Instructions to use KBLab/kb-whisper-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/kb-whisper-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KBLab/kb-whisper-medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KBLab/kb-whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("KBLab/kb-whisper-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f2472b35295a87d190e7d0ab54561781e9f7bf1da83cf520991c855d18a09a2
- Size of remote file:
- 1.53 GB
- SHA256:
- 4a4a32952026bcfa0bcfaa76b0b006f232ffba6a8f5bccbcb12e4a1153a40494
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