TTS-AGI/emolia-3k-speaker-clusters-DACVAE

Emolia 3K Speaker Clusters

A curated set of 3,000 diverse speaker clusters derived from the TTS-AGI/emolia-hq dataset, with up to 20 representative audio samples per cluster.

Overview

The original emolia-hq dataset contains hundreds of thousands of speech samples with 128-dimensional WavLM speaker timbre embeddings. These were first clustered into 10,000 centroids, then intelligently pruned to 3,000 using density-aware farthest-point sampling to ensure:

• Outlier preservation: Unique/rare voice types are kept (1.4x outlier over-representation)
• Redundancy reduction: Dense clusters of similar voices (e.g., many similar bright female voices) are collapsed into representatives
• Even coverage: The 3,000 centroids are spread uniformly across the embedding space

Key Statistics

Metric	Value
Total clusters	3,000
Clusters fully filled (20 samples)	2994
Total audio samples	59,977
Samples per cluster	up to 20
Embedding dimension	128 (WavLM timbre)
Distance metric	Cosine
Avg DNS-MOS (best samples)	3.46
Avg duration (best samples)	9.3s
Source dataset	TTS-AGI/emolia-hq

Language Distribution (best-of samples)

Language	Count
EN	3000

Repository Structure

.
├── cluster_samples/              # Tar archives of all samples
│   ├── cluster_samples_0000-0499.tar
│   ├── cluster_samples_0500-0999.tar
│   ├── cluster_samples_1000-1499.tar
│   ├── cluster_samples_1500-1999.tar
│   ├── cluster_samples_2000-2499.tar
│   ├── cluster_samples_2500-2999.tar
├── cluster_best.tar              # Best sample per cluster (highest DNS-MOS)
│                                 # Contains cluster_best/{0..2999}.mp3 + .json
├── centroids_pruned.npy          # 3000x128 float32 cluster centroids
├── centroids_pruned_indices.npy  # Indices mapping to original 10k centroids
├── pruning_report.html           # Detailed report on the pruning methodology
├── pruning_stats.json            # Raw metrics for all pruning methods tested
├── scripts/
│   ├── prune_centroids.py        # Centroid pruning pipeline
│   └── extract_cluster_samples.py  # Sample extraction pipeline
└── README.md

Each cluster_samples_XXXX-YYYY.tar contains folders named by cluster index, each with up to 20 .mp3 + .json pairs.

Pruning Methodology

Three methods were compared to reduce 10,000 centroids to ~3,000:

1. HDBSCAN + Medoid Selection

HDBSCAN identifies density-based clusters; noise points (outliers) are preserved, and each cluster is represented by its medoid. Result: Could not reach the 2k-4k target range (kept 6,500+ points due to high noise fraction).

2. Farthest-Point Sampling with Outlier Protection (Winner)

1. Identify the top 10% most isolated points (by avg cosine distance to 10 nearest neighbors)
2. Pre-seed these 1,000 outliers into the selection
3. Iteratively pick the point farthest from the current selection

This method won because it provides the best balance of coverage, spread, and outlier preservation.

3. Density-Based Greedy Pruning

Remove points from densest regions first, preserving isolated points. Good outlier preservation but worse coverage quality.

Quality Metrics (Selected Method)

Metric	Value	Meaning
Coverage mean	0.1185	Avg cosine dist from any original centroid to nearest selected
Coverage max	0.2741	Worst-case distance (no centroid is "orphaned")
Mean min pairwise	0.3028	Selected centroids are well spread apart
Outlier preservation	1.40x	Isolated voices over-represented (desired)

Sample Metadata Format

Each .json file contains:

{
  "id": "EN_B00042_S00123_W000456",
  "text": "Transcription of the utterance",
  "duration": 8.5,
  "dnsmos": 3.82,
  "speaker": "EN_B00042_S00123",
  "language": "en",
  "emotion_caption": "Natural language description of emotional content",
  "emotion_annotation": { "Arousal_best": 1.5, "Valence_best": 0.8, "..." : "..." },
  "wavelm_timbre_embedding": [0.044, -0.022, "...128 dims..."],
  "_cluster_idx": 42,
  "_cosine_similarity": 0.95
}

Usage

Load centroids

import numpy as np
centroids = np.load("centroids_pruned.npy")  # (3000, 128)

Find nearest cluster for a new embedding

def find_cluster(embedding, centroids):
    emb = np.array(embedding) / np.linalg.norm(embedding)
    norms = np.linalg.norm(centroids, axis=1, keepdims=True)
    centroids_normed = centroids / np.maximum(norms, 1e-8)
    similarities = centroids_normed @ emb
    return int(np.argmax(similarities)), float(similarities.max())

cluster_idx, similarity = find_cluster(new_embedding, centroids)

Extract samples from tar

import tarfile
with tarfile.open("cluster_samples_0000-0499.tar") as tf:
    tf.extractall(".")
# Now cluster_samples/0/, cluster_samples/1/, ... are available

License

CC-BY-4.0 (inherited from TTS-AGI/emolia-hq)

Citation

@dataset{emolia_3k_speaker_clusters,
  title={Emolia 3K Speaker Clusters},
  author={LAION},
  year={2026},
  url={https://huggingface.co/datasets/laion/emolia-3k-speaker-clusters},
  note={Derived from TTS-AGI/emolia-hq}
}