Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
hf-asr-leaderboard
whisper-event
Eval Results (legacy)
Instructions to use softcatala/whisper-medium-ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use softcatala/whisper-medium-ca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="softcatala/whisper-medium-ca")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("softcatala/whisper-medium-ca") model = AutoModelForSpeechSeq2Seq.from_pretrained("softcatala/whisper-medium-ca") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31de33db35fa7ce6c5305e454a03e0ecbe7737a1ed4c3c8f557bd4000884991c
- Size of remote file:
- 3.5 kB
- SHA256:
- 49bb781f884747688fc9143eb0b4d4d9328e356e1837da7ca089977913f317b2
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