Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7c970c16f1880f8d40a6235da2f6a0ef86b3db1fa51006f187a7efbfab35110
- Size of remote file:
- 1.26 GB
- SHA256:
- 89b7d8c61714ecc8fb263ca2731e413cd5c2c4d1550eb9c3b517b2c22858c584
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