Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
mozilla-foundation/common_voice_7_0
Generated from Trainer
Instructions to use jcmc/wav2vec2-large-xlsr-53-ir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcmc/wav2vec2-large-xlsr-53-ir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jcmc/wav2vec2-large-xlsr-53-ir")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jcmc/wav2vec2-large-xlsr-53-ir") model = AutoModelForCTC.from_pretrained("jcmc/wav2vec2-large-xlsr-53-ir", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7933ea9bdaedada5d4b3eb33834636fcc12f35b0342d26940770471f3583941
- Size of remote file:
- 1.26 GB
- SHA256:
- b47d3e78fde38ad751e57fe17fa7a09a47fd46fdb32aa8d942b9be80e7578f6b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.