Common Voice.
@@ -90,7 +103,7 @@ LANG_ID = "pt"
 MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-portuguese"
 DEVICE = "cuda"
 
-CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", "-", ";", ":", '""', "%", "'", '"', "�", "ʿ", "·", "჻", "~", "՞", 
+CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞", 
                    "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
                    "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。"]
 
@@ -134,6 +147,5 @@ print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"],
 
 **Test Result**:
 
-WER: 12.18%
-
-CER: 11.01%
+- WER: 12.51%
+- CER: 13.59%