VCTK

This is a mirror of the VCTK Corpus. The original files were converted from FLAC to Opus to reduce the size and accelerate streaming.

• Sampling rate: 48 kHz
• Channels: 1
• Format: Opus
• Splits:
  • train_mic1: 90 speakers, 33.6 hours, 35987 utterances
  • train_mic2: 90 speakers, 33.6 hours, 35987 utterances
  • val_mic1: 10 random speakers unseen during training: p238, p244, p254, p263, p265, p272, p288, p294, p305, and p335. 4.0 hours, 4179 utterances.
  • val_mic2: Same speakers as val_mic1. 4.0 hours, 4179 utterances.
  • test_mic1: 10 random speakers unseen during validation and training: p230, p260, p276, p278, p280, p292, p295, p341, p347, p376. 3.5 hours, 3707 utterances
  • test_mic2: Same speakers as test_mic1. 3.5 hours, 3707 utterances
• License: CC BY 4.0
• Source: https://datashare.ed.ac.uk/handle/10283/3443
• Paper: The Voice Bank Corpus: Design, Collection and Data Analysis of a Large Regional Accent Speech Database

Usage

import io

import soundfile as sf
from datasets import Features, Value, load_dataset

for item in load_dataset(
    "philgzl/vctk",
    split="train_mic1",
    streaming=True,
    features=Features({"audio": Value("binary"), "name": Value("string")}),
):
    print(item["name"])
    buffer = io.BytesIO(item["audio"])
    x, fs = sf.read(buffer)
    # do stuff...

Citation

@inproceedings{veaux2013voice,
  title = {The {Voice} {Bank} corpus: {Design}, collection and data analysis of a large regional accent speech database},
  author = {Veaux, Christophe and Yamagishi, Junichi and King, Simon},
  booktitle = {Proc. O-COCOSDA/CASLRE},
  pages = {1--4},
  year = {2013},
}