Update

README.md

@@ -19,9 +19,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Whisper Large V3 Turbo - Spanish
 
-This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 17.0 dataset.
+This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 17.0 dataset - spanish subset.
 
-The fine-tuning process reduced the Word Error Rate (WER) from 10.18% to 2.69%, emonstrating significant improvement in transcription accuracy for spanish audios.
+The fine-tuning process reduced the Word Error Rate (WER) from 6.91% to 5.34%, demonstrating significant improvement in transcription accuracy for spanish audios.
 
 ## Model description
 
@@ -35,8 +35,8 @@ More information needed
 
 The model was trained using the Common Voice 17.0 dataset - spanish subset (mozilla-foundation/common_voice_17_0). Both the base model, whisper-large-v3-turbo, and the fine-tuned model, whisper-large-v3-turbo-es, were evaluated using Word Error Rate (WER) on the test split of the same dataset. The results are as follows:
 
-- WER for whisper-large-v3-turbo (base): 10.18%
-- WER for whisper-large-v3-turbo-es (fine-tuned): 2.69%
+- WER for whisper-large-v3-turbo (base): 6.91%
+- WER for whisper-large-v3-turbo-es (fine-tuned): 5.34%
 
 This significant reduction in WER shows that fine-tuning the model for spanish audio led to improved transcription accuracy compared to the original base model.