Audio Classification
Transformers
Safetensors
Spanish
wav2vec2-bert
emotion-recognition
speech-emotion-recognition
multimodal-learning
speech-processing
text-processing
spanish
affective-computing
umuteam
Eval Results (legacy)
Instructions to use UMUTeam/w2v-bert-beto-multihead-emotion-es 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-es 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-es")# Load model directly from transformers import AutoProcessor, CustomAudioClassificationAttn processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-beto-multihead-emotion-es") model = CustomAudioClassificationAttn.from_pretrained("UMUTeam/w2v-bert-beto-multihead-emotion-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32a22c7491350579cbae1d6dc0793add1ebd441513cf6e382c054c3755d09293
- Size of remote file:
- 2.5 GB
- SHA256:
- 2a582f76f1871dbca98ede3609cb5813fb42f9741b24dcb7159abe25473b4961
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