Instructions to use ambujm22/mspeecht5_asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ambujm22/mspeecht5_asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ambujm22/mspeecht5_asr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ambujm22/mspeecht5_asr") model = AutoModelForSpeechSeq2Seq.from_pretrained("ambujm22/mspeecht5_asr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d750b19353e948cd215b9af758fe21f5f08b6887faa5cf2ec292f3ba215f334
- Size of remote file:
- 619 MB
- SHA256:
- 96d77bfc9fdf149adaec24c897476035aa84365b16f705e4b758d9799eb72b98
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