Instructions to use hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b4e26dcedd5b889ca6cd3e0286402560d510f47d14f6bc718ae1850137d3901
- Size of remote file:
- 706 kB
- SHA256:
- 9e70175699491cd414ec82cf5db5d4e5d3f13b129ba457dd0b4c29ac343d8357
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