Automatic Speech Recognition
Transformers
PyTorch
JAX
Basque
wav2vec2
audio
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use cahya/wav2vec2-large-xlsr-basque with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cahya/wav2vec2-large-xlsr-basque with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cahya/wav2vec2-large-xlsr-basque")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("cahya/wav2vec2-large-xlsr-basque") model = AutoModelForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-basque") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 61a4355ccd1b1ef1117eb2c9f233343ac0b01080b5c62eec0b6412b6a16afc5b
- Size of remote file:
- 1.26 GB
- SHA256:
- 009c77c9f2807faae58ba815cb07ac1a03ee44ba3815e683c4c3ad035c1ef575
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