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
Download pytorch_model.bin from zuazo/whisper-large-v2-eu: direct link, hf CLI and curl.
- Browser
- Download file 6.17 GB
-
https://huggingface.co/zuazo/whisper-large-v2-eu/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://zuazo/whisper-large-v2-eu/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/zuazo/whisper-large-v2-eu/resolve/main/pytorch_model.bin
6.17 GB
- Xet hash:
- 1b37f187335c3a35b7d8766f77c1e8d908654f58ab8b46235277c841520cc796
- Size of remote file:
- 6.17 GB
- SHA256:
- 72e141a3a15cd7f737d0a7109aab4f34d0cde1b14e67be53c62a5de6af6c17dd
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