Instructions to use mskov/whisper-small-miso with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mskov/whisper-small-miso with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mskov/whisper-small-miso")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mskov/whisper-small-miso") model = AutoModelForSpeechSeq2Seq.from_pretrained("mskov/whisper-small-miso") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d6219589f0e6ebf64680a7415666985a2b65ccc3895c40ef53cbab93858e549
- Size of remote file:
- 967 MB
- SHA256:
- 0f32d7b0e6c1227d8c2f787c05b0c873f2e4d72e65115ede58ab1fabd04b2ee9
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