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-concat-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-concat-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-concat-emotion-es")# Load model directly from transformers import AutoProcessor, CustomAudioClassificationConcat processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-beto-concat-emotion-es") model = CustomAudioClassificationConcat.from_pretrained("UMUTeam/w2v-bert-beto-concat-emotion-es") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b21caf4052bd6100c8f79b43a2b873bdee138571220d5d486a6079713ea9b9c
- Size of remote file:
- 2.33 GB
- SHA256:
- d35c264f3fbab18d65e92bca78a515bc060613c9eaee33fb2c8666137af35595
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