Natural language guidance of high-fidelity text-to-speech with synthetic annotations
Source
- Raw Markdown: paper_natural-language-guidance-tts-2024.md
- PDF: paper_natural-language-guidance-tts-2024.pdf
- Preprint: arXiv 2402.01912
- Official samples: text-description-to-speech.com
Core Claim
Large-scale speech generation can be controlled by natural-language descriptions of speaker identity, speaking style, and recording conditions when those descriptions are produced through scalable synthetic annotation.
Key Contributions
- Automatically labels a 45k-hour speech dataset with attributes such as gender, accent, speaking rate, pitch, signal-to-noise ratio, and reverberation.
- Converts structured acoustic labels into natural-language descriptions.
- Trains a speech language model conditioned on transcript text and style/recording descriptions.
- Shows that adding as little as 1% high-fidelity audio plus a stronger codec can substantially improve generated audio fidelity.
- Demonstrates speech generation across accents, prosodic styles, channel conditions, and acoustic conditions using one model.
Method Notes
The model uses text as a control interface for non-lexical audio attributes. The transcript and the descriptive prompt play different roles: one specifies what is said, the other specifies how it should sound.
For the wiki’s time-series interests, the portable lesson is data construction. Dense sensor or audio streams often lack human-written metadata, so automatic labeling plus language rephrasing can make instruction-style conditioning scalable.
Evidence And Results
The paper reports human/listening-study improvements over contemporary description-conditioned TTS, and argues that the quality gain comes from both the high-fidelity data slice and use of a state-of-the-art codec.
Alex Notes
- Important / read.
- Alex highlighted three takeaways: how to generate labeled data for audio, how 1% high-fidelity data can radically improve quality, and how to prompt voice generation.
Limitations
- Focused on English speech corpora and TTS, not general audio or time-series forecasting.
- Automatic labels can encode classifier bias or lose subtle speaker/style information.
- Natural-language control is not the same as a physically grounded action or intervention channel.
Foundation TSFM Relevance
| Agenda slot | Verdict | Evidence | Missing pieces |
|---|---|---|---|
| Context interface | adjacent | Separates transcript content from natural-language descriptions of speaker, style, accent, and recording conditions. | Audio-control prompts are not a general channel/system context interface for numeric streams. |
| Generation and editing | partially closes | Demonstrates controllable high-fidelity temporal generation using synthetic labels and text descriptions. | Does not forecast, edit, or generate general multivariate numeric time series. |
| Data diversity and synthetic annotation | adjacent | Scales metadata by deriving labels for 45k hours of speech and rephrasing structured attributes into text. | Label noise and English audiobook scope limit transfer claims. |
Links Into The Wiki
- Foundation Time-Series Model Research Agenda
- Unified Multimodal Models
- Synthetic Data For Time Series
- Context-Aided Forecasting
Open Questions
- Can the same synthetic-annotation loop create useful text context for sensor, telemetry, or biomedical time series?
- How much high-quality data is needed to steer other temporal generators?
- How should prompt fields be separated into content, style, environment, and control inputs?