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DV3 Attention Block

9 papers tagged archive 2025-07-28

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

DV3 Attention Block is an attention-based module used in the Deep Voice 3 architecture. It uses a dot-product attention mechanism. A query vector (the hidden states of the decoder) and the per-timestep key vectors from the encoder are used to compute attention weights. This then outputs a context vector computed as the weighted average of the value vectors.

Source: Deep Voice 3: Scaling Text-to-Speech with Convolutional...See Code · r9y9/deepvoice3_pytorch

Papers archive 2025-07-28

9 shown of 9, 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

9 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 Synthesis4
Text to Speech4
text-to-speech4
Domain Adaptation2
Unsupervised Domain Adaptation2
GPU1
Melody Extraction1
Retrieval1
Text-To-Speech Synthesis1

Usage over time archive 2025-07-28

Papers per year tagged with DV3 Attention Block: 2017 to 2022, peak 3 3 0 2017: 1 paper 2017 2018: 1 paper 2018 2019: 3 papers 2019 2020: 3 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 (9 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

Audio Model Blocks

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