Instructions to use Scalable-ML/checkpoint-1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scalable-ML/checkpoint-1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Scalable-ML/checkpoint-1000")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Scalable-ML/checkpoint-1000") model = AutoModelForSpeechSeq2Seq.from_pretrained("Scalable-ML/checkpoint-1000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5534b04dddd2fffbfd31d36612428bc7753517010b68294b0218259a7ea3e593
- Size of remote file:
- 967 MB
- SHA256:
- 56b67475010d5ef04b5b0601dabf60d6b094bf35073cf529a45113a7cd0ce2e1
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