Update info.txt

info.txt

@@ -8,5 +8,5 @@ The open-source MESD dataset was used to fine-tune the Wav2Vec2 base model, whic
 The aforementioned prototype may not function well in noisy environments or audio with a musical/noisy background due to the models being trained on too little data due to sparse availability. In order to make our model robust, our future work includes:
   --- Accumulation  of more audio data which closely resembles the aural environment in which the app will be used/tested.
   --- The F1 score for the "Fear" class is 47.5%. We aim to do targeted improvement for the class “Fear” which the model often misclassifies.
-  --- We tried to finetune Wav2Vec2-xls-r Spanish checkpoints on MESD dataset, ran several tests on different wav2vec2-xls-r Spanish checkpoints and were surprised to find that they performed worse than the Wav2Vec2-base model fine-tuned on MESD. To recheck and establish whether the prosodies critical for audio sentiment classification tasks are lost during the finetuning of the model for ASR purposes, an in-depth study and root cause analysis need to be done.
+  --- We tried to finetune Wav2Vec2-XLS-R Spanish checkpoints on MESD dataset, ran several tests on different Wav2Vec2-XLS-R Spanish checkpoints and were surprised to find that they performed worse than the Wav2Vec2-base model fine-tuned on MESD. To recheck and establish whether the prosodies critical for audio sentiment classification tasks are lost during the finetuning of the model for ASR purposes, an in-depth study and root cause analysis need to be done.
 1. Drishti Sharma    2. Manuel Fernandez Moya    3. Antonio Alberto Soto Hernández    4. Jefferson Quispe Pinares    5. Matias Gaona