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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration: direct link, hf CLI and curl.
- Browser
- Download file 726 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration/resolve/1da80293ec78762e136cf6dd64b652693f9ab364/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration@1da80293ec78762e136cf6dd64b652693f9ab364/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration/resolve/1da80293ec78762e136cf6dd64b652693f9ab364/pytorch_model.bin
726 kB
- Xet hash:
- afdd21b9583aafddcbb6ce32d1f958b68923e24e4430d9ace6757d3f7c9a91f9
- Size of remote file:
- 726 kB
- SHA256:
- 2405e0f0f7db2a9a78bb15df24eae0a88a8cc089b3ed19e53e4d218ea4325f84
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