Instructions to use hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 364716c36b8c848382015314c7ddff53fa0d9406041af104f2b07c26b9c7792f
- Size of remote file:
- 706 kB
- SHA256:
- 05e88ab0a4fff5fa9e894049921614c89dff1f16c9b5783939e1e85eb9cb9af5
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