Instructions to use susmitabhatt/whisper_aii_clp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use susmitabhatt/whisper_aii_clp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="susmitabhatt/whisper_aii_clp")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("susmitabhatt/whisper_aii_clp") model = AutoModelForSpeechSeq2Seq.from_pretrained("susmitabhatt/whisper_aii_clp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from susmitabhatt/whisper_aii_clp: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/susmitabhatt/whisper_aii_clp/resolve/main/training_args.bin
- Command line
-
hf download hf://susmitabhatt/whisper_aii_clp/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/susmitabhatt/whisper_aii_clp/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- c6b6b808ad8834935980f1deb3502b46b3b8745731c0be2f2755a23513984ec0
- Size of remote file:
- 5.5 kB
- SHA256:
- cc5b56da9f21e5b7dd8845c803e98b6798b59a7ea66bb07f483b402852334963
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.