Metadata-Version: 2.1
Name: audiologic
Version: 0.1.1
Summary: Python utilities to process and predict on audio attributes
Author-email: Ty Martz <jacksontmartz@gmail.com>
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

![audiologic logo](static/audiologic_Logo_rec.png)
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Python Module to process and predict on music attributes

[![PyPI Latest Release](https://img.shields.io/pypi/v/audiologic)](https://pypi.org/project/audiologic/)
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Two Models were built and trained to predict valence given an audio sample. One uses a feature pipeline on top of librosa to make a number of predictors that go into a Random Forest model to determine a valence prediction. The other uses OpenAI's whisper model to transcribe lyrics, then tokenize the words, and again a trained Random Forest model makes the prediction based on lyrics.

|        | Model RMSE |
|--------|------------|
| Audio  | 1.56       |
| Lyrics | 1.28       |

### Data Used:
- 1000 Song Dataset - [Download here](https://cvml.unige.ch/databases/emoMusic/)
- Spotify Developer API - [30 second previews](https://developer.spotify.com/documentation/web-api/)

## Package Requirements
`pip install -r requirements.txt`
- make sure to download [whisper from openai](https://github.com/openai/whisper) (not currently included in requirements.txt)
- Also must install ffmpeg (using brew, choco, etc.)
