Automatic Speech Recognition
Transformers
PyTorch
English
wav2vec2
audio
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use jonatasgrosman/wav2vec2-large-english with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonatasgrosman/wav2vec2-large-english with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-english")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jonatasgrosman/wav2vec2-large-english") model = AutoModelForCTC.from_pretrained("jonatasgrosman/wav2vec2-large-english") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19906e39425146b1a8d66c21f17b0fdcc31bc9b173e37f4aaad9436e51fb8e02
- Size of remote file:
- 1.26 GB
- SHA256:
- ef32d54a3ebc911d64e7a8b8d04896f0957bbab4e3da4c6c0ae42ab901d6e4e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.