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:
- 81cf4242a273b0a26a29d3db913bce1eb91e4d72b544adc3f9dfbd2e684973d0
- Size of remote file:
- 2.5 GB
- SHA256:
- a8ecc79b2d467562d29fece360ab299e3cbb5f1215f2ba9217d576b0b1e6b575
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