# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("tahiyacy/emotion-recognition")
model = AutoModel.from_pretrained("tahiyacy/emotion-recognition", device_map="auto")Perceiver-based Emotion Recognition
This model is a Perceiver-based (https://huggingface.co/docs/transformers/model_doc/perceiver) emotion recognition model trained on RAVDESS dataset (https://zenodo.org/record/1188976#.Y5iqPy2B1QI). The model is trained using 3 modalities: video, audio, and text.
For details on the data collection, check here: https://zenodo.org/record/1188976
The feature extraction for each modality and training procedure follows the steps mentioned here: https://dl.acm.org/doi/10.1145/3551876.3554806
Intended uses
You can use the raw model for directly recognize emotion (classes: 01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised) or fine-tune on a downstream task.
Limitations
The model is trained on only one dataset and uses 8 specific classes of emotions. The limitation lies in the lack of diversity in the demographics and emotions.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tahiyacy/emotion-recognition")