mythicinfinity/libritts_opus
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---
license: cc-by-4.0
task_categories:
- text-to-speech
language:
- en
size_categories:
- 10K<n<100K
configs:
- config_name: dev
data_files:
- split: dev.clean
path: &id001
- data/dev/dev.clean/data-00000.parquet
- data/dev/dev.clean/data-00001.parquet
- data/dev/dev.clean/data-00002.parquet
- data/dev/dev.clean/data-00003.parquet
- data/dev/dev.clean/data-00004.parquet
- data/dev/dev.clean/data-00005.parquet
- data/dev/dev.clean/data-00006.parquet
- data/dev/dev.clean/data-00007.parquet
default: true
- config_name: clean
data_files:
- split: dev.clean
path: *id001
- split: test.clean
path: &id003
- data/clean/test.clean/data-00000.parquet
- data/clean/test.clean/data-00001.parquet
- data/clean/test.clean/data-00002.parquet
- data/clean/test.clean/data-00003.parquet
- data/clean/test.clean/data-00004.parquet
- data/clean/test.clean/data-00005.parquet
- data/clean/test.clean/data-00006.parquet
- data/clean/test.clean/data-00007.parquet
- split: train.clean.100
path: &id005
- data/clean/train.clean.100/data-00000.parquet
- data/clean/train.clean.100/data-00001.parquet
- data/clean/train.clean.100/data-00002.parquet
- data/clean/train.clean.100/data-00003.parquet
- data/clean/train.clean.100/data-00004.parquet
- data/clean/train.clean.100/data-00005.parquet
- data/clean/train.clean.100/data-00006.parquet
- data/clean/train.clean.100/data-00007.parquet
- split: train.clean.360
path: &id006
- data/clean/train.clean.360/data-00000.parquet
- data/clean/train.clean.360/data-00001.parquet
- data/clean/train.clean.360/data-00002.parquet
- data/clean/train.clean.360/data-00003.parquet
- data/clean/train.clean.360/data-00004.parquet
- data/clean/train.clean.360/data-00005.parquet
- data/clean/train.clean.360/data-00006.parquet
- data/clean/train.clean.360/data-00007.parquet
- config_name: other
data_files:
- split: dev.other
path: &id002
- data/other/dev.other/data-00000.parquet
- data/other/dev.other/data-00001.parquet
- data/other/dev.other/data-00002.parquet
- data/other/dev.other/data-00003.parquet
- data/other/dev.other/data-00004.parquet
- data/other/dev.other/data-00005.parquet
- data/other/dev.other/data-00006.parquet
- data/other/dev.other/data-00007.parquet
- split: test.other
path: &id004
- data/other/test.other/data-00000.parquet
- data/other/test.other/data-00001.parquet
- data/other/test.other/data-00002.parquet
- data/other/test.other/data-00003.parquet
- data/other/test.other/data-00004.parquet
- data/other/test.other/data-00005.parquet
- data/other/test.other/data-00006.parquet
- data/other/test.other/data-00007.parquet
- split: train.other.500
path: &id007
- data/other/train.other.500/data-00000.parquet
- data/other/train.other.500/data-00001.parquet
- data/other/train.other.500/data-00002.parquet
- data/other/train.other.500/data-00003.parquet
- data/other/train.other.500/data-00004.parquet
- data/other/train.other.500/data-00005.parquet
- data/other/train.other.500/data-00006.parquet
- data/other/train.other.500/data-00007.parquet
- config_name: all
data_files:
- split: dev.clean
path: *id001
- split: dev.other
path: *id002
- split: test.clean
path: *id003
- split: test.other
path: *id004
- split: train.clean.100
path: *id005
- split: train.clean.360
path: *id006
- split: train.other.500
path: *id007
---
# Dataset Card for LibriTTS
<!-- Provide a quick summary of the dataset. -->
LibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate,
prepared by Heiga Zen with the assistance of Google Speech and Google Brain team members. The LibriTTS corpus is
designed for TTS research. It is derived from the original materials (mp3 audio files from LibriVox and text files
from Project Gutenberg) of the LibriSpeech corpus.
