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 train_results.json from cloudwalkerw/facebook_wav2vec2-base: direct link, hf CLI and curl.
- Browser
- Download file 167 Bytes
-
https://huggingface.co/cloudwalkerw/facebook_wav2vec2-base/resolve/main/train_results.json
- Command line
-
hf download hf://cloudwalkerw/facebook_wav2vec2-base/train_results.json
-
curl -L -o train_results.json https://huggingface.co/cloudwalkerw/facebook_wav2vec2-base/resolve/main/train_results.json
167 Bytes
| { | |
| "epoch": 9.98, | |
| "train_loss": 0.5994764679610127, | |
| "train_runtime": 37020.076, | |
| "train_samples_per_second": 6.856, | |
| "train_steps_per_second": 0.107 | |
| } |