Instructions to use alphaedge-ai/whisper-medium-swe-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/whisper-medium-swe-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-swe-16384")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alphaedge-ai/whisper-medium-swe-16384") model = AutoModelForSpeechSeq2Seq.from_pretrained("alphaedge-ai/whisper-medium-swe-16384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b25c5e7768178ec00917dcd05e3e1e600966153014dc93a175a473d1f8bd70ce
- Size of remote file:
- 1.46 GB
- SHA256:
- cf06945758777a9f0b4fbf41566ef43bae02da7cae1f6ec787fcc32d8e703fea
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