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
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README.md
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- Loss: 0.2511
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- Wer: 0.2415
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####Evaluation Commands
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1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
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- Loss: 0.2511
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- Wer: 0.2415
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#### Evaluation Commands
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1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
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