Instructions to use mabahboh/wav2vec2-ser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mabahboh/wav2vec2-ser with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mabahboh/wav2vec2-ser")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("mabahboh/wav2vec2-ser") model = AutoModelForAudioClassification.from_pretrained("mabahboh/wav2vec2-ser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1114e274bfad9184a6a9c315996242403112eb01ab36aa1172fe33e7179d09e7
- Size of remote file:
- 5.27 kB
- SHA256:
- 1090e6603177a9bb7746b3a394d19da5a6154324cd9d4377e1d461b393287895
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