Instructions to use cloudwalkerw/wavlm-base_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/wavlm-base_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/wavlm-base_4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/wavlm-base_4") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/wavlm-base_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from cloudwalkerw/wavlm-base_4: direct link, hf CLI and curl.
- Browser
- Download file 198 Bytes
-
https://huggingface.co/cloudwalkerw/wavlm-base_4/resolve/main/eval_results.json
- Command line
-
hf download hf://cloudwalkerw/wavlm-base_4/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/cloudwalkerw/wavlm-base_4/resolve/main/eval_results.json
198 Bytes
| { | |
| "epoch": 9.98, | |
| "eval_f1": 0.9459482392872296, | |
| "eval_loss": 0.3324863016605377, | |
| "eval_runtime": 496.607, | |
| "eval_samples_per_second": 50.027, | |
| "eval_steps_per_second": 25.014 | |
| } |