datapointai/tts-human-preferences-medium

TTS Human Preferences (Medium)

Human preference dataset for text-to-speech (TTS) audio quality evaluation. Each row contains two TTS audio renderings of the same text prompt, along with 15 human preference annotations indicating which audio sounds more natural.

This is the medium (2,000-row) subset. See also: small (1,000 rows). Larger versions will follow.

Dataset Summary

Metric	Value
Total rows	2,000
Annotations per row	15
Total annotations	30,000
Unique prompts	2,000
Audio format	MP3

Comparison Strategies

The dataset uses three strategies (balanced ~667 rows each) to create meaningful variation between audio pairs:

1. Model Gap (1_model_gap)

Same voice, same settings, different TTS model. Compares across model pairs from: eleven_multilingual_v2, eleven_monolingual_v1, eleven_turbo_v2_5, eleven_flash_v2_5.

2. Settings Gap (2_settings_gap)

Same voice, same model, different voice settings. Four sub-strategies:

• stability_contrast — low (0.25-0.40) vs high (0.75-0.90) stability
• speed_contrast — slow (0.75-0.88) vs fast (1.12-1.25)
• style_contrast — low (0.0-0.10) vs high (0.35-0.55) style
• combined — multiple settings differ simultaneously

3. Voice Gap (3_voice_gap)

Same model, same settings, different voice. Three sub-types: male vs male, female vs female, male vs female. Voice pool: 10 male + 8 female voices.

Prompts

Sourced from the LJ Speech dataset:

• Filtered to 8-22 word English sentences ending in .!?
• Content-filtered to remove violence, crime, sexual, and other sensitive topics
• 2,000 unique, deduplicated prompts

Dataset Structure

Columns

Column	Type	Description
prompt	string	Text prompt used to generate both audio clips
audio_a	audio	MP3 audio file for audio A
audio_b	audio	MP3 audio file for audio B
strategy	string	Comparison strategy: 1_model_gap, 2_settings_gap, 3_voice_gap
strategy_detail	string	Sub-strategy (e.g., v2_vs_v1, stability_contrast, male_vs_female)
audio_a_voice_name	string	Voice name for audio A
audio_a_voice_id	string	Voice ID for audio A
audio_a_model_id	string	TTS model for audio A
audio_a_stability	float	Stability setting for audio A
audio_a_similarity_boost	float	Similarity boost setting for audio A
audio_a_style	string	Style setting for audio A (N/A for unsupported models)
audio_a_speed	float	Speed setting for audio A
audio_a_speaker_boost	string	Speaker boost for audio A (N/A for unsupported models)
audio_b_voice_name	string	Voice name for audio B
audio_b_voice_id	string	Voice ID for audio B
audio_b_model_id	string	TTS model for audio B
audio_b_stability	float	Stability setting for audio B
audio_b_similarity_boost	float	Similarity boost setting for audio B
audio_b_style	string	Style setting for audio B (N/A for unsupported models)
audio_b_speed	float	Speed setting for audio B
audio_b_speaker_boost	string	Speaker boost for audio B (N/A for unsupported models)
weighted_results_audio_a	float	Fraction of annotators who preferred audio A
weighted_results_audio_b	float	Fraction of annotators who preferred audio B
num_annotations	int	Number of annotations (always 15)
detailed_results	list	Per-annotator votes with display_position, time_taken_ms, winner

Detailed Results Structure

Each entry in detailed_results:

{
  "display_position": "unknown",
  "time_taken_ms": 14376,
  "winner": "audio_a"
}

Usage

from datasets import load_dataset

ds = load_dataset("datapointai/tts-human-preferences-medium", split="train")
print(ds[0]["prompt"])
print(f"Audio A preferred: {ds[0]['weighted_results_audio_a']:.1%}")
print(f"Audio B preferred: {ds[0]['weighted_results_audio_b']:.1%}")

Train a Reward Model

import pandas as pd
from datasets import load_dataset

ds = load_dataset("datapointai/tts-human-preferences-medium", split="train")
df = ds.to_pandas()

for _, row in df.iterrows():
    prompt = row["prompt"]
    score_a = row["weighted_results_audio_a"]
    score_b = row["weighted_results_audio_b"]
    strategy = row["strategy"]
    # Use as preference pairs for DPO, reward modeling, etc.

Data Collection

Annotations were collected through Datapoint AI's consumer app SDK using forced-choice pairwise comparison ("Which audio sounds more natural?"). Each comparison was annotated by 15 unique annotators.

License

CC-BY-4.0

Citation

@dataset{datapointai_tts_preferences_2026,
  title={TTS Human Preferences},
  author={Datapoint AI},
  year={2026},
  url={https://huggingface.co/datasets/datapointai/tts-human-preferences-medium},
  note={30,000 pairwise human preference labels for TTS audio quality}
}