Instructions to use hf-internal-testing/tiny-random-unispeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-unispeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-unispeech")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-unispeech") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-unispeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model (#3)
Verified signature- Adding `safetensors` variant of this model (cd83bd9149631201c12b3da6be79f0484fbfd51f) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>3ee5cca
-
Upload ONNX weights (#2)
Verified signature- [Awaiting approval] Upload ONNX weights (8be843a7b9d460abbfd72504764530252d0e2921)0b4b24b
-
up
a90315f
-
up
9512a40 -
initial commit
b69d202