Instructions to use chrisjay/afrospeech-wav2vec-yor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chrisjay/afrospeech-wav2vec-yor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="chrisjay/afrospeech-wav2vec-yor")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("chrisjay/afrospeech-wav2vec-yor") model = AutoModelForAudioClassification.from_pretrained("chrisjay/afrospeech-wav2vec-yor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from chrisjay/afrospeech-wav2vec-yor: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/chrisjay/afrospeech-wav2vec-yor/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://chrisjay/afrospeech-wav2vec-yor/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/chrisjay/afrospeech-wav2vec-yor/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 87ff4a13183ac3a55442bec82a68ea948282eabfdbea4c5c57fa19ef735b82f0
- Size of remote file:
- 378 MB
- SHA256:
- 8aea55a3aaf0464640160b78321a6b85319f323aa11635059832c49f6637d2a5
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