Instructions to use hajar817/whisper_tiny_fa_cv13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hajar817/whisper_tiny_fa_cv13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hajar817/whisper_tiny_fa_cv13")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hajar817/whisper_tiny_fa_cv13") model = AutoModelForSpeechSeq2Seq.from_pretrained("hajar817/whisper_tiny_fa_cv13") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f44eebcc3f7e3fb64f33a0682ad898427b46249a271ae94fcc98f89262d977ff
- Size of remote file:
- 5.11 kB
- SHA256:
- 22c97199bff72c33ef21f10824eac6ea9c1460399a7d815ca784e11a7a0b6bcc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.