NVIDIA introduces ‘Incremental FastPitch’, a variant of FastPitch, to enable real-time speech synthesis with lower latency and high-quality Mel chunks. The model incorporates chunk-based FFT blocks, training with receptive field-constrained chunk attention masks, and inference with fixed-size past model states. It offers comparable speech quality to parallel FastPitch but with significantly reduced latency.
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Incremental FastPitch: Revolutionizing Real-Time Speech Synthesis
Introduction
Parallel Text-to-Speech (TTS) models are commonly used for on-the-fly speech synthesis, but face challenges regarding incremental synthesis. Researchers from NVIDIA Corporation propose Incremental FastPitch, a variant of FastPitch, which can incrementally produce high-quality Mel chunks with lower latency for real-time speech synthesis.
Key Features
The proposed model improves the architecture with chunk-based FFT blocks, training with receptive field-constrained chunk attention masks, and inference with fixed-size past model states. It offers speech quality comparable to parallel FastPitch but with significantly lower latency, making it suitable for real-time speech applications.
Experimental Results
Experimental results show that Incremental FastPitch can produce speech quality comparable to parallel FastPitch, with significantly lower latency, contributing to improved performance. A visualized ablation study demonstrates that incremental FastPitch can generate Mel-spectrograms with almost no observable difference compared to parallel FastPitch, highlighting the effectiveness of the proposed model.
Conclusion
The Incremental FastPitch offers a faster and more controllable speech synthesis process, making it a promising approach for real-time applications.
For more details, check out the paper.
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