Instructions to use octava/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use octava/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="octava/audio_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("octava/audio_classification") model = AutoModelForAudioClassification.from_pretrained("octava/audio_classification") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 2
Browse files- pytorch_model.bin +1 -1
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 378362729
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3ea5a46bc3dc1bab0aa6e10f19bd9a79dbc0f00f3015e3dd97613ec98e2ae583
|
| 3 |
size 378362729
|