Fine-tuning text-to-speech (TTS) models involves adapting pre-trained models to specific tasks, voices, or datasets. Here are key techniques:
1. Transfer Learning with Partial Retraining A common approach is to start with a pre-trained TTS model (e.g., Tacotron 2 or FastSpeech) and fine-tune it on a smaller target dataset. Instead of retraining all layers, developers often freeze early layers (which capture general speech patterns) and update only later layers to adapt to new data. For example, a model trained on generic English speech can be fine-tuned on medical terminology by updating the decoder layers while keeping the encoder fixed. This reduces overfitting and computational cost. Tools like ESPnet or PyTorch facilitate layer-specific parameter freezing during training.
2. Speaker Adaptation and Embeddings Speaker adaptation tailors a TTS model to mimic a specific voice. One method uses speaker embeddings, where a vector representing the target speaker’s voice is fed into the model during training. For instance, NVIDIA’s RAD-TTS allows conditioning on speaker IDs or embeddings. Another approach is few-shot adaptation, which fine-tunes the model using a small dataset (e.g., 5–10 minutes of target speaker audio). Advanced techniques like Meta-StyleSpeech enable style transfer by separating speaker identity and speech style, allowing adaptation with minimal data.
3. Vocoder-Specific Tuning and Data Augmentation TTS pipelines often include a vocoder (e.g., WaveGlow or HiFi-GAN) that converts spectrograms to audio. Fine-tuning the vocoder on target data improves audio quality for specific conditions. For example, a vocoder trained on studio recordings can be adapted to noisy environments using augmented data with background noise. Data augmentation—such as pitch shifting, time stretching, or adding reverb—helps models generalize better. Tools like TorchAudio provide built-in augmentation transforms, which are applied during fine-tuning to simulate diverse speaking conditions without requiring extensive new data.
These techniques balance computational efficiency, data availability, and output quality, making them practical choices for developers adapting TTS systems to specialized use cases.