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:
- 2fa0baa576bc2a1cab71cc846eada076fd30bd14ec68f93acf86054d73cd066f
- Size of remote file:
- 4.86 kB
- SHA256:
- 6c8075a6be71df1c23f3604cf751a8375979244eb9940a1f26614768330e4d9e
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