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:
- 3493e30d1f21c831dbd4bd29c2c6691d5cb9e8848b6ea8bf63f57674792ba715
- Size of remote file:
- 2.99 kB
- SHA256:
- c5b9e6e201681bde5431952836c131c17b4e39f06fcb1863ffc4ee379189b289
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