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:
- 6f4dd84f9e29ecfde19c78cf68431c948f41e71e490061faf975cca28fb61c67
- Size of remote file:
- 2.5 GB
- SHA256:
- 9953c8ad209c69ffdd94df800901213dc20c3fa8c639d126bdf1dec62109c0ee
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