Automatic Speech Recognition
Transformers
PyTorch
Basque
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use zuazo/whisper-large-v2-eu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zuazo/whisper-large-v2-eu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="zuazo/whisper-large-v2-eu")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("zuazo/whisper-large-v2-eu") model = AutoModelForSpeechSeq2Seq.from_pretrained("zuazo/whisper-large-v2-eu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 381fc9b874701ba13d17b53a000815cec489ed213bb15f573fef2ff60eb945cc
- Size of remote file:
- 4.16 kB
- SHA256:
- 6ac10f1575df1f247a86a6c2074853e0089ae34f91a3741b06ad42677e497fd1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.