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:
- 78447cd6d2d5700c568481971a82ccc7c35efbc23c8fe03fa0076d28bc73c096
- Size of remote file:
- 94.6 kB
- SHA256:
- b9da789434b2e6cf676c863a011d39bbc1add7ee20deb2e8b20baabbeda882ce
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