Audio Classification
Transformers
Safetensors
Spanish
wav2vec2-bert
emotion-recognition
speech-emotion-recognition
speech-processing
spanish
affective-computing
umuteam
Eval Results (legacy)
Instructions to use UMUTeam/w2v-bert-emotion-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMUTeam/w2v-bert-emotion-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="UMUTeam/w2v-bert-emotion-es")# Load model directly from transformers import AutoProcessor, CustomAudioClassification processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-emotion-es") model = CustomAudioClassification.from_pretrained("UMUTeam/w2v-bert-emotion-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bafdcbed746dd6c6fc3447c6298e928077fbd9b1ae6fc18799f3009629ac65e
- Size of remote file:
- 4.86 kB
- SHA256:
- e68a9c8cb7b5ce540ab18cf8957fdd9bf3866c2884e28d6465577802c7d9f478
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.