## Overview
This is the LibriTTS dataset, adapted for the `datasets` library.
## Transcoding notes
- Source dataset: `mythicinfinity/libritts`
- Target codec: `opus 48kbps`
## Usage
### Splits
There are 7 splits (dots replace dashes from the original dataset, to comply with hf naming requirements):
- dev.clean
- dev.other
- test.clean
- test.other
- train.clean.100
- train.clean.360
- train.other.500
### Configurations
There are 3 configurations, each which limits the splits the `load_dataset()` function will download.
The default configuration is "all".
- "dev": only the "dev.clean" split (good for testing the dataset quickly)
- "clean": contains only "clean" splits
- "other": contains only "other" splits
- "all": contains only "all" splits
### Example
Loading the `clean` config with only the `train.clean.360` split.
```
load_dataset("blabble-io/libritts", "clean", split="train.clean.100")
```
Streaming is also supported.
```
load_dataset("blabble-io/libritts", streaming=True)
```
### Columns
```
{
"audio": datasets.Audio(sampling_rate=24_000),
"text_normalized": datasets.Value("string"),
"text_original": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"path": datasets.Value("string"),
"chapter_id": datasets.Value("string"),
"id": datasets.Value("string"),
}
```
### Example Row
```
{
'audio': {
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS/dev-clean/3081/166546/3081_166546_000028_000002.wav',
'array': ...,
'sampling_rate': 24000
},
'text_normalized': 'How quickly he disappeared!"',
'text_original': 'How quickly he disappeared!"',
'speaker_id': '3081',
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS/dev-clean/3081/166546/3081_166546_000028_000002.wav',
'chapter_id': '166546',
'id': '3081_166546_000028_000002'
}
```
## Dataset Details
### Dataset Description
- **License:** CC BY 4.0
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Homepage:** https://www.openslr.org/60/
- **Paper:** https://arxiv.org/abs/1904.02882
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```
@ARTICLE{Zen2019-kz,
title = "{LibriTTS}: A corpus derived from {LibriSpeech} for
text-to-speech",
author = "Zen, Heiga and Dang, Viet and Clark, Rob and Zhang, Yu and
Weiss, Ron J and Jia, Ye and Chen, Zhifeng and Wu, Yonghui",
abstract = "This paper introduces a new speech corpus called
``LibriTTS'' designed for text-to-speech use. It is derived
from the original audio and text materials of the
LibriSpeech corpus, which has been used for training and
evaluating automatic speech recognition systems. The new
corpus inherits desired properties of the LibriSpeech corpus
while addressing a number of issues which make LibriSpeech
less than ideal for text-to-speech work. The released corpus
consists of 585 hours of speech data at 24kHz sampling rate
from 2,456 speakers and the corresponding texts.
