Instructions to use fahdmirzac/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fahdmirzac/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fahdmirzac/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("fahdmirzac/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("fahdmirzac/whisper-small-dv") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48ddf6055b06da379cb58985d64306f52aecd1977579fd3090a3147784de5014
- Size of remote file:
- 5.24 kB
- SHA256:
- 46ee931378908434f8be94c2903855cc438198c60e4d6c50c6b93130b2d9b9e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.