Instructions to use devkyle/whisper-tiny-dp-wd-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/whisper-tiny-dp-wd-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/whisper-tiny-dp-wd-v2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/whisper-tiny-dp-wd-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/whisper-tiny-dp-wd-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95a024c579e03747fba389568ca68e733353c1f30f0fd14834d98d89e063dbf4
- Size of remote file:
- 151 MB
- SHA256:
- 2098d92c18b5cee98d09315ded251fba4f3193a90b61f3d883d087013deac9df
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.