Instructions to use padmalcom/wav2vec2-large-emotion-detection-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use padmalcom/wav2vec2-large-emotion-detection-german with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="padmalcom/wav2vec2-large-emotion-detection-german")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("padmalcom/wav2vec2-large-emotion-detection-german") model = Wav2Vec2ForSpeechClassification.from_pretrained("padmalcom/wav2vec2-large-emotion-detection-german", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -12,7 +12,7 @@ datasets:
|
|
| 12 |
- emo-DB
|
| 13 |
widget:
|
| 14 |
- src: >-
|
| 15 |
-
https://huggingface.co/padmalcom/
|
| 16 |
example_title: Sample 1
|
| 17 |
pipeline_tag: audio-classification
|
| 18 |
metrics:
|
|
|
|
| 12 |
- emo-DB
|
| 13 |
widget:
|
| 14 |
- src: >-
|
| 15 |
+
https://huggingface.co/padmalcom/wav2vec2-large-emotion-detection-german/resolve/main/test.wav
|
| 16 |
example_title: Sample 1
|
| 17 |
pipeline_tag: audio-classification
|
| 18 |
metrics:
|