Instructions to use cloudwalkerw/wavlm-base_2_predict with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/wavlm-base_2_predict with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/wavlm-base_2_predict")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/wavlm-base_2_predict") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/wavlm-base_2_predict", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from cloudwalkerw/wavlm-base_2_predict: direct link, hf CLI and curl.
- Browser
- Download file 187 Bytes
-
https://huggingface.co/cloudwalkerw/wavlm-base_2_predict/resolve/main/eval_results.json
- Command line
-
hf download hf://cloudwalkerw/wavlm-base_2_predict/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/cloudwalkerw/wavlm-base_2_predict/resolve/main/eval_results.json
187 Bytes
| { | |
| "eval_accuracy": 0.8974400257607471, | |
| "eval_loss": 0.33257797360420227, | |
| "eval_runtime": 502.5226, | |
| "eval_samples_per_second": 49.439, | |
| "eval_steps_per_second": 24.719 | |
| } |