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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration: direct link, hf CLI and curl.
- Browser
- Download file 726 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration/resolve/5a64e5dfbd4a154498c810e4d0f97252f4739fce/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration@5a64e5dfbd4a154498c810e4d0f97252f4739fce/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration/resolve/5a64e5dfbd4a154498c810e4d0f97252f4739fce/pytorch_model.bin
726 kB
- Xet hash:
- 6331510d61d872f8ea231afec183b625592ff73b1d484b2f2195b48734179448
- Size of remote file:
- 726 kB
- SHA256:
- b13bd885fe98a7446d569a71bca37c78d6b75746d0e4c415592c518bbd92262f
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