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:
- b9f7f4d489f055f1fcb20a8876f02a8600f434bf6fff7e3e242d7f09f01fb757
- Size of remote file:
- 95.9 MB
- SHA256:
- 6dfe3189b9404e4429422dc6d24f6e84c7a87369f53c917c0bb7caf39428465c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.