Instructions to use esb/wav2vec2-ctc-switchboard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esb/wav2vec2-ctc-switchboard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esb/wav2vec2-ctc-switchboard")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esb/wav2vec2-ctc-switchboard") model = AutoModelForCTC.from_pretrained("esb/wav2vec2-ctc-switchboard") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6052190ce1ad18f02907a63b2c8d1c885800d634fb20ea0945506174e1f39422
- Size of remote file:
- 1.26 GB
- SHA256:
- de965e57033b5cf6db40ce4bdb9a83d1ddac8e396e33510b226adf5bd94c1ad4
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