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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 99ebe60137a0b1248b531247b4c3060a2ee250618e0ad8683049eb7e95bc0fcb
- Size of remote file:
- 837 kB
- SHA256:
- f8806302a22a12fe6857c6ba67ec37eec424a06d78e83c07ccc5e95ac9bdbcd2
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