Papers › PortaSpeech: Portable and High-Quality Generative Text-to-Speech

PortaSpeech: Portable and High-Quality Generative Text-to-Speech

30 Sep 2021NeurIPS 2021 12arXiv:2109.15166archive 2025-07-28

Yi Ren, Jinglin Liu, Zhou Zhao

Non-autoregressive text-to-speech (NAR-TTS) models such as FastSpeech 2 and Glow-TTS can synthesize high-quality speech from the given text in parallel. After analyzing two kinds of generative NAR-TTS models (VAE and normalizing flow), we find that: VAE is good at capturing the long-range semantics features (e.g., prosody) even with small model size but suffers from blurry and unnatural results; and normalizing flow is good at reconstructing the frequency bin-wise details but performs poorly when the number of model parameters is limited. Inspired by these observations, to generate diverse speech with natural details and rich prosody using a lightweight architecture, we propose PortaSpeech, a portable and high-quality generative text-to-speech model. Specifically, 1) to model both the prosody and mel-spectrogram details accurately, we adopt a lightweight VAE with an enhanced prior followed by a flow-based post-net with strong conditional inputs as the main architecture. 2) To further compress the model size and memory footprint, we introduce the grouped parameter sharing mechanism to the affine coupling layers in the post-net. 3) To improve the expressiveness of synthesized speech and reduce the dependency on accurate fine-grained alignment between text and speech, we propose a linguistic encoder with mixture alignment combining hard inter-word alignment and soft intra-word alignment, which explicitly extracts word-level semantic information. Experimental results show that PortaSpeech outperforms other TTS models in both voice quality and prosody modeling in terms of subjective and objective evaluation metrics, and shows only a slight performance degradation when reducing the model parameters to 6.7M (about 4x model size and 3x runtime memory compression ratio compared with FastSpeech 2). Our extensive ablation studies demonstrate that each design in PortaSpeech is effective.

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Code

Syntology Ran 6 of 12 code samples harvested from 2 repositories linked to this paper; 6 have no recorded run. Of those that ran: 3 ran · honoured contract; 2 ran · our draft was wrong; 1 ran · fixture could not drive it.

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natspeech/natspeech officialmentioned in paperpytorchMIT report
MoonInTheRiver/DiffSinger mentioned on GitHubpytorchMIT report
RayeRen/RayeRen mentioned on GitHub report
keonlee9420/PortaSpeech mentioned on GitHubpytorchMIT report

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12 samples harvested; 6 ran; 3 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

3ran · honoured contract
2ran · our draft was wrong
1ran · fixture could not drive it
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read_lexicon identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · 2b5c06c1ba593c3c · report

Tasks

Text to SpeechText-To-Speech SynthesisVocal Bursts Intensity PredictionWord Alignmenttext-to-speech

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Methods

Activation NormalizationAffine CouplingAttentionDense ConnectionsDropoutFastSpeech 2GLOWGlow-TTSInvertible 1x1 ConvolutionLayer NormalizationLinear LayerMulti-Head AttentionNormalizing FlowsPosition-Wise Feed-Forward LayerReLUResidual ConnectionSoftmax

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