You agree to use this dataset under the CC-BY-4.0 license with attribution to Datapoint AI, and you agree not to attempt to re-identify annotators. Usage rights for the audio are governed by each model provider's terms.

Datapoint Audio Bench — 315K votes across 15 models

Text-to-speech human preferences: 315K votes across 15 models

This gated dataset contains the evaluation record behind Datapoint Audio Bench: 315,000 eligible pairwise votes comparing 15 text-to-speech models in a complete round-robin over 300 English prompts. The prompt set covers eight practical voice-agent categories, and every generated sample is included as a typed audio record.

The source evaluation collected 357,651 completed responses. The published benchmark excluded 41,873 trust-voided responses and deterministically capped 778 overserved responses, leaving the exact 315,000 votes used by the leaderboard published on 2026-09-01. The responses config contains only those score-bearing responses; voided and over-cap responses are not included.

Built on the Datapoint annotation platform.

Key features

Complete comparison design. Every one of the 105 model pairings is evaluated on all 300 prompts, for 31,500 comparison cells.
Audio previews. The default audio config stores all 4,500 source FLAC renders as typed Hugging Face Audio features. Each render is stored once rather than duplicated across every comparison in which it appears. The card below also provides direct previews for authorized users when the private-repository Dataset Viewer is unavailable on the owner's Hub plan.
Operational voice-agent prompts. The categories cover transactional readouts, empathy and de-escalation, names and spell-outs, instructions and troubleshooting, repairs and disfluency, greetings and brand delivery, policy disclosure, and scheduling and escalation.
Blind comparison. Annotators received two unlabeled audio candidates and selected the preferred delivery. The schedule balances which model occupies the first candidate slot across prompts.
Frozen quality evidence. Every response contains the audio-trust estimate captured at completion time. Raw votes drive the official benchmark; trust-weighted aggregates are provided for sensitivity analysis.

Prompt categories

The benchmark contains 300 prompts across eight practical customer-support categories. Every prompt is evaluated for all 105 model pairs with ten score-bearing responses per pair.

Category	Dataset value	Prompts
Empathy & De-escalation	`empathy_deescalation`	45
Greetings & Brand	`greetings_brand`	30
Instructions & Troubleshooting	`instructions_troubleshooting`	40
Names & Spell-outs	`names_spellouts`	45
Policy & Disclosure	`policy_disclosure`	30
Repairs & Disfluency	`repairs_disfluency`	35
Scheduling & Escalation	`scheduling_escalation`	30
Transactional Readouts	`transactional_readouts`	45
Total		300

Audio preview

The three clips below render the same held-out empathy prompt. They are representative examples for checking playback and are not a claim about model quality. Access remains subject to this repository's approval gate.

I need to share something serious. Your email address was part of last week's data exposure — your password was not. Here is exactly what we're doing about it.

Chatterbox HD

Eleven v3

GPT-4o mini TTS

Dataset structure

Config	Rows	Description
`audio` (default)	4,500	One full 48 kHz, mono, 24-bit FLAC render stored as typed audio
`pairs`	31,500	One prompt/model comparison with vote aggregates and audio join keys
`responses`	315,000	One score-bearing human response with anonymized annotator and vote metadata
`prompts`	300	Prompt script, target delivery, persona, pace, and rubric
`models`	15	Stable model identifier and display name

audio

Each row stores one model output. This is the default config so opening the Dataset Viewer exposes playable examples whenever the repository owner's Hub plan supports a private viewer. The card-level examples above remain available to authorized users without that feature.

Column	Type	Description
`audio_key`	string	Stable `{model_id}/{prompt_id}` join key
`prompt_id`, `prompt_index`, `category`	string/int	Prompt identity and benchmark category
`model_id`	string	Stable model identifier
`audio`	audio	Embedded FLAC bytes rendered as a player by the Hub
`sample_rate`, `channels`, `bit_depth`	int	Source format metadata
`total_samples`, `duration_seconds`, `byte_size`	numeric	Audio size and duration metadata
`sha256`	string	SHA-256 digest of the exact FLAC bytes

pairs

Each row is one comparison. audio_a_key and audio_b_key join to the audio config without embedding the same FLAC many times.

