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
wav2vec2-large-xls-r-300m-hi-wx1 / runs /Feb07_13-35-23_a52265f50712 /1644241056.0324395 /events.out.tfevents.1644241056.a52265f50712.97.1
- Xet hash:
- e0da8d8d8e4d3fdfc5379769c39c4d2957d72c8315e1cb2a131a852234f9ecd2
- Size of remote file:
- 4.81 kB
- SHA256:
- 135231d71f82e474e96012f1050f3ad0256abd5aeb9a3d39a29d6099a36a823a
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