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-mean-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-mean-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-mean-emotion-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, CustomAudioClassificationMean processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-beto-mean-emotion-en") model = CustomAudioClassificationMean.from_pretrained("UMUTeam/w2v-bert-beto-mean-emotion-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from UMUTeam/w2v-bert-beto-mean-emotion-en: direct link, hf CLI and curl.
- Browser
- Download file 2.33 GB
-
https://huggingface.co/UMUTeam/w2v-bert-beto-mean-emotion-en/resolve/main/model.safetensors
- Command line
-
hf download hf://UMUTeam/w2v-bert-beto-mean-emotion-en/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/UMUTeam/w2v-bert-beto-mean-emotion-en/resolve/main/model.safetensors
2.33 GB
- Xet hash:
- db62c9d71e1fbe3e73db83dd2ad9e5348efb7bfaa9fbc23af435e0a6a40ecdf9
- Size of remote file:
- 2.33 GB
- SHA256:
- f904b60f74bef2731183421a2f1b0e85602c9de3ac0b6d7421fd65f6790ee5c7
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