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:
- 930ebc35ce5fa15d406da30ef8dd96e9bfce90eeaf0044e99d4d31c80a01586d
- Size of remote file:
- 290 MB
- SHA256:
- 7f437f61f4bceac6b0941879b65ba55e39c220355f39da029ae4d305e9510a25
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