Demostración de Gradio para la clasificación de sentimientos de audios usando 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.
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.
The open-source MESD dataset was used to fine-tune the Wav2Vec2 base model, which contains ~1200 audio recordings, all of which were recorded in professional studios and were only one second long. Out of ~1200 audio recordings only 890 of the recordings were utilized for training. Due to these factors, the model and hence this Gradio application may not be able to perform well in noisy environments or audio with background music or noise. It's also worth mentioning that this model performs poorly when it comes to audio recordings from the class "Fear," which the model often misclassifies.
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