Instructions to use Sunbird/asr-wav2vec2-lug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sunbird/asr-wav2vec2-lug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sunbird/asr-wav2vec2-lug")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sunbird/asr-wav2vec2-lug") model = AutoModelForCTC.from_pretrained("Sunbird/asr-wav2vec2-lug", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8275967710da7ab4931faabe1777288cb659a1a88538dc7596cd5b2c8c75862e
- Size of remote file:
- 1.26 GB
- SHA256:
- 2d858aaf611c7b4fec6f144ed02c674bbb7960ed4d4ebab87b5ea1400b76b8bc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.