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:
- f57202c5938fe9527ec48113301112473ff549fa37232b7f98e3255c1013109d
- Size of remote file:
- 2.49 GB
- SHA256:
- 464b22be2484e5e9119571f048e14bb09df05df1d01aa57814186b9203d56c30
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