Instructions to use unanam/tiny_test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unanam/tiny_test1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="unanam/tiny_test1")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("unanam/tiny_test1") model = AutoModelForSpeechSeq2Seq.from_pretrained("unanam/tiny_test1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76b3fb64f0567032d02a2b9205f7f62fe7d92ac732ebb1b7f84f10c13b8cd246
- Size of remote file:
- 4.6 kB
- SHA256:
- 82d385b5e839e23a5dade082b9caca3276a9487893ffba9c193589d1d6315d61
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.