Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Upper Sorbian
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
model_for_talk
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3 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-hsb-v3 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-hsb-v3")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ba12db19363bb5f56838e7644b488eff36e8ced53039e0e47f7dcfc6398e541
- Size of remote file:
- 1.26 GB
- SHA256:
- 59bb2bcb9be40814d2598adfd0f2926113359ce18ff1f1964d97e351ecc5c475
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