Audio Classification
Transformers
PyTorch
Safetensors
English
ser
wavlm
msp-podcast
emotion-recognition
audio
speech
valence
arousal
dominance
lucas
speech-emotion-recognition
custom_code
Instructions to use 3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes", trust_remote_code=True)# Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
metrics (concordance correlation coeff) or which team?
#1
by makrai - opened
Dear Ali,
thanks for publishing the model. Could you please tell us what metrics this model achieves? Were you one of the participants? Could you please tell me your Team Name?
Thanks
Márton Makrai
from the AstroSpeech project
3loi changed discussion status to closed
3loi changed discussion status to open
Thanks!
3loi changed discussion status to closed