FastSpeech 2

FastSpeech 2 is a state-of-the-art non-autoregressive text-to-speech (TTS) architecture developed by Microsoft Research. Unlike its predecessor, FastSpeech, which relied on a teacher-student distillation process, FastSpeech 2 utilizes a simplified training pipeline by directly predicting speech parameters from the ground truth. The model architecture is built on a Feed-Forward Transformer that enables parallelized mel-spectrogram generation, significantly reducing inference latency compared to autoregressive models like Tacotron 2. A critical technical innovation in FastSpeech 2 is the introduction of Variance Adapters, which explicitly predict duration, pitch, and energy. This allows for fine-grained control over prosody and addresses the 'one-to-many' mapping problem in TTS, where the same text can be spoken in various ways. In the 2026 market, FastSpeech 2 remains a foundational architecture for edge computing and real-time voice applications due to its computational efficiency and robust alignment stability. It is widely implemented via frameworks like ESPnet, Fairseq, and TensorSpeech, making it the industry standard for developers requiring high-fidelity voice output without the high overhead of diffusion-based or massive autoregressive models.