Instructions to use controngo/whisper-tinyfinacialKI500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use controngo/whisper-tinyfinacialKI500 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="controngo/whisper-tinyfinacialKI500")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("controngo/whisper-tinyfinacialKI500") model = AutoModelForSpeechSeq2Seq.from_pretrained("controngo/whisper-tinyfinacialKI500", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from controngo/whisper-tinyfinacialKI500: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/controngo/whisper-tinyfinacialKI500/resolve/main/training_args.bin
- Command line
-
hf download hf://controngo/whisper-tinyfinacialKI500/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/controngo/whisper-tinyfinacialKI500/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 51cd698dec5a7c50b8d3dd0bcbb511c8362dafecf98f806f7e7d090ef9a681b9
- Size of remote file:
- 5.24 kB
- SHA256:
- ab0b0bb9e9fee918f6f832a3c6a26064ff8c279fe2dbb0a1851730af37469cd7
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