Instructions to use seastar105/whisper-small-komixv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seastar105/whisper-small-komixv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="seastar105/whisper-small-komixv2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("seastar105/whisper-small-komixv2") model = AutoModelForSpeechSeq2Seq.from_pretrained("seastar105/whisper-small-komixv2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 464e75b6486e227f7085b78e21a9ee495f9ab51e6b7a24a7307b73f1aea4a27b
- Size of remote file:
- 967 MB
- SHA256:
- f92f5be3b5df1974e4290d58eeec7d1963ce053948bb63b077fae807a9f0ed27
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.