What tools exist for training custom TTS models?

Training custom text-to-speech (TTS) models requires tools that balance flexibility, ease of use, and access to advanced architectures. Several open-source frameworks, cloud services, and specialized libraries exist to support this process. The choice depends on factors like customization needs, computational resources, and integration requirements.

Open-source frameworks like TensorFlow TTS and ESPnet are popular for building custom TTS models from scratch. TensorFlow TTS provides implementations of modern architectures like Tacotron 2, FastSpeech, and MelGAN, allowing developers to train models using their own datasets. It integrates with the TensorFlow ecosystem, making it easier to deploy models on diverse platforms. ESPnet, built on PyTorch, offers end-to-end pipelines for TTS and supports models like Transformer-TTS and VITS. It includes pre-trained models and scripts for data preprocessing, which can accelerate development. Another option is Coqui TTS, a PyTorch-based library focused on accessibility, with pre-trained models like Glow-TTS and tools for fine-tuning voices using small datasets. These frameworks are ideal for teams with technical expertise who need full control over model architecture and training workflows.

Cloud-based services like Google Cloud Text-to-Speech, Amazon Polly, and Microsoft Azure Speech offer TTS customization without requiring deep learning expertise. For example, Google’s Custom Voice allows users to upload recordings to train a unique voice model, though it requires approval and adheres to strict usage policies. Amazon Polly’s Neural TTS supports fine-tuning prosody and emphasis via SSML, while Azure provides a “Voice Lab” for limited voice adaptation. These services handle infrastructure and scaling but may lack flexibility compared to open-source tools. They are suitable for developers prioritizing quick deployment and minimal maintenance, though costs and data privacy considerations can be limiting.

For research-focused or high-performance use cases, tools like NVIDIA NeMo and PaddleSpeech provide optimized pipelines. NeMo offers modular components for TTS models like FastPitch and RadTTS, with multi-GPU training support. PaddleSpeech, part of PaddlePaddle, includes state-of-the-art models like VITS and integrates with speech recognition workflows. These libraries cater to developers needing advanced features like hybrid TTS/ASR systems or real-time synthesis. When choosing a tool, consider trade-offs: open-source frameworks offer control but require significant resources, while cloud services simplify deployment but limit customization.