Instructions to use mbien/fma2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbien/fma2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mbien/fma2vec")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("mbien/fma2vec") model = AutoModel.from_pretrained("mbien/fma2vec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # Predicting music popularity using DNNs | |
| This is a pre-trained wav2vec2.0 model, trained on a fill Free Music Archive repository, created as part of DH-401: Digital Musicology class on EPFL | |
| ## Team | |
| * Elisa (elisa.michelet@epfl.ch) | |
| * Michał (michal.bien@epfl.ch) | |
| * Noé (noe.durandard@epfl.ch) | |
| ## Milestone 3 | |
| Main notebook presenting out results is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone3.ipynb) | |
| Notebook describing the details of Wav2Vec2.0 pre-training and fine-tuning for the task is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone3-wav2vec2.ipynb) | |
| ## Milestone 2 | |
| Exploratory data analysis notebook is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone2.ipynb) | |
| ## Milestone 1 | |
| Refined project proposal is available [here](https://github.com/Glorf/DH-401/blob/main/milestone0.md) | |
| ## Milestone 0 | |
| Original project proposal is available in git history [here](https://github.com/Glorf/DH-401/blob/bb14813ff2bbbd9cdc6b6eecf34c9e3c160598eb/milestone0.md) |