What datasets are best for training custom TTS voices?
Asked on Oct 13, 2025
Answer
When training custom Text-to-Speech (TTS) voices, selecting the right dataset is crucial for achieving high-quality and natural-sounding results. The ideal datasets should include diverse and high-quality audio recordings paired with accurate transcriptions. Commonly used datasets for TTS training include LibriSpeech, VCTK, and proprietary datasets from platforms like ElevenLabs or Play.ht.
Example Concept: A high-quality TTS dataset typically consists of thousands of hours of recorded speech with corresponding text transcriptions. The recordings should cover a wide range of phonetic contexts, speaking styles, and emotional tones to ensure the synthesized voice can handle various expressions and nuances. Additionally, datasets should be clean, with minimal background noise and consistent audio quality, to train robust and versatile TTS models.
Additional Comment:
- LibriSpeech is a popular open-source dataset derived from audiobooks, providing diverse speech samples.
- VCTK offers multi-speaker recordings, useful for training models with different accents and dialects.
- Ensure datasets are legally permissible for use in commercial TTS applications if needed.
- Consider augmenting datasets with synthetic data to cover underrepresented phonetic combinations.
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