Instructions to use alphaedge-ai/whisper-medium-jav-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/whisper-medium-jav-16384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alphaedge-ai/whisper-medium-jav-16384")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alphaedge-ai/whisper-medium-jav-16384") model = AutoModelForSpeechSeq2Seq.from_pretrained("alphaedge-ai/whisper-medium-jav-16384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f988810d22f13d6d79934a4b6dcb7cd52a1ac659d5cc139d8be738e3b14882e5
- Size of remote file:
- 1.46 GB
- SHA256:
- 7c04ecf8b618f59634a302a1637b3ac206bc06b3702571c50419b113f8ea4b2c
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