Thomcles/YodaLingua-TTS-Arena-English

YodaLingua English TTS Arena

1,080 generated clips · 18 TTS systems · 301 anonymous pairwise judgments · 20 reference speakers · 60 distinct prompts

This is the public English snapshot of the YodaLingua multilingual TTS Arena. It pairs model outputs, shared zero-shot voice references, objective measurements, and privacy-safe human preferences on the same frozen evaluation protocol.

Start here

The default outputs configuration has one row per model × prompt, so the viewer opens on 1,080 playable generated clips, not a 60-row prompt table.

from datasets import load_dataset

outputs = load_dataset(
    "Thomcles/YodaLingua-TTS-Arena-English",
    "outputs",
    split="test",
)
judgments = load_dataset(
    "Thomcles/YodaLingua-TTS-Arena-English",
    "judgments",
    split="train",
)

Configuration	Rows	Intended use
outputs	1,080	Listen, filter by model/task, recompute metrics
judgments	301	Pairwise ranking and preference research
prompts	60	Shared texts and reference voices
model_summary	72	Ready-to-use objective and early preference summaries

Evaluation design

• Natural: conversational rhythm, spontaneity, and varied sentence structure.
• Expressive: emotion, prosody, intention, and storytelling delivery.
• Pronunciation: proper nouns, rare words, loanwords, and difficult sequences.
• Protocol: 20 speakers × 3 unique prompts per speaker = 60 English trials per model.
• Coverage: 18 systems have complete English output coverage in this snapshot.
• No cherry-picking: model failures, repetitions, and hallucinations remain part of the data.

What you can build

• Compare TTS and zero-shot voice-cloning systems on identical inputs.
• Audit pronunciation, naturalness, expressiveness, cloning similarity, and failure modes.
• Recompute WER, CER, speaker similarity, and ranking methods.
• Train or test preference models, evaluators, and leaderboard tooling.
• Create internal model-selection reports while retaining exact benchmark provenance.

Configurations

outputs

Contains the generated FLAC bytes, target text, anonymized speaker ID, task, immutable model revision or provider alias, model-specific license, inference timing, WER/CER components, ASR transcript, and speaker similarity. rtf is generation time divided by audio duration. API timing includes network/provider latency and is not a controlled L4 comparison.

judgments

Contains only the trial/task, randomized A/B model identities, a, b, or tie, and the preferred model when applicable. It deliberately excludes session/viewer identifiers, IP or device information, comparison IDs, playback events, and exact timestamps.

prompts

Contains each reference FLAC and target text once. Speaker IDs are release-scoped, deterministic pseudonyms; source video/channel identifiers are not published here.

model_summary

Contains Overall, Natural, Expressive, and Pronunciation rows per model. Objective error rates are corpus-weighted. Speaker similarity is the mean of successful trial scores. Preference values use the same regularized Bradley–Terry method as the Arena.

Human-preference limitations

The judgments are from self-selected anonymous listening sessions, not a representative panel of verified English speakers. Sessions are not people. Early ratings can move substantially as votes accumulate; consult judgment counts and uncertainty fields. Pair scheduling is adaptive, so the raw data is not an independently and identically distributed sample. Use this snapshot for exploration and transparent research, not as proof of a universal “best” model.

Licensing

The dataset-level license is other because generated outputs inherit different model and provider terms. Every output and summary row includes model_license, model_repository, and model_revision. This dataset does not grant blanket commercial, training, or redistribution rights for all audio. Review the relevant model/provider terms and the YodaLingua source dataset terms before reuse.

Provenance

• Arena snapshot: arena-v3-api-complete-2026-09-16
• Voting cohort: 5cc0b0e569fdad5debdb78b8c36d5a6b7e387a82e28e2acfb6d1b13e96e8f326
• Private source revision: 727ef53ca1c5b37ac43e02beeb6a8332ec3dbda8
• Benchmark code: https://github.com/Thomcle/yodalingua-tts-benchmark
• Live Arena: https://huggingface.co/spaces/Thomcles/yodalingua-tts-arena
• Methodology: https://github.com/Thomcle/yodalingua-tts-benchmark/blob/main/docs/task-methodology-v3.md

The public export is deterministic for a fixed catalog, source revision, vote database snapshot, exporter version, and PyArrow version. File checksums and row counts are recorded in release.json.

Citation

@misc{yodalingua_tts_arena_english,
  author = {YodaLingua contributors},
  title = {YodaLingua English TTS Arena: Model Outputs and Human Preferences},
  year = {2026},
  url = {https://huggingface.co/datasets/Thomcles/YodaLingua-TTS-Arena-English}
}