Instructions to use MrFitzmaurice/finetune-whisper-test-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MrFitzmaurice/finetune-whisper-test-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MrFitzmaurice/finetune-whisper-test-2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("MrFitzmaurice/finetune-whisper-test-2") model = AutoModelForSpeechSeq2Seq.from_pretrained("MrFitzmaurice/finetune-whisper-test-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 264803435c8b18b57eb9b148316c8431ef7a827524b00ca6d90f20119cf1a907
- Size of remote file:
- 3.06 GB
- SHA256:
- 393c4f43623a0c43c497be44987a80fe7edaf20a22e4133279752743153b8ecf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.