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-concat-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-concat-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-concat-emotion-en")# Load model directly from transformers import AutoProcessor, CustomAudioClassificationConcat processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-beto-concat-emotion-en") model = CustomAudioClassificationConcat.from_pretrained("UMUTeam/w2v-bert-beto-concat-emotion-en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ef7b528695145aa8a332b8a706b4d4d3d37499482473a8c6886c58eac6f1b32
- Size of remote file:
- 5.84 kB
- SHA256:
- eabead3ef27c25485ad830344dcca2813605db9dba72ed4c0a78cbd2a17452d9
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