The following techniques are commonly used for fine-tuning text-to-speech (TTS) models to improve performance or adapt them to specific use cases:
Transfer Learning with Pre-trained Models Most modern TTS systems start with pre-trained models like Tacotron 2, FastSpeech, or VITS. Developers fine-tune these models on domain-specific data (e.g., medical terminology or regional accents) while keeping the base architecture intact. For example, retaining the encoder layers while retraining the decoder on custom audio-text pairs helps preserve linguistic understanding while adapting to new voice characteristics. This approach reduces data requirements compared to training from scratch.
Data Augmentation and Multi-Speaker Adaptation Augmenting limited training data with techniques like pitch shifting, time stretching, and background noise addition improves model robustness. For multi-speaker TTS, methods like Global Style Tokens (GSTs) or speaker embedding layers enable a single model to mimic multiple voices. Meta-learning approaches like MAML can also help models quickly adapt to new speakers with minimal samples.
Specialized Training Objectives Beyond standard mean squared error (MSE) loss, techniques include:
- Adversarial Training: Using GANs to make synthesized speech indistinguishable from real recordings
- Prosody Control: Adding duration/pitch predictors to explicitly model speech rhythm and intonation
- Knowledge Distillation: Compressing large TTS models into lighter versions while preserving quality
Developers often combine these methods – for instance, fine-tuning a pre-trained FastSpeech 2 model with adversarial training and speaker embeddings to create a multi-voice system for audiobook generation. The choice depends on factors like available data, target hardware constraints, and specific quality requirements for the deployment environment.