Instructions to use Musembi/whisper-small-kamba-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Musembi/whisper-small-kamba-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Musembi/whisper-small-kamba-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Musembi/whisper-small-kamba-model") model = AutoModelForSpeechSeq2Seq.from_pretrained("Musembi/whisper-small-kamba-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Musembi/whisper-small-kamba-model: direct link, hf CLI and curl.
- Browser
- Download file 5.39 kB
-
https://huggingface.co/Musembi/whisper-small-kamba-model/resolve/main/training_args.bin
- Command line
-
hf download hf://Musembi/whisper-small-kamba-model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Musembi/whisper-small-kamba-model/resolve/main/training_args.bin
5.39 kB
- Xet hash:
- efc1604eaf4fff1dd8ec16c16ed90d4517473a807a685d91209e8bb286e6d4ac
- Size of remote file:
- 5.39 kB
- SHA256:
- d6da1dcdc31177bdfb5348d0d79552e7f376be8a6d52608724e40ff02a0a72be
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