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 Gradio demo for sentiment classification of Spanish audios using Wav2Vec2
 🔊 Audio Sentiment Classifier
-This is a Gradio demo for classifying the  sentiment of the speech/audio using Wav2Vec2 fine-tuned on Mexican Emotional Speech Database (MESD) dataset. The MESD dataset contains single-word utterances for the emotive prosodies of anger, disgust, fear, happiness, neutrality, and sadness with Mexican culture shaping. In addition, the utterances in MESD dataset have been contributed by both adult and child non-professional actors: 3 female, 2 male, and 6 child voices are available.
+This is a Gradio demo for classifying the  sentiment of the speech/audio using Wav2Vec2 fine-tuned on Mexican Emotional Speech Database (MESD) dataset. The MESD dataset contains single-word utterances for the emotive prosodies of anger, disgust, fear, happiness, neutrality, and sadness with Mexican culture shaping. In addition, the utterances in MESD dataset have been contributed by both adult and child non-professional actors:3 female, 2 male, and 6 child voices are available.
 Targeted SGDs: 1) Good health and well being 2) Peace, Justice and Strong Institutions
 Potential Applications: Although this is a very small prototype, it can be scaled up to detect user's mood or current mental state. The audio libraries or other media in general can be presented, recommended, and categorized based on the recognized user's mood and preference. A mood lighting system, in addition to the aforementioned features, can be implemented to aid in user's health and overall wellbeing. Additionally, the model can be trained on data with more class labels in order to be useful in Peace, Justice scenarios, particularly in detecting brawls, and any other uneventful scenario. An audio classifier can be integrated in a surveillance system to detect brawls and other unsettling events that can be recognized using "sound."
 To begin with, we didn't have enough audio data in Spanish suitable for sentiment classification tasks. We had to make do with whatever data we could find in the MESD database because much of the material we came across was not open-source. Furthermore, in the MESD dataset, augmented versions of audios pre-existed, accounting for up to 25% of the total data, thus we decided not to undertake any more data augmentation to avoid overwhelming the original audio samples.