Instructions to use cloudwalkerw/wavlm-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/wavlm-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/wavlm-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/wavlm-base") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/wavlm-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from cloudwalkerw/wavlm-base: direct link, hf CLI and curl.
- Browser
- Download file 169 Bytes
-
https://huggingface.co/cloudwalkerw/wavlm-base/resolve/main/train_results.json
- Command line
-
hf download hf://cloudwalkerw/wavlm-base/train_results.json
-
curl -L -o train_results.json https://huggingface.co/cloudwalkerw/wavlm-base/resolve/main/train_results.json
169 Bytes
| { | |
| "epoch": 10.0, | |
| "train_loss": 0.3268948573686476, | |
| "train_runtime": 17006.2717, | |
| "train_samples_per_second": 14.924, | |
| "train_steps_per_second": 0.466 | |
| } |