Instructions to use superb/hubert-base-superb-er with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superb/hubert-base-superb-er with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="superb/hubert-base-superb-er")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("superb/hubert-base-superb-er") model = AutoModelForAudioClassification.from_pretrained("superb/hubert-base-superb-er", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md
Browse files
README.md
CHANGED
|
@@ -9,9 +9,9 @@ tags:
|
|
| 9 |
- audio-classification
|
| 10 |
license: apache-2.0
|
| 11 |
widget:
|
| 12 |
-
-
|
| 13 |
src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav
|
| 14 |
-
-
|
| 15 |
src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro04_F000.wav
|
| 16 |
---
|
| 17 |
|
|
|
|
| 9 |
- audio-classification
|
| 10 |
license: apache-2.0
|
| 11 |
widget:
|
| 12 |
+
- example_title: IEMOCAP clip "happy"
|
| 13 |
src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav
|
| 14 |
+
- example_title: IEMOCAP clip "neutral"
|
| 15 |
src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro04_F000.wav
|
| 16 |
---
|
| 17 |
|