Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Basque
wav2vec2
basque
Generated from Trainer
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use deepdml/wav2vec2-large-xls-r-300m-basque with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepdml/wav2vec2-large-xls-r-300m-basque with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="deepdml/wav2vec2-large-xls-r-300m-basque")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("deepdml/wav2vec2-large-xls-r-300m-basque") model = AutoModelForCTC.from_pretrained("deepdml/wav2vec2-large-xls-r-300m-basque", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7f5c8e11b4229d34f4f89fc5560c1a9c362f87a64645973562998830a8e5454
- Size of remote file:
- 3.06 kB
- SHA256:
- 28433fca4b36659e8d892619688c32cea4ca82bd218e2c7096fb4bebfc582314
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