Methods › Sequential › Sequence To Sequence Models › ClariNet

ClariNet

7 papers tagged archive 2025-07-28

Introduced by Wei Ping et al. in ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

ClariNet is an end-to-end text-to-speech architecture. Unlike previous TTS systems which use text-to-spectogram models with a separate waveform synthesizer (vocoder), ClariNet is a text-to-wave architecture that is fully convolutional and can be trained from scratch. In ClariNet, the WaveNet module is conditioned on the hidden states instead of the mel-spectogram. The architecture is otherwise based on Deep Voice 3.

PaperSource

Papers archive 2025-07-28

7 shown of 7, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Speech Synthesis3
Domain Adaptation2
Text to Speech2
Unsupervised Domain Adaptation2
text-to-speech2
Melody Extraction1
Retrieval1
Text-To-Speech Synthesis1

Usage over time archive 2025-07-28

Papers per year tagged with ClariNet: 2018 to 2022, peak 3 3 0 2018: 1 paper 2018 2019: 3 papers 2019 2020: 2 papers 2020 2021: 0 papers 2021 2022: 1 paper 2022
Papers per year the archive tags with this method, by the paper's archive date (7 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Sequence To Sequence ModelsText-to-Speech Models

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