Instructions to use kimbochen/whisper-tiny-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kimbochen/whisper-tiny-ja with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kimbochen/whisper-tiny-ja")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kimbochen/whisper-tiny-ja") model = AutoModelForSpeechSeq2Seq.from_pretrained("kimbochen/whisper-tiny-ja", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 034ca23fa94caf5c38942391d37b63c661cdd5d2c035467174d364256df805ea
- Size of remote file:
- 151 MB
- SHA256:
- c3dc6e7522aab9091f53c9ee720e713ded215f9b18dde84b98237293bc4cfd5e
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