Instructions to use waxal-benchmarking/whisper-tiny-sid-Oreoluwa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waxal-benchmarking/whisper-tiny-sid-Oreoluwa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="waxal-benchmarking/whisper-tiny-sid-Oreoluwa")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("waxal-benchmarking/whisper-tiny-sid-Oreoluwa") model = AutoModelForSpeechSeq2Seq.from_pretrained("waxal-benchmarking/whisper-tiny-sid-Oreoluwa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29de655cb414a8dce3f480b81dd68a893540d08d344525732d570a9bed22b446
- Size of remote file:
- 5.39 kB
- SHA256:
- b9b15141203e36e1103d60d8597d96cdd23915f981a0b009039b011f60080265
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