Column	Type	Description
`pair_key`	string	Stable comparison identifier
`category`, `prompt_id`	string	Prompt and category identity
`model_a`, `model_b`	string	Models assigned to the two source candidate slots
`audio_a_key`, `audio_b_key`	string	Join keys into the `audio` config
`votes_a`, `votes_b`	int	Eligible votes used by the published leaderboard
`label_a`, `label_b`	float	Preference fractions; 0.5/0.5 when no eligible vote exists
`trust_weighted_votes_a`, `trust_weighted_votes_b`	float	Eligible vote totals weighted by frozen audio trust
`winner`	string	`a`, `b`, or `tie`
`num_votes`	int	Total eligible votes for the comparison, capped at 10

responses

Each row is one response used by the published score. A pair has at most ten rows, exactly matching votes_a + votes_b in pairs; voided and over-cap responses are omitted. Raw account and response IDs are not released. annotator is a salted hash that is stable only inside this export.

Column	Type	Description
`pair_key`	string	Joins to `pairs`
`prompt_id`, `category`	string	Prompt identity and benchmark category
`chosen`	string	`a` or `b` for a valid selection
`annotator`	string	Export-local salted annotator hash
`trust_score`	float	Frozen audio-trust estimate used for sensitivity weighting
`time_taken_ms`, `completed_at`, `country`	mixed	Response timing and country snapshot

Splits

The audio, pairs, responses, and prompts configs share a deterministic, prompt-level train/test split. Thirty prompts are held out with stratification across the eight categories. No prompt or audio render crosses the split, so a reward model can be trained on train and evaluated on test without prompt leakage. models is a reference table with one train split.

Usage

Approved gated access and an authenticated Hugging Face token are required.

from datasets import load_dataset

audio = load_dataset(
    "datapointai/text-to-speech-human-preferences-315k",
    "audio",
    split="train",
    token=True,
)
sample = audio[0]
print(sample["model_id"], sample["prompt_id"])
print(sample["audio"])

Load comparison aggregates and join them to audio by key:

pairs = load_dataset(
    "datapointai/text-to-speech-human-preferences-315k",
    "pairs",
    split="train",
    token=True,
)

audio_by_key = {row["audio_key"]: row["audio"] for row in audio}
pair = pairs[0]
audio_a = audio_by_key[pair["audio_a_key"]]
audio_b = audio_by_key[pair["audio_b_key"]]
print(pair["model_a"], "vs", pair["model_b"], "->", pair["winner"])

For metadata-only iteration without decoding audio:

from datasets import Audio

audio = audio.cast_column("audio", Audio(decode=False))

Benchmark methodology

The official category boards fit a deterministic Bradley–Terry model to raw eligible votes and transform the fitted strengths to Elo. Prompt-cluster bootstrap samples provide score uncertainty and rank spread. The Overall board combines the eight published category boards with equal category weight.

The official fit does not use trust_weighted_votes_*. Those fields support a sensitivity analysis against the frozen audio-trust estimates.

Leaderboard

Elo ratings come from the deterministic Bradley–Terry fit on the 315,000 published responses. The chart shows the Overall board published on 2026-09-01, combining all eight categories with equal weight.

Customer support audio model Elo rankings — 15 models ranked by Elo score

Intended use

Use this dataset to:

train or evaluate audio preference and reward models;
study human preference aggregation and quality filtering;
compare text-to-speech systems by prompt category;
reproduce or audit the published benchmark inputs; and
evaluate reranking or best-of-N selection methods for speech generation.

Limitations and responsible use

The benchmark measures preference on this fixed English prompt set; it does not establish universal speech quality, accessibility, safety, or language coverage.
Model folder names are the stable identities supplied with the source benchmark. Exact provider revisions and generation parameters were not available and must not be inferred from the audio.
Human preference can encode demographic and cultural bias. Category or overall rank should not be treated as a guarantee for every listener or use case.
Do not attempt to re-identify annotators from the export-local hashes, timestamps, or country fields.
Synthetic voices can be misused for impersonation. Follow applicable law, provider terms, disclosure requirements, and consent expectations.

License

Votes, prompts, and Datapoint-authored metadata are provided under CC-BY-4.0. Audio files are outputs of the listed third-party models and are included for private evaluation and research; rights and usage restrictions for those outputs remain governed by the applicable model-provider terms.

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