CSTR VCTK
Audio
NLP
|...
许可协议: CC BY 4.0

Overview

This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive. The newspaper texts were taken from Herald Glasgow, with permission from Herald & Times Group. Each speaker has a different set of the newspaper texts selected based a greedy algorithm that increases the contextual and phonetic coverage. The rainbow passage and elicitation paragraph are the same for all speakers. All recordings were converted into 16 bits, were downsampled to 48 kHz, and were manually end-pointed. This corpus was originally aimed for HMM-based text-to-speech synthesis systems, especially for speaker-adaptive HMM-based speech synthesis that uses average voice models trained on multiple speakers and speaker adaptation technologies. This corpus is also suitable for DNN-based multi-speaker text-to-speech synthesis systems and neural waveform modeling. Please note while text files containing transcripts of the speech are provided for 109 of the 110 recordings, in the '/txt' folder, the 'p315' text was lost due to a hard disk error.

Data Collection

All speech data was recorded using an identical recording setup: an omni-directional microphone (DPA 4035) and a small diaphragm condenser microphone with very wide bandwidth (Sennheiser MKH 800), 96kHz sampling frequency at 24 bits and in a hemi-anechoic chamber of the University of Edinburgh. (However, two speakers, p280 and p315 had technical issues of the audio recordings using MKH 800).

Citation

Please use the following citation when referencing the dataset:

@ARTICLE{vctk,
  title={CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit},
  author={Yamagishi, Junichi and Veaux, Christophe and MacDonald, Kirsten.},
  url={https://doi.org/10.7488/ds/2645}
}

License

CC BY 4.0

数据概要
数据格式
Audio,
数据量
--
文件大小
10.94GB
发布方
CSTR (The Centre for Speech Technology Research)
CSTR is an interdisciplinary research centre linking Informatics and Linguistics and English Language.
数据集反馈
| 80 | 数据量 -- | 大小 10.94GB
CSTR VCTK
Audio
NLP
许可协议: CC BY 4.0

Overview

This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive. The newspaper texts were taken from Herald Glasgow, with permission from Herald & Times Group. Each speaker has a different set of the newspaper texts selected based a greedy algorithm that increases the contextual and phonetic coverage. The rainbow passage and elicitation paragraph are the same for all speakers. All recordings were converted into 16 bits, were downsampled to 48 kHz, and were manually end-pointed. This corpus was originally aimed for HMM-based text-to-speech synthesis systems, especially for speaker-adaptive HMM-based speech synthesis that uses average voice models trained on multiple speakers and speaker adaptation technologies. This corpus is also suitable for DNN-based multi-speaker text-to-speech synthesis systems and neural waveform modeling. Please note while text files containing transcripts of the speech are provided for 109 of the 110 recordings, in the '/txt' folder, the 'p315' text was lost due to a hard disk error.

Data Collection

All speech data was recorded using an identical recording setup: an omni-directional microphone (DPA 4035) and a small diaphragm condenser microphone with very wide bandwidth (Sennheiser MKH 800), 96kHz sampling frequency at 24 bits and in a hemi-anechoic chamber of the University of Edinburgh. (However, two speakers, p280 and p315 had technical issues of the audio recordings using MKH 800).

Citation

Please use the following citation when referencing the dataset:

@ARTICLE{vctk,
  title={CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit},
  author={Yamagishi, Junichi and Veaux, Christophe and MacDonald, Kirsten.},
  url={https://doi.org/10.7488/ds/2645}
}

License

CC BY 4.0

数据集反馈
0
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