Audio Classification
Transformers
PyTorch
TensorBoard
wav2vec2
text-classification
Generated from Trainer
Instructions to use Jungwoo4021/wav2vec2-base-ks-finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jungwoo4021/wav2vec2-base-ks-finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Jungwoo4021/wav2vec2-base-ks-finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("Jungwoo4021/wav2vec2-base-ks-finetuning") model = AutoModelForSequenceClassification.from_pretrained("Jungwoo4021/wav2vec2-base-ks-finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from Jungwoo4021/wav2vec2-base-ks-finetuning: direct link, hf CLI and curl.
- Browser
- Download file 169 Bytes
-
https://huggingface.co/Jungwoo4021/wav2vec2-base-ks-finetuning/resolve/main/train_results.json
- Command line
-
hf download hf://Jungwoo4021/wav2vec2-base-ks-finetuning/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Jungwoo4021/wav2vec2-base-ks-finetuning/resolve/main/train_results.json
169 Bytes
| { | |
| "epoch": 10.0, | |
| "train_loss": 0.8214968571662903, | |
| "train_runtime": 30319.4082, | |
| "train_samples_per_second": 16.852, | |
| "train_steps_per_second": 0.016 | |
| } |