treadon/fma-mert-embeddings

FMA-MERT Embeddings

Pre-computed MERT-v1-330M embeddings for the FMA-Small dataset. 7,997 tracks, each represented as a 1024-dimensional vector, with banger scores (0-10) derived from log-normalized play counts.

Use this dataset to train music quality scorers, explore music similarity, or experiment with audio representation learning -- without needing to download 7.2 GB of audio or run MERT yourself.

Dataset Description

Each row represents one track from FMA-Small, encoded through MERT-v1-330M and annotated with popularity-based quality labels.

Fields

Field	Type	Description
track_id	int	FMA track identifier
embedding	list[float] (1024)	Mean-pooled MERT-v1-330M embedding
banger_score	float (0-10)	Log-normalized play count, scaled to 0-10
genre	string	Top-level genre from FMA metadata
listens	int	Raw play count from FMA

Size

• 7,997 tracks (3 corrupt MP3s out of 8,000 failed during embedding extraction -- 99.96% success rate)
• 1024 dimensions per embedding
• ~31 MB as a NumPy array on disk

Genre Breakdown

FMA-Small is perfectly balanced across 8 genres (~1,000 tracks each):

Genre	Count
Hip-Hop	~1,000
Pop	~1,000
Folk	~1,000
Experimental	~1,000
Rock	~1,000
International	~1,000
Electronic	~1,000
Instrumental	~1,000

Score Distribution

Banger scores are derived from FMA play counts via log-normalization:

log_listens = np.log1p(df["listens"])
banger_score = (log_listens - log_listens.min()) / (log_listens.max() - log_listens.min()) * 10.0

Statistic	Value
Mean	3.27
Median	3.20
Std	1.37
Min	0.00
Max	10.00
Tracks >= 5.0	668 (8.4%)
Tracks >= 7.0	45 (0.6%)
Tracks >= 9.0	4 (0.1%)

The distribution is concentrated in the 1-5 range. Very few tracks have high scores, which reflects the heavy-tailed nature of music popularity (a few hits, many average tracks).

Source Data

Audio Source

FMA (Free Music Archive) -- a large-scale, freely available dataset of audio tracks. FMA-Small contains 8,000 tracks of 30-second clips (7.2 GB), Creative Commons licensed.

Play count range: 196 to 543,252 (mean 4,730, median 2,492). The massive gap between mean and median reflects the power-law distribution typical of music popularity.

How Embeddings Were Generated

Model: m-a-p/MERT-v1-330M -- a 330M parameter, 24-layer self-supervised music understanding model trained on 160,000 hours of audio.

Process:

1. Load each MP3 track and resample to 24kHz mono (MERT's expected input rate) using librosa
2. Truncate to 30 seconds maximum
3. Run through MERT's feature extractor and forward pass
4. Mean-pool the last hidden state across the time dimension: outputs.last_hidden_state.mean(dim=1) to produce a single 1024-dim vector per track
5. Save as NumPy array

# Core embedding logic
waveform, _ = librosa.load("track.mp3", sr=24000, mono=True)
waveform = waveform[:24000 * 30]  # 30s max

inputs = feature_extractor(waveform, sampling_rate=24000, return_tensors="pt")
with torch.no_grad():
    outputs = mert(**inputs)
    embedding = outputs.last_hidden_state.mean(dim=1).squeeze(0).cpu().numpy()  # (1024,)

Compute:

• Device: Apple M4 Pro, Metal Performance Shaders (MPS)
• Processing rate: 1.3 tracks/second
• Total time: 101 minutes for 7,997 tracks
• Peak memory: ~1.7 GB (MERT model + one audio buffer)
• Failures: 3 out of 8,000 (corrupt MP3s)

Why mean pooling? MERT produces ~1,200 time frames (one per ~25ms) for a 30-second clip, each with a 1024-dim vector. Mean pooling collapses these into a single vector that captures the overall "essence" of the track -- rhythm patterns, harmonic content, timbral quality, melodic structure -- while discarding temporal ordering. Simple and effective as a baseline; attention pooling could be explored for improvements.

How to Use

from datasets import load_dataset
import numpy as np

# Load the dataset
ds = load_dataset("treadon/fma-mert-embeddings", split="train")

# Access a single track
track = ds[0]
embedding = np.array(track["embedding"])  # (1024,)
score = track["banger_score"]             # float 0-10
genre = track["genre"]                    # e.g., "Electronic"
listens = track["listens"]                # raw play count

print(f"Track {track['track_id']}: {genre}, score={score:.2f}, listens={listens}")
print(f"Embedding shape: {embedding.shape}")

# Filter by genre
electronic = ds.filter(lambda x: x["genre"] == "Electronic")
print(f"Electronic tracks: {len(electronic)}")

# Get all embeddings as a matrix for training
all_embeddings = np.array(ds["embedding"])   # (7997, 1024)
all_scores = np.array(ds["banger_score"])    # (7997,)

Train a scorer on these embeddings

import torch
import torch.nn as nn
from sklearn.model_selection import train_test_split

# Load embeddings
ds = load_dataset("treadon/fma-mert-embeddings", split="train")
X = np.array(ds["embedding"])     # (7997, 1024)
y = np.array(ds["banger_score"])  # (7997,)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define a simple MLP
scorer = nn.Sequential(
    nn.Linear(1024, 512), nn.BatchNorm1d(512), nn.ReLU(), nn.Dropout(0.3),
    nn.Linear(512, 256), nn.BatchNorm1d(256), nn.ReLU(), nn.Dropout(0.3),
    nn.Linear(256, 128), nn.BatchNorm1d(128), nn.ReLU(), nn.Dropout(0.15),
    nn.Linear(128, 1),
)

# Train... (see treadon/banger-scorer for full training code)

The trained model that ships with treadon/banger-scorer achieved MAE 0.858 and Spearman 0.468 on this data, training in ~30 seconds on M4 Pro.

Use Cases

• Train music quality scorers without downloading 7.2 GB of FMA audio or running MERT (which takes ~100 minutes on GPU)
• Music similarity search -- compute cosine similarity between embeddings to find similar-sounding tracks
• Genre classification -- train a classifier on the embeddings using the genre labels
• Explore MERT's representation space -- visualize with t-SNE/UMAP, analyze what musical features each dimension captures
• Baseline for music understanding tasks -- compare against fine-tuned or alternative audio models

Limitations

• FMA-Small only. 8,000 tracks is relatively small. FMA-Medium (25K) or FMA-Large (106K) would provide more diverse representations.
• Popularity labels are noisy. Play counts reflect many factors beyond musical quality: playlist placement, artist following, upload timing. They are a useful but imperfect proxy.
• Mean pooling discards temporal info. The embeddings capture "what happens" but not "when it happens." Songs with identical frequency content but different temporal structures will have similar embeddings.
• 30-second clips. FMA-Small contains 30-second excerpts, not full tracks. The embedding represents only part of each song.
• Fixed MERT version. These embeddings are from MERT-v1-330M specifically. They are not compatible with other audio encoders or MERT versions.

Citation

@article{li2023mert,
  title={MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training},
  author={Li, Yizhi and Yuan, Ruibin and Zhang, Ge and Ma, Yinghao and others},
  journal={arXiv preprint arXiv:2306.00107},
  year={2023}
}

@inproceedings{defferrard2017fma,
  title={FMA: A Dataset For Music Analysis},
  author={Defferrard, Micha{\"e}l and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier},
  booktitle={ISMIR},
  year={2017}
}

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