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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from deepdml/wav2vec2-large-xls-r-300m-basque: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/deepdml/wav2vec2-large-xls-r-300m-basque/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://deepdml/wav2vec2-large-xls-r-300m-basque/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deepdml/wav2vec2-large-xls-r-300m-basque/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- cfc6fd9aea40d04b4edc210871a7db843ccd054a506c03161acbfe41c80146f0
- Size of remote file:
- 1.26 GB
- SHA256:
- 68de92a72e8133debc7e998189074714207033ebf31dc8dc6552a80654679b03
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