Instructions to use defining/maggle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use defining/maggle with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="defining/maggle")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("defining/maggle") model = AutoModelForSpeechSeq2Seq.from_pretrained("defining/maggle", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f3df5f7bfcf41262ccda189582e09ebc2d34365d386882b2f6a8aacfea4aa34
- Size of remote file:
- 6.17 GB
- SHA256:
- c3b2988d9bda8155463311709196fec1a4cc4c74dac73db1be1258017d5f2fa6
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