Instructions to use mbien/fma2vec2popularity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbien/fma2vec2popularity with Transformers:
# Load model directly from transformers import AutoProcessor, Wav2Vec2ForAudioClassification processor = AutoProcessor.from_pretrained("mbien/fma2vec2popularity") model = Wav2Vec2ForAudioClassification.from_pretrained("mbien/fma2vec2popularity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,057 Bytes
695a1ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | # Predicting music popularity using DNNs
This is a model fine-tuned for music popularity classification, 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) |