Instructions to use esb/wav2vec2-aed-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esb/wav2vec2-aed-pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esb/wav2vec2-aed-pretrained")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esb/wav2vec2-aed-pretrained") model = AutoModelForSpeechSeq2Seq.from_pretrained("esb/wav2vec2-aed-pretrained") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b06f010b6eea4f6630bd3c92312af8cb92f081ffe6250ae4eef097e37f891ffd
- Size of remote file:
- 2.35 GB
- SHA256:
- 426a0c8b8f095282de9d9080d52d8d2bf0df5ff4d549023dc9583079af514fd7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.