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
whisper-medium-ca / openai /33ea9a6ef046879ee1adf42aff012a6e02b3c1a4731634b4c8a1a3ee6e3b1a3c /softcatala-medium-openai-format.pt
- Xet hash:
- 3ed40dbe38f4bbddce596d5c21b52788a3bc2541ff2ae7c6ef2ed9d6ea2ce02e
- Size of remote file:
- 3.06 GB
- SHA256:
- 33ea9a6ef046879ee1adf42aff012a6e02b3c1a4731634b4c8a1a3ee6e3b1a3c
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