Instructions to use anderloh/FinetunedWav2vec5ClassProblemNew with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anderloh/FinetunedWav2vec5ClassProblemNew with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anderloh/FinetunedWav2vec5ClassProblemNew")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anderloh/FinetunedWav2vec5ClassProblemNew") model = AutoModelForAudioClassification.from_pretrained("anderloh/FinetunedWav2vec5ClassProblemNew") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b154f1a0f1dd5464a965b720a368d13fbfb4cd739c4b9ccbd193980e2b45a5e
- Size of remote file:
- 52.2 MB
- SHA256:
- 51779c90f182773c0eccdebb31d61e1a81936b73024a5badb5719741abedadfe
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