README.md

metadata

pretty_name: TTS Voice Direction Benchmark
language:
  - en
task_categories:
  - text-to-speech
tags:
  - text-to-speech
  - voice-direction
  - voice-cloning
  - benchmark

TTS Voice Direction Benchmark

🏆 Leaderboard | 🛠️ Evaluation Suite

TTS Voice Direction is a benchmark of 700 reference-conditioned speech generation tasks. It evaluates whether a text-to-speech model can preserve a reference speaker while following a natural-language direction that controls how a new transcript is performed.

The benchmark emphasizes practical voice acting beyond basic emotion control. It covers accent, acoustic delivery, vocal events, emotion, physiological state, communicative intent, role performance, multi-attribute composition, and temporal variation.

Dataset At A Glance

• 700 English voice-direction tasks
• 25 synthetic reference voices
• 9 fine-grained direction categories
• 3 macro categories
• One reference audio, reference transcript, direction, and target transcript per task
• Four-digit output IDs from 0001 through 0700

Taxonomy

Macro category	Fine-grained category	Cases
Foundational Speech Control	Accent	50
Foundational Speech Control	Acoustic Attributes	100
Foundational Speech Control	Vocal Events	50
Situational Voice Acting	Emotion	150
Situational Voice Acting	Physiological State	50
Situational Voice Acting	Communicative Intent	50
Situational Voice Acting	Role	100
Complex	Composition	100
Complex	Variation	50

Foundational cases target directly controllable speech properties. Situational cases describe a high-level state, purpose, or role and require the model to realize an appropriate performance. Complex cases combine multiple controls or request a perceptible change over time.

Reference Voices

All 25 reference voices are synthetic and sourced from the BreezeBlue Voice Library. Reference files are stored in reference_audio/ and named voice-01.wav through voice-25.wav.

The benchmark rotates directions across the reference set so that each model is tested on both instruction following and speaker preservation. Reference voice identity is evaluated separately from direction following.

Data Format

Each line in voice_direction_dataset.jsonl is one task:

{
  "description_id": "0001",
  "language": "en",
  "macro_category": "foundational_speech_control",
  "category": "accent",
  "voice_id": "voice-15",
  "ref_audio_path": "reference_audio/voice-15.wav",
  "ref_audio_text": "Reference transcript...",
  "description": "Use a moderate, consistent General American English accent throughout the line.",
  "transcript": "Target transcript..."
}

Field	Description
description_id	Stable four-digit task ID
language	Target transcript language; currently en
macro_category	One of the three aggregation groups
category	One of the nine fine-grained direction categories
voice_id	Stable reference-voice identifier
ref_audio_path	Reference audio path relative to the dataset file
ref_audio_text	Transcript of the reference audio
description	Natural-language voice-direction instruction
transcript	Text to synthesize

Running The Benchmark

For every record:

1. Condition the model on ref_audio_path and, when required by the model, ref_audio_text.
2. Use description as the voice-direction instruction.
3. Synthesize the exact transcript.
4. Save one audio file named after description_id, for example 0001.wav.

Do not use macro_category or category as additional model inputs. They are provided only for evaluation and analysis.

Evaluation

The benchmark reports two complementary metrics:

• Voice Direction Score (VDS) is a holistic 1-5 judge score for audible fulfillment of the requested direction, requested degree or timing, semantic preservation, and coherent execution.
• Speaker Similarity (SIM) is cosine similarity between WavLM-Large ECAPA embeddings of the generated and reference audio.

Both metrics are aggregated in the same hierarchy: reference voices are macro-averaged within each fine-grained category, fine-grained categories are equally averaged within each macro category, and the three macro-category scores are equally averaged into the overall score.

The self-contained judge prompts, evaluation scripts, speaker-embedding code, and metric documentation are available in the evaluation suite.