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-multihead-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-multihead-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-multihead-emotion-en")# Load model directly from transformers import AutoProcessor, CustomAudioClassificationAttn processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-beto-multihead-emotion-en") model = CustomAudioClassificationAttn.from_pretrained("UMUTeam/w2v-bert-beto-multihead-emotion-en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2663df7474007a165eb535e2e8368c363d523e160fe09ade27edd529d245099
- Size of remote file:
- 5.84 kB
- SHA256:
- 35368d7b5094e5aef0c50cec7f35a204ec7cb0c173fc516b7e943f86ca580f6c
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