Datasetpublic

philgzl/clarity

Clarity Speech Corpus This is a mirror of the Clarity Speech Corpus. The original files were converted from WAV to Opus to reduce the size and accelerate streaming. Sampling rate: 48 kHz (resampled from 44.1 kHz to support Opus format) Channels: 1 Format: Opus Duration: 9 hours, 11352 utterances License: CC BY 4.0 Source: https://doi.org/10.17866/rd.salford.16918180 Paper: Dataset of British English speech recordings for psychoacoustics and speech processing research: The… See the full description on the dataset page: https://huggingface.co/datasets/philgzl/clarity.

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Dataset Card

Clarity Speech Corpus

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

—Sampling rate: 48 kHz (resampled from 44.1 kHz to support Opus format)
—Channels: 1
—Format: Opus
—Duration: 9 hours, 11352 utterances
—License: CC BY 4.0
—Source: https://doi.org/10.17866/rd.salford.16918180
—Paper: Dataset of British English speech recordings for psychoacoustics and speech processing research: The Clarity Speech Corpus

Usage

python

import io

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

for item in load_dataset(
    "philgzl/clarity",
    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

bibtex

@article{cox2022clarity,
  title = {Dataset of {British} {English} speech recordings for psychoacoustics and speech processing research: {The} {Clarity} {Speech} {Corpus}},
  author = {Simone Graetzer and Michael A. Akeroyd and Jon Barker and Trevor J. Cox and John F. Culling and Graham Naylor and Eszter Porter and Rhoddy Viveros-Mu{\~{n}}oz},
  journal = {Data Br.},
  volume = {41},
  pages = {107951},
  year = {2022},
}