Audio Classification
Transformers
Safetensors
English
wav2vec2-bert
emotion-recognition
speech-emotion-recognition
multimodal-learning
speech-processing
text-processing
english
affective-computing
umuteam
Eval Results (legacy)
Instructions to use UMUTeam/w2v-bert-beto-concat-emotion-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMUTeam/w2v-bert-beto-concat-emotion-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="UMUTeam/w2v-bert-beto-concat-emotion-en")# Load model directly from transformers import AutoProcessor, CustomAudioClassificationConcat processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-beto-concat-emotion-en") model = CustomAudioClassificationConcat.from_pretrained("UMUTeam/w2v-bert-beto-concat-emotion-en") - Notebooks
- Google Colab
- Kaggle
File size: 129 Bytes
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