Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Spanish
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use arpagon/whisper-large-v2-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arpagon/whisper-large-v2-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arpagon/whisper-large-v2-es")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("arpagon/whisper-large-v2-es") model = AutoModelForSpeechSeq2Seq.from_pretrained("arpagon/whisper-large-v2-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from arpagon/whisper-large-v2-es: direct link, hf CLI and curl.
- Browser
- Download file 6.17 GB
-
https://huggingface.co/arpagon/whisper-large-v2-es/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://arpagon/whisper-large-v2-es/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/arpagon/whisper-large-v2-es/resolve/main/pytorch_model.bin
6.17 GB
- Xet hash:
- 29f09446442743e9f47b1ae45e39715f1d16eb5d438cbeb6baed2b7e8fde47f4
- Size of remote file:
- 6.17 GB
- SHA256:
- 76b2b07f4ae77c7d9fa349f33f197aaccb9cb614ee3d4f229bed882c0ff08982
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