Instructions to use alphaedge-ai/whisper-medium-rus-32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/whisper-medium-rus-32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alphaedge-ai/whisper-medium-rus-32768")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alphaedge-ai/whisper-medium-rus-32768") model = AutoModelForSpeechSeq2Seq.from_pretrained("alphaedge-ai/whisper-medium-rus-32768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e43a2be4aa1dcac6348d33fd118161dc5eadf126dc7b5074a28cd9440aaa04cb
- Size of remote file:
- 1.49 GB
- SHA256:
- 30ba37be5694a0ce2e1237c1329e9cc3c37a2a8d92aed65e9467bc352d7e7601
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