Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hindi
wav2vec2
robust-speech-event
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2 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-cv8-b2 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-cv8-b2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad20475cf848d3179577f097fdbc7360092f8f13625f67d136be384b65a75b8a
- Size of remote file:
- 1.26 GB
- SHA256:
- 0cbbbc6d9525cf438697ea93143b0de86e8b76cb6b18c56720cab86d8933cccb
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