Instructions to use rosyvs/whisat-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rosyvs/whisat-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rosyvs/whisat-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rosyvs/whisat-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("rosyvs/whisat-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Create README.md
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add save_path direclty to yaml same as sb example
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add a dummy valid model_save path
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typo
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add save_path to yaml
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remove cache_dir from yaml'
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first commit - version 202_base-en_v1 model run. trained without augmentation
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initial commit
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