Instructions to use rasgaard/whisper-tiny.da with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rasgaard/whisper-tiny.da with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rasgaard/whisper-tiny.da")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rasgaard/whisper-tiny.da") model = AutoModelForSpeechSeq2Seq.from_pretrained("rasgaard/whisper-tiny.da", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4fee4740138dfb2df2f0d95ce0e7065f63a5ed8b0ab5d6c19ef943515deb205d
- Size of remote file:
- 5.43 kB
- SHA256:
- 0ca5cf3400e87a72d8cce0371b6ee9c2fdf93de8542a6e425e651a99325f09d7
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