Instructions to use vonewman/fine_tuning_wave2vec_waxal_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vonewman/fine_tuning_wave2vec_waxal_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="vonewman/fine_tuning_wave2vec_waxal_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("vonewman/fine_tuning_wave2vec_waxal_model") model = AutoModelForAudioClassification.from_pretrained("vonewman/fine_tuning_wave2vec_waxal_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vonewman/fine_tuning_wave2vec_waxal_model: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/vonewman/fine_tuning_wave2vec_waxal_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vonewman/fine_tuning_wave2vec_waxal_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vonewman/fine_tuning_wave2vec_waxal_model/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- ec0eeed6aacf07034cf50ccc68207df6e361e6afbd5886234df6b46f83ea67a6
- Size of remote file:
- 378 MB
- SHA256:
- 33fc408fbed201205cd4c94d5cb56b123294cb96fda62ddfef91c6b6640c5092
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