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
Download training_args.bin from nyralabs/CrisperWhisper: direct link, hf CLI and curl.
- Browser
- Download file 26.6 kB
-
https://huggingface.co/nyralabs/CrisperWhisper/resolve/main/training_args.bin
- Command line
-
hf download hf://nyralabs/CrisperWhisper/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nyralabs/CrisperWhisper/resolve/main/training_args.bin
26.6 kB
- Xet hash:
- e961c5b7b8c33a523283914a015a789ab77cc95fe1166d26c8ed0ff69e3552c7
- Size of remote file:
- 26.6 kB
- SHA256:
- e9b4a87e935a72c4ad19940265a06cfcd003c0f653392960b6d96d369c47ae8d
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