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")# pip install -U transformers accelerate # 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
Download training_args.bin from UMUTeam/w2v-bert-emotion-en: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/UMUTeam/w2v-bert-emotion-en/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://UMUTeam/w2v-bert-emotion-en@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/UMUTeam/w2v-bert-emotion-en/resolve/refs%2Fpr%2F1/training_args.bin
5.84 kB
- Xet hash:
- 35d0c1527e4d84b5d779d3d51d58289b1813287994c7fc6240f20b17f1749086
- Size of remote file:
- 5.84 kB
- SHA256:
- 90b71c1f7027e82025d33e01f747d0f23dd99350889de344bd661adfc4dd4287
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