Instructions to use hf-internal-testing/tiny-random-wavlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-wavlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-wavlm")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-wavlm") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-wavlm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
[Awaiting approval] Upload ONNX weights
Verified signature[Automated] Converted using [Optimum](https://github.com/huggingface/optimum). Models will be merged manually by @Xenova once they have been checked with [Transformers.js](https://github.com/xenova/transformers.js).c925972
-
Update config.json
3cabe08 -
Update config.json
809c269 -
Update config.json
b50a065 -
Update config.json
be01993 -
Update config.json
ad1fc2f -
add wavlm
17b558f -
initial commit
44e9ce0