Instructions to use hf-internal-testing/tiny-random-speech-encoder-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-speech-encoder-decoder 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-speech-encoder-decoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-speech-encoder-decoder") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-internal-testing/tiny-random-speech-encoder-decoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-speech-encoder-decoder: direct link, hf CLI and curl.
- Browser
- Download file 645 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-speech-encoder-decoder/resolve/fb33a9a7ad6f5ecaab3e83d24c8de9531bb8ac61/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-speech-encoder-decoder@fb33a9a7ad6f5ecaab3e83d24c8de9531bb8ac61/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-speech-encoder-decoder/resolve/fb33a9a7ad6f5ecaab3e83d24c8de9531bb8ac61/pytorch_model.bin
645 kB
- Xet hash:
- d1a1a453bec79778f646af2a48961f4423779083ba2c21cffa98daec0cc35f92
- Size of remote file:
- 645 kB
- SHA256:
- 0e5d97dd2d931ac2ce5e3532b3ec32862c0501edca5b31f99a34ce601df19627
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