Instructions to use cloudwalkerw/facebook_wav2vec2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/facebook_wav2vec2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/facebook_wav2vec2-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/facebook_wav2vec2-base") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/facebook_wav2vec2-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from cloudwalkerw/facebook_wav2vec2-base: direct link, hf CLI and curl.
- Browser
- Download file 205 Bytes
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https://huggingface.co/cloudwalkerw/facebook_wav2vec2-base/resolve/main/eval_results.json
- Command line
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hf download hf://cloudwalkerw/facebook_wav2vec2-base/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/cloudwalkerw/facebook_wav2vec2-base/resolve/main/eval_results.json
205 Bytes
| { | |
| "epoch": 9.98, | |
| "eval_accuracy": 0.8974400257607471, | |
| "eval_loss": 0.4227793216705322, | |
| "eval_runtime": 491.8427, | |
| "eval_samples_per_second": 50.512, | |
| "eval_steps_per_second": 25.256 | |
| } |