Instructions to use firdhokk/speech-emotion-recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use firdhokk/speech-emotion-recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="firdhokk/speech-emotion-recognition")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("firdhokk/speech-emotion-recognition") model = AutoModelForAudioClassification.from_pretrained("firdhokk/speech-emotion-recognition") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7abdf7b183970810484b619606d209dad346000a2c8249ce78b1845d461264ba
- Size of remote file:
- 4.6 kB
- SHA256:
- 58ddb3581d72f6a7976d1e3b91317e9dc17dac54827400cc3edb27b110f03e0e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.