Audio Classification
Transformers
Safetensors
Spanish
wav2vec2-bert
emotion-recognition
speech-emotion-recognition
speech-processing
spanish
affective-computing
umuteam
Eval Results (legacy)
Instructions to use UMUTeam/w2v-bert-emotion-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMUTeam/w2v-bert-emotion-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="UMUTeam/w2v-bert-emotion-es")# Load model directly from transformers import AutoProcessor, CustomAudioClassification processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-emotion-es") model = CustomAudioClassification.from_pretrained("UMUTeam/w2v-bert-emotion-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 988693495e9ad9cc391bc35e4bc3d0ecca37fa7cf893e897a45b478590688a70
- Size of remote file:
- 2.33 GB
- SHA256:
- 97a919d65a2719dfd64007f47cba6e3f91307ea42fcbb14d84a6bf90db348c93
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