| --- |
| 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](https://breezeblue.ai/benchmarks/tts-voice-direction) | 🛠️ [Evaluation Suite](https://github.com/breezeblue-ai/tts-voice-direction-benchmark) |
|
|
| 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: |
|
|
| ```json |
| { |
| "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](https://github.com/breezeblue-ai/tts-voice-direction-benchmark). |
|
|
