Instructions to use mispeech/ced-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mispeech/ced-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mispeech/ced-tiny", trust_remote_code=True)# Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("mispeech/ced-tiny", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -22,8 +22,7 @@ CED are simple ViT-Transformer-based models for audio tagging. Notable differenc
|
|
| 22 |
|
| 23 |
## Install
|
| 24 |
```bash
|
| 25 |
-
|
| 26 |
-
pip install -r requirements.txt
|
| 27 |
```
|
| 28 |
|
| 29 |
## Inference
|
|
|
|
| 22 |
|
| 23 |
## Install
|
| 24 |
```bash
|
| 25 |
+
pip install git+https://github.com/jimbozhang/hf_transformers_custom_model_ced.git
|
|
|
|
| 26 |
```
|
| 27 |
|
| 28 |
## Inference
|