Dataset assetClassic DatasetSpeech RecognitionSpeech Synthesis

VCTK Corpus

This repository provides full‑context label files for the VCTK corpus. These label files were created following the preprocessing steps in r9y9/deepvoice3_pytorch. The dataset includes both full and mono label files, detailing the segmentation and annotation format of the audio data.

Source

github

Created

Mar 8, 2020

Updated

Jun 23, 2022

Signals

330 views

Availability

Linked source ready

Overview

Dataset description and usage context

Dataset Overview

Dataset Name

Full‑context label for VCTK‑Corpus

Dataset Content

Provides full‑context label files for the VCTK‑Corpus.

Dataset Structure

├── lab
│   ├── full
│   │   ├── p225
│   │   │   ├── p225_001.lab
│   │   │   ├── p225_002.lab
│   │   │   ├── p225_003.lab
│   │   │   ├── p225_004.lab
│   │   │   ├── p225_005.lab
│   │   │   ...
│   ├── mono
│   │   ├── p225
│   │   │   ├── p225_001.lab
│   │   │   ├── p225_002.lab
│   │   │   ├── p225_003.lab
│   │   │   ├── p225_004.lab
│   │   │   ├── p225_005.lab
│   │   │   ...

Missing Files

lab/*/p315/*.lab (p315 lacks txt)
lab/mono/p295/p295_047.lab (alignment failed)
lab/mono/p305/p305_423.lab (alignment failed)
lab/mono/p317/p317_424.lab (alignment failed)
lab/mono/p345/p345_387.lab (alignment failed)

Label Format

Mono label

         0     850000 pau
    850000    2850000 pau
   2850000    3600000 p
   3600000    3900000 l
   3900000    6000000 iy
   6000000    8450000 z
   8450000    8600000 k
   8600000   11300000 ao
  11300000   11450000 l
  11450000   12800000 s
  12800000   13099999 t
  13099999   15800000 eh
  15800000   16050000 l
  16050000   17600000 ax
  17600000   20400000 pau

Full context label

         0     850000 x^x-pau+pau=p@x_x/A:0_0_0/B:x-x-x@x-x&x-x#x-x$x-x!x-x;x-x|x/C:0+0+0/D:0_0/E:x+x@x+x&x+x#x+x/F:0_0/G:0_0/H:x=x@1=1|0/I:0=0/J:4+3-1
    850000    2850000 x^pau-pau+p=l@x_x/A:0_0_0/B:x-x-x@x-x&x-x#x-x$x-x!x-x;x-x|x/C:1+1+4/D:0_0/E:x+x@x+x&x+x#x+x/F:content_1/G:0_0/H:x=x@1=1|0/I:4=3/J:4+3-1
   2850000    3600000 pau^pau-p+l=iy@1_4/A:0_0_0/B:1-1-4@1-1&1-4#1-3$1-4!0-1;0-1|iy/C:1+1+3/D:0_0/E:content+1@1+3&1+2#0+1/F:content_1/G:0_0/H:4=3@1=1|L-L%/I:0=0/J:4+3-1
   ...

References

Need downstream help?

Pair the dataset with AI analysis and content workflows.

Once the source passes your review, move straight into summarization, transformation, report drafting, or presentation generation with the JuheAI toolchain.

Explore AI studio