Instructions to use h9LtLSb/whisper-small-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use h9LtLSb/whisper-small-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="h9LtLSb/whisper-small-es")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("h9LtLSb/whisper-small-es") model = AutoModelForSpeechSeq2Seq.from_pretrained("h9LtLSb/whisper-small-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4edb2370b8bba3e7dfd78cade8033a847ceb398f30e407f9fc6c11fcd789472
- Size of remote file:
- 967 MB
- SHA256:
- df0ea07ba4d5770feafcc4232cc944691d6e5e513a5a155997f42556c3d97ab0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.