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:
- 053054ff18912e539ef595b05ce1af3f571f9981894389a2af9fdca78b96b711
- Size of remote file:
- 967 MB
- SHA256:
- cf4d80150803c6322e25f921de127840dc9931531498032dd2075daef7ed9fcf
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