Instructions to use chrisjay/afrospeech-wav2vec-sna with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chrisjay/afrospeech-wav2vec-sna with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="chrisjay/afrospeech-wav2vec-sna")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("chrisjay/afrospeech-wav2vec-sna") model = AutoModelForAudioClassification.from_pretrained("chrisjay/afrospeech-wav2vec-sna", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c907b1f935f837d2ff5de5993922fde65f50fe516c859beb0212d5877cd9c081
- Size of remote file:
- 378 MB
- SHA256:
- 194883f5dd1b856edfc4467c0e7a8cfa1f59663217259deb9562301b488ee751
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.