Instructions to use chrisjay/afrospeech-wav2vec-run with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chrisjay/afrospeech-wav2vec-run with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="chrisjay/afrospeech-wav2vec-run")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("chrisjay/afrospeech-wav2vec-run") model = AutoModelForAudioClassification.from_pretrained("chrisjay/afrospeech-wav2vec-run", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 473ef0ca4fd1139ff1862e3a1b4361e33e1924953dba1381fb856a20f30ac435
- Size of remote file:
- 378 MB
- SHA256:
- 90826ad0529017f83593430cbd1e0e593d9314cd5d43f895d1f6cde169970fae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.