vancenceho/youtube-spotify-audio-features

---
license: cdla-sharing-1.0
language:
- en
tags:
- music
pretty_name: Spotify-YouTube Audio Features
size_categories:
- 10K<n<100K
---

# Spotify–YouTube Audio Features

Tabular **librosa** audio features for tracks aligned with the Spotify / YouTube pipeline in the *viral-content-predictor* project. Each row is one Spotify `track_id` matched to a downloaded YouTube audio clip; features are aggregated statistics (mean / std) computed on the decoded waveform.

## Files

| File | Description |
|------|-------------|
| `audio_features.csv` | One row per track: `track_id`, 89 derived feature dimensions (means/stds), `extraction_success`, `error_message`. |

## Data instance

- **Format:** CSV (header row).
- **Rows:** on the order of **tens of thousands** of tracks (exact count may change as the extraction job is updated).
- **Key column:** `track_id` — Spotify track identifier, join key to other project tables.
- **Label / target:** not included; this file is **features only**.

## Feature groups (89 dimensions)

Features are extracted with **librosa** from MP3s under the project’s YouTube-audio download step (`02b_youtube_audio_feature_extraction.ipynb`). Groups include:

- Tempo (1)
- RMS energy (2)
- Zero crossing rate (2)
- Spectral centroid, rolloff, bandwidth (6)
- Spectral contrast — 7 bands (14)
- MFCC — 13 coefficients (26)
- Chroma — 12 pitch classes (24)
- Tonnetz (12)
- Onset strength (2)

Most groups contribute **mean** and **std** columns; see the CSV header for exact names.

Additional columns:

- **`extraction_success`** — boolean-like flag indicating whether feature extraction completed for that file.
- **`error_message`** — empty or diagnostic text when extraction failed.

## Provenance

- **Audio source:** user-downloaded YouTube audio (`.mp3`) matched to Spotify metadata elsewhere in the pipeline.
- **Processing:** Python, **librosa**; optional multiprocessing for throughput.
- **This artifact:** not the raw audio; only numeric summaries suitable for modeling and joins.

## Usage

```python
import pandas as pd

df = pd.read_csv("audio_features.csv")
# Join on track_id with Spotify / YouTube tables in your project
```

## Limitations

- Coverage is limited to tracks with a successful YouTube match and a readable audio file.
- Feature definitions follow librosa defaults; hyperparameters (e.g. hop length, FFT size) are fixed in the extraction notebook.

## License

This dataset card specifies **CDLA-Sharing-1.0**. Ensure your redistribution of the CSV and any derived data complies with that license and with the licenses of the underlying **Spotify** and **YouTube** data you used to build the file.