Instructions to use codeceejay/HIYACCENT_Wav2Vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codeceejay/HIYACCENT_Wav2Vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="codeceejay/HIYACCENT_Wav2Vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("codeceejay/HIYACCENT_Wav2Vec2") model = AutoModelForCTC.from_pretrained("codeceejay/HIYACCENT_Wav2Vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6754beb9d2b52a6db807d015a0c1478a2fa89aa1d25ad1e3fc29aff3deea1feb
- Size of remote file:
- 2.8 kB
- SHA256:
- b1b07d1dd4cc6493def1bd7e8913100e68f098e1ae5794305fd1ea27af2e811b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.