Instructions to use akera/whisper-small-lg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akera/whisper-small-lg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="akera/whisper-small-lg")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("akera/whisper-small-lg") model = AutoModelForSpeechSeq2Seq.from_pretrained("akera/whisper-small-lg") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7d1ec053c43823e89c2e85ca2f79d3dd808bc92e84a42f8e39abb10233745bb
- Size of remote file:
- 967 MB
- SHA256:
- 657fdbba3f4979cc01eaf0317a0f8d043333b69ce47638734851f417b6412308
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