Instructions to use hf-internal-testing/tiny-random-UniSpeechSatForAudioFrameClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UniSpeechSatForAudioFrameClassification with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-UniSpeechSatForAudioFrameClassification") model = AutoModelForAudioFrameClassification.from_pretrained("hf-internal-testing/tiny-random-UniSpeechSatForAudioFrameClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8af2546f64b746e9b96ff211e6fccd5d814e47742fbdab05fe1870832716e52b
- Size of remote file:
- 134 kB
- SHA256:
- 0caf0d0d4f15fcd7ec50d3068cb1c9068a6caa12a19f323a1b8713754c313ee0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.