Instructions to use MIbrahimAI/Wav2Vec-SER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MIbrahimAI/Wav2Vec-SER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="MIbrahimAI/Wav2Vec-SER")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("MIbrahimAI/Wav2Vec-SER") model = AutoModelForAudioClassification.from_pretrained("MIbrahimAI/Wav2Vec-SER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d54e97c5fa583b213844a6b5f0a5092b3e3a6bfcc385b270bb2f9d92311442b
- Size of remote file:
- 1.47 kB
- SHA256:
- cb2f5d169f2dc2430f9ac7c7658fe011c5ec7f293ab67e7fcc701ff3e06e4aae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.