Rhasspy Larynx
High-quality, privacy-first neural text-to-speech for local edge computing.
Adversarial high-fidelity speech synthesis for low-latency production environments.
GAN-TTS (Generative Adversarial Network for Text-to-Speech) represents a paradigm shift in neural audio synthesis, originally pioneered by researchers at DeepMind. Unlike traditional autoregressive models like WaveNet, which generate audio samples sequentially, GAN-TTS utilizes a feed-forward generator paired with multiple discriminators to produce high-fidelity speech in parallel. This architecture significantly reduces inference latency, making it ideal for 2026 real-time applications such as interactive NPCs in gaming and edge-computing voice assistants. The framework operates by using a conditional GAN where the generator creates raw waveforms from linguistic features, while a suite of 'Random Window Discriminators' (RWD) evaluates the output across multiple time scales to ensure both spectral consistency and temporal realism. By 2026, GAN-TTS derivatives have become the industry standard for high-throughput pipelines where Mean Opinion Score (MOS) must be balanced with extreme computational efficiency. Its ability to handle long-form synthesis without the cumulative error drift seen in autoregressive models positions it as a critical component in the generative audio stack, especially for developers seeking to avoid the high costs and latency of cloud-based proprietary TTS APIs.
Uses a battery of discriminators that analyze the audio at different frequencies and time windows simultaneously.
High-quality, privacy-first neural text-to-speech for local edge computing.
A high-speed, fully convolutional neural architecture for multi-speaker text-to-speech synthesis.
Real-time neural text-to-speech architecture for massive-scale multi-speaker synthesis.
A Multilingual Single-Speaker Speech Corpus for High-Fidelity Text-to-Speech Synthesis.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A non-autoregressive feed-forward network architecture that synthesizes all audio samples in a single pass.
A specialized discriminator architecture that samples random segments of the generated audio to ensure global coherence.
Implementation of GLUs within the generator layers to better model the non-linearities of human speech.
Internal attention mechanisms that align text phonemes to temporal audio frames without external aligners.
Combines adversarial loss with multi-resolution STFT loss functions.
Architecture supports the injection of speaker embeddings to clone voices with minimal data.
Pre-recorded dialogue limits player interaction and consumes massive storage space.
Registry Updated:2/7/2026
Translating podcasts while maintaining the original speaker's voice across languages.
Cloud-based TTS introduces noticeable delay in user conversations.