Papers › Natural language guidance of high-fidelity text-to-speech with synthetic annotations

Natural language guidance of high-fidelity text-to-speech with synthetic annotations

2 Feb 2024arXiv:2402.01912archive 2025-07-28

Dan Lyth, Simon King

Text-to-speech models trained on large-scale datasets have demonstrated impressive in-context learning capabilities and naturalness. However, control of speaker identity and style in these models typically requires conditioning on reference speech recordings, limiting creative applications. Alternatively, natural language prompting of speaker identity and style has demonstrated promising results and provides an intuitive method of control. However, reliance on human-labeled descriptions prevents scaling to large datasets. Our work bridges the gap between these two approaches. We propose a scalable method for labeling various aspects of speaker identity, style, and recording conditions. We then apply this method to a 45k hour dataset, which we use to train a speech language model. Furthermore, we propose simple methods for increasing audio fidelity, significantly outperforming recent work despite relying entirely on found data. Our results demonstrate high-fidelity speech generation in a diverse range of accents, prosodic styles, channel conditions, and acoustic conditions, all accomplished with a single model and intuitive natural language conditioning. Audio samples can be heard at https://text-description-to-speech.com/.

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Syntology Ran 5 of 6 code samples harvested from 2 repositories linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · honoured contract; 3 ran · our draft was wrong; 1 ran · fixture could not drive it.

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huggingface/dataspeech mentioned on GitHubpytorchMIT report
huggingface/parler-tts mentioned on GitHubpytorch report
ylacombe/dataspeech mentioned on GitHubpytorch report

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6 samples harvested; 5 ran; 1 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
3ran · our draft was wrong
1ran · fixture could not drive it
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apply_delay_pattern_mask huggingface/parler-tts/parler_tts/modeling_parler_tts.py community (archive-listed) ran · honoured contract Apache-2.0 (permissive) · 233eb885ada9ec14 · report
bins_to_text ylacombe/dataspeech/scripts/metadata_to_text.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 1b43b176ddd9bf74 · report
build_delay_pattern_mask huggingface/parler-tts/parler_tts/modeling_parler_tts.py community (archive-listed) ran · our draft was wrong Apache-2.0 (permissive) · 41a684b3ecdb898c · report
sorted_checkpoints ylacombe/dataspeech/scripts/run_prompt_creation.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · 30d59078e7ba4ce5 · report
speaker_level_relative_to_gender ylacombe/dataspeech/scripts/metadata_to_text.py community (archive-listed) unverified MIT (permissive) · 904d40e4c83a2cf1 · report
repeat_kv identical code first harvested elsewhere ran · fixture could not drive it fingerprinted licence of this copy not recorded · 3c76e52815c5401d · report

Tasks

In-Context LearningLanguage ModelingLanguage ModellingText to Speechtext-to-speech

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