A fine-tuned wav2vec2 model trained on the MSP-Podcast corpus for dimensional emotion recognition. It classifies audio into emotional attributes such as arousal, dominance, and valence. Useful for researchers and developers working on affective computing, call center analytics, or human-computer interaction.
Wav2vec2 Large Robust 12 Ft Emotion Msp Dim is a Speech to text project. Automatically recognizing emotional dimensions from spoken audio recordings. Wav2vec2 Large Robust 12 Ft Emotion Msp Dim is an open-source project aimed at developers. Wav2vec2 Large Robust 12 Ft Emotion Msp Dim is open source under the MIT license. It ships for the web and API.
It is developed by audEERING, and it first shipped in 2022. Development happens publicly on GitHub with 555 stars. Among its 3 catalogued features are Emotion Recognition, Audio Classification, and Speech Analysis. It exposes integrations via a public API.
Summary written by a language model from the project’s public pages.
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