Instructions to use nyralabs/CrisperWhisper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nyralabs/CrisperWhisper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nyralabs/CrisperWhisper")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("nyralabs/CrisperWhisper") model = AutoModelForSpeechSeq2Seq.from_pretrained("nyralabs/CrisperWhisper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f61338de99bec71df1e6a5d261582f42e5f69eea5060e712d53ef8fa335d9db2
- Size of remote file:
- 3.22 GB
- SHA256:
- ab370a321fa94ce08d419959813540a4f9b0239b88b2f4207ae28ef6607083c0
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