How Yuedu's TTS Platform Automates High‑Quality Audiobook Production

Yuedu TTS Technology Series – Exploration

The audiobook market has grown over 35% annually for the past three years and remains in an expansion phase. Platforms need to boost listener immersion and produce high‑quality audio books efficiently.

Platform Architecture

The TTS audiobook production platform consists of three main stages: content preprocessing, content structuring, and post‑processing.

2.1 Content Preprocessing

Preprocessing prepares raw novel text for synthesis by extracting style, correcting errors, and building a role graph.

2.1.1 Style Extraction

A domain‑specific pre‑trained model tags novels with style labels (e.g., Xianxia, fantasy) and automatically matches appropriate voice timbres and background sounds.

2.1.2 Text Correction

Because web novels often contain typos and redundant symbols, a custom correction model trained on web‑novel data reduces errors dramatically. The module also rewrites punctuation‑only dialogue symbols and filters non‑narrative content such as footnotes and notices.

2.1.3 Role Graph Construction

Role mining creates a knowledge graph of characters, their attributes, and relationships. Named‑entity recognition based on a BERT model extracts character names and genders, while dependency parsing extracts personality traits from comments. Relationship extraction handles aliases, sibling, and romantic links, enabling consistent voice assignment.

2.2 Content Structuring

Structured content transforms raw text into a script‑like format, identifying scenes, characters, actions, emotions, and special‑effect cues.

2.2.1 Chapter Structuring

Each chapter is split into dialogue blocks, converting narrative prose into a screenplay style (e.g., narrator, character lines).

2.2.2 Emotion and Effect Scene Recognition

A BERT‑based classifier assigns fine‑grained emotions (joy, anger, sadness, fear, surprise) to each sentence. Over 30 categories and 500+ sound effects are matched to textual cues, allowing automatic insertion of background sounds.

2.2.3 Effect Sound Alignment

Effect audio is normalized (bitrate, loudness, length) and precisely aligned with the corresponding text segment, using character‑level timing or ASR‑based segmentation for dynamic voice styles.

2.3 Post‑Processing and Online Deployment

After generating the AI‑driven storyboard (AI‑drawn script), the platform integrates with front‑end pages for review, allows manual adjustments of character voices, emotions, and effects, and submits synthesis jobs to third‑party TTS engines. Completed audio is merged with background sounds, stored in COS, and distributed to downstream channels. The workflow supports batch updates of voice attributes and prioritizes premium content for better listener experience.

Conclusion

Yuedu's TTS platform combines role mining, voice matching, chapter structuring, emotion and effect recognition, and a full‑stack production pipeline to automate the creation of high‑quality, multi‑character audiobooks, dramatically improving production efficiency while maintaining commercial‑grade audio quality.