Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hindi
wav2vec2
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5abc19683b891cb8e9bf41162d815a5101e8d5a518f7017c9fcb89b33ee064f6
- Size of remote file:
- 1.26 GB
- SHA256:
- 17928788cfbb80147bf830dbf2501cf525f38487e8995d6421d01745cc13128e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.