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:
- c8372e2cfcf1988d65c6696b3a97b023af088ce2e59e02b619baac9282fb7eaa
- Size of remote file:
- 2.33 GB
- SHA256:
- 4f793135a7d7196951ae775d650665e0e925b1781ef68eb80b72e043e00deffa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.