Hubert Large for Emotion Recognition Model description This is a ported version of S3PRL's Hubert for the SUPERB Emotion Recognition task. The base model is hubert large ll60k, which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to SUPERB: Speech processing Universal PERformance Benchmark Task and dataset description Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalanced emotion classes to leave the final four classes with a similar amount of data points and cross validate on five folds of the standard splits. For the original model's training and evaluation instructions refer to the S3PRL downstream task README. Usage examples You can use the model via the Audio Classification pipeline: Or use the model directly: Eval results The evaluation metric is accuracy. s3prl transformers session1 0.6762 N/A BibTeX entry and citation info
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