Instructions to use bezzam/parakeet-ctc-1.1b-hf-module-list-ParakeetEncoderConvModule with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bezzam/parakeet-ctc-1.1b-hf-module-list-ParakeetEncoderConvModule with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bezzam/parakeet-ctc-1.1b-hf-module-list-ParakeetEncoderConvModule")# Load model directly from transformers import AutoModelForCTC model = AutoModelForCTC.from_pretrained("bezzam/parakeet-ctc-1.1b-hf-module-list-ParakeetEncoderConvModule", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 58bfbb2cda3dd392df3f628922c438906a0cc40f5a0c517bedaf6f81cf0371ee
- Size of remote file:
- 4.25 GB
- SHA256:
- b1a87d1dc8e2467afb702688a018c5a5a7ff14f84975ba5ca11453832f7afd83
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.