---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: speaker_id
    dtype: string
  - name: audio
    dtype: audio
  - name: mic_id
    dtype: string
  splits:
  - name: train
    num_bytes: 16540026180.2
    num_examples: 88156
  download_size: 17595288543
  dataset_size: 16540026180.2
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-to-speech
- automatic-speech-recognition
- text-to-audio
license: cc-by-4.0
language:
- en
size_categories:
- 10K<n<100K
---

# VCTK

This is a processed clone of the VCTK dataset with leading and trailing silence removed using Silero VAD. A fixed 25 ms of padding has been added to both ends of each audio clip to (hopefully) imrprove training and finetuning.

The original dataset is available at: https://datashare.ed.ac.uk/handle/10283/3443.


## Reproducing

This repository notably lacks a requirements.txt file. There's likely a missing dependency or two, but roughly:

```
pydub
tqdm
torch
torchaudio
python-dotenv
```

are the required python packages to clean the dataset.

### Steps

1. Download VCTK dataset (0.92) and extract it. This should net a `wav48_silence_trimmed` directory and a `txt` directory.
2. Run `process.py`, which will generate a `dataset` directory. This can be restarted if stopped.


### Licensing Information

Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))

### Citation Information

```bibtex
@inproceedings{Veaux2017CSTRVC,
    title        = {CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit},
    author       = {Christophe Veaux and Junichi Yamagishi and Kirsten MacDonald},
    year         = 2017
}
```