Tacotron 2 Statistics [2026 Updated]

Tacotron 2 achieved a Mean Opinion Score of 4.53 in 2017, reaching within 1.09% of human speech quality and establishing the benchmark for neural text-to-speech synthesis. NVIDIA’s PyTorch implementation garnered over 5,300 GitHub stars and 1,400 forks, while the global TTS market reached $3.87 billion in 2024 with projections to hit $7.28 billion by 2030.

Neural and AI-powered voice technologies captured 67.90% of market revenue in 2024, validating the architectural paradigm that Tacotron 2 pioneered for end-to-end speech synthesis.

Tacotron 2 Key Statistics

• Tacotron 2 recorded a Mean Opinion Score of 4.53, placing it just 0.05 points below professionally recorded human speech at 4.58
• NVIDIA’s official PyTorch implementation accumulated 5,300+ GitHub stars and 1,400+ repository forks as of 2026
• The model demonstrated 18.6% improvement over Tacotron 1, jumping from a 3.82 to 4.53 MOS score
• Tacotron 2 achieves 7x faster than real-time synthesis on RTX 2080Ti hardware configurations
• The global TTS market reached $3.87 billion in 2024 with a projected 12.89% CAGR through 2030

Tacotron 2 Performance Benchmarks

Tacotron 2 marked the first neural TTS system to approach near-human quality when Google researchers published the architecture in December 2017. The model processes 80-channel mel filterbanks spanning 125 Hz to 7.6 kHz at a 22,050 Hz sample rate.

The feature prediction network generates spectrograms at 9.65 per second on NVIDIA Titan XP hardware. Frame computation occurs at 12.5 millisecond intervals, producing 80 frames per second of synthesized speech output.

Tacotron 2 Developer Adoption Metrics

NVIDIA’s official PyTorch implementation demonstrates substantial open-source engagement with 5,300+ stars positioning it among the most popular TTS repositories on GitHub. The repository accumulated 1,400+ forks from developers adapting the model for multilingual applications.

The development team contributed 134 commits across the repository lifespan, with 8 core contributors maintaining the codebase. The community opened 193 issues and submitted 26 pull requests, reflecting active engagement with the open-source implementation.

Researchers developed Tacotron 2 implementations across multiple frameworks including TensorFlow, PyTorch, and Coqui TTS. Community-developed models extended support to over 10 languages including Arabic, Korean, Chinese, and Vietnamese speech synthesis.

Tacotron 2 Training Requirements

The LJSpeech dataset serves as the primary benchmark for Tacotron 2 development, comprising approximately 24 hours of single-speaker recordings across 13,100 labeled audio clips. Training typically requires 7-10 days on limited GPU configurations without optimization.

NVIDIA’s implementation supports mixed precision training with dynamic loss scaling, achieving 2.0x faster training for Tacotron 2 and 3.1x faster training for WaveGlow compared to standard precision approaches. The WaveGlow vocoder utilizes 512 residual channels in its coupling layer configuration.

Tacotron 2 Architecture Components

The encoder utilizes three convolutional layers with 512 filters each in a 5×1 filter shape, followed by a bidirectional LSTM network for character embedding extraction. The decoder employs two LSTM layers for mel-spectrogram prediction with location-sensitive attention mechanisms.

Location-sensitive attention uses a kernel size of 32 for precise alignment between input text sequences and output mel-spectrogram frames. The post-net applies five convolutional filters with 512 filters in a 5×1 shape with batch normalization, producing 80-dimensional mel-scale representations.

Text-to-Speech Market Growth

The global TTS market reached $3.87 billion in 2024 with projections to hit $7.28 billion by 2030, representing a 12.89% compound annual growth rate. Neural and AI-powered voice technologies captured 67.90% of market revenue in 2024, growing at a 15.60% CAGR.

Software segments maintained dominance with 76.30% market share, while cloud-based deployment represented 63.80% of implementations. North America led regional markets with 37.20% share, driven by enterprise adoption of voice-enabled applications.

Tacotron 2 Comparative Analysis

Tacotron 2 demonstrated a 0.71-point MOS improvement over its predecessor Tacotron 1, representing an 18.6% enhancement in perceived speech naturalness. Research conducted in 2024 confirmed the model’s continued superiority in low-resource environments, achieving a MOS of 4.25 ± 0.17 at 95% confidence interval.

When combined with WaveNet vocoder, Tacotron 2 reached a 4.53 MOS score compared to Deep Voice 2 + WaveNet at 3.53 and Deep Voice 1 at 2.67. However, subsequent non-autoregressive models like FastSpeech demonstrated 270x speedup in mel-spectrogram generation compared to Tacotron 2’s autoregressive approach.

Tacotron 2 Research Impact

A 13-member Google Brain and Research team published the Tacotron 2 paper in December 2017 as an arXiv preprint, with conference proceedings appearing at ICASSP 2018. The architecture introduced WaveNet conditioning on mel-spectrogram predictions, establishing the dominant pattern for neural TTS systems.

Pre-trained models became available through PyTorch Hub and Hugging Face distribution channels, enabling rapid deployment for researchers and developers. The model’s influence extended beyond the original implementation, spawning derivative frameworks and multilingual adaptations across the speech synthesis community.

FAQ

Citations:

Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions – arXiv

NVIDIA Tacotron 2 PyTorch Implementation – GitHub

Tacotron 2 and WaveGlow for PyTorch – NVIDIA NGC Catalog

Text-to-Speech Market Analysis – Mordor Intelligence