Audio Classification
Transformers
Safetensors
English
wav2vec2-bert
emotion-recognition
speech-emotion-recognition
speech-processing
english
affective-computing
umuteam
Eval Results (legacy)
Instructions to use UMUTeam/w2v-bert-emotion-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMUTeam/w2v-bert-emotion-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="UMUTeam/w2v-bert-emotion-en")# Load model directly from transformers import AutoProcessor, CustomAudioClassification processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-emotion-en") model = CustomAudioClassification.from_pretrained("UMUTeam/w2v-bert-emotion-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1313a3402313bded18e739c519616e23094fb3231ca80e9782648dcefab1daf0
- Size of remote file:
- 2.33 GB
- SHA256:
- e5bd52c3614d0681bfd489b0d7ab52383027ef4b52ddb5791fe231746c52f771
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