Instructions to use Subhadeep/whisper-tiny-fet-small-model-Hi-Bank_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Subhadeep/whisper-tiny-fet-small-model-Hi-Bank_v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Subhadeep/whisper-tiny-fet-small-model-Hi-Bank_v5")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Subhadeep/whisper-tiny-fet-small-model-Hi-Bank_v5") model = AutoModelForSpeechSeq2Seq.from_pretrained("Subhadeep/whisper-tiny-fet-small-model-Hi-Bank_v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7728156c6514af69b923e21ce672ae479b8c17e078f8f8b823067a77d015b47
- Size of remote file:
- 967 MB
- SHA256:
- b04838bb19142484652ede657b48c84a11605cd082147fb592af0100db040f87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.