Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
hf-asr-leaderboard
whisper-event
Eval Results (legacy)
Instructions to use softcatala/whisper-base-ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use softcatala/whisper-base-ca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="softcatala/whisper-base-ca")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("softcatala/whisper-base-ca") model = AutoModelForSpeechSeq2Seq.from_pretrained("softcatala/whisper-base-ca", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc226622aa2b150540d8930e7e71d0eb6d4810ebb40b969a411cc6f4ae9d7030
- Size of remote file:
- 3.5 kB
- SHA256:
- 1cdcacca71096b407d5e5c278dec9dd90cce2b454621ab22ec9e9a46fa747ec3
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