Instructions to use esb/wav2vec2-ctc-librispeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esb/wav2vec2-ctc-librispeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esb/wav2vec2-ctc-librispeech")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esb/wav2vec2-ctc-librispeech") model = AutoModelForCTC.from_pretrained("esb/wav2vec2-ctc-librispeech") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5711c90ce2b3c15e8e53407dfdeb17cda4d9b42ff53206ebffc956fe16e5194e
- Size of remote file:
- 1.26 GB
- SHA256:
- 0d3ed0c5657138e0d9d941c1ad487dd2df01d72585b747b838519ce209c929b4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.