Instructions to use lorenzoncina/whisper-small-ja-jvs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lorenzoncina/whisper-small-ja-jvs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lorenzoncina/whisper-small-ja-jvs")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("lorenzoncina/whisper-small-ja-jvs") model = AutoModelForSpeechSeq2Seq.from_pretrained("lorenzoncina/whisper-small-ja-jvs") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1552e21d87d2a909bad9239af638c3d2a7122940ed56eec36f3915049b63d844
- Size of remote file:
- 967 MB
- SHA256:
- 6a778d6c120d23c7553e5ea091a89c791ccf218e83722b054fc665821a8103f3
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