Experimental results show that neural end-to-end TTS models
trained from the LibriTTS corpus achieved above 4.0 in mean
opinion scores in naturalness in five out of six evaluation
speakers. The corpus is freely available for download from
http://www.openslr.org/60/.",
month = apr,
year = 2019,
copyright = "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
archivePrefix = "arXiv",
primaryClass = "cs.SD",
eprint = "1904.02882"
}
```
许可协议：知识共享署名4.0（CC BY 4.0）
task_categories:
- 文本转语音（text-to-speech）
language:
- 英语（en）
size_categories:
- 10K<n<100K
configs:
- config_name: dev
data_files:
- split: dev.clean
path: &id001
- data/dev/dev.clean/data-00000.parquet
- data/dev/dev.clean/data-00001.parquet
- data/dev/dev.clean/data-00002.parquet
- data/dev/dev.clean/data-00003.parquet
- data/dev/dev.clean/data-00004.parquet
- data/dev/dev.clean/data-00005.parquet
- data/dev/dev.clean/data-00006.parquet
- data/dev/dev.clean/data-00007.parquet
default: true
- config_name: clean
data_files:
- split: dev.clean
path: *id001
- split: test.clean
path: &id003
- data/clean/test.clean/data-00000.parquet
- data/clean/test.clean/data-00001.parquet
- data/clean/test.clean/data-00002.parquet
- data/clean/test.clean/data-00003.parquet
- data/clean/test.clean/data-00004.parquet
- data/clean/test.clean/data-00005.parquet
- data/clean/test.clean/data-00006.parquet
- data/clean/test.clean/data-00007.parquet
- split: train.clean.100
path: &id005
- data/clean/train.clean.100/data-00000.parquet
- data/clean/train.clean.100/data-00001.parquet
- data/clean/train.clean.100/data-00002.parquet
- data/clean/train.clean.100/data-00003.parquet
- data/clean/train.clean.100/data-00004.parquet
- data/clean/train.clean.100/data-00005.parquet
- data/clean/train.clean.100/data-00006.parquet
- data/clean/train.clean.100/data-00007.parquet
- split: train.clean.360
path: &id006
- data/clean/train.clean.360/data-00000.parquet
- data/clean/train.clean.360/data-00001.parquet
- data/clean/train.clean.360/data-00002.parquet
- data/clean/train.clean.360/data-00003.parquet
- data/clean/train.clean.360/data-00004.parquet
- data/clean/train.clean.360/data-00005.parquet
- data/clean/train.clean.360/data-00006.parquet
- data/clean/train.clean.360/data-00007.parquet
- config_name: other
data_files:
- split: dev.other
path: &id002
- data/other/dev.other/data-00000.parquet
- data/other/dev.other/data-00001.parquet
- data/other/dev.other/data-00002.parquet
- data/other/dev.other/data-00003.parquet
- data/other/dev.other/data-00004.parquet
- data/other/dev.other/data-00005.parquet
- data/other/dev.other/data-00006.parquet
- data/other/dev.other/data-00007.parquet
- split: test.other
path: &id004
- data/other/test.other/data-00000.parquet
- data/other/test.other/data-00001.parquet
- data/other/test.other/data-00002.parquet
- data/other/test.other/data-00003.parquet
- data/other/test.other/data-00004.parquet
- data/other/test.other/data-00005.parquet
- data/other/test.other/data-00006.parquet
- data/other/test.other/data-00007.parquet
- split: train.other.500
path: &id007
- data/other/train.other.500/data-00000.parquet
- data/other/train.other.500/data-00001.parquet
- data/other/train.other.500/data-00002.parquet
- data/other/train.other.500/data-00003.parquet
- data/other/train.other.500/data-00004.parquet
- data/other/train.other.500/data-00005.parquet
- data/other/train.other.500/data-00006.parquet
- data/other/train.other.500/data-00007.parquet
- config_name: all
data_files:
- split: dev.clean
path: *id001
- split: dev.other
path: *id002
- split: test.clean
path: *id003
- split: test.other
path: *id004
- split: train.clean.100
path: *id005
- split: train.clean.360
path: *id006
- split: train.other.500
path: *id007
# LibriTTS 数据集卡片
<!-- 简要概述数据集 -->
LibriTTS 是一个多说话人英语语料库，包含约585小时的24kHz采样率朗读英语语音，由Heiga Zen在Google Speech与Google Brain团队成员协助下制作完成。该语料库专为文本转语音（text-to-speech）研究设计，其数据源自LibriSpeech语料库的原始素材——LibriVox的MP3音频文件与古腾堡计划的文本文件。
## 概述
本数据集为适配`datasets`库改造的LibriTTS数据集。
## 转码说明
- 源数据集：`mythicinfinity/libritts`
- 目标编码格式：opus 48kbps
## 使用方法
### 分割集
共有7个分割集（为符合Hugging Face数据集库的命名规范，将原数据集的横杠替换为点号）：
- dev.clean
- dev.other
- test.clean
- test.other
- train.clean.100
- train.clean.360
- train.other.500
### 配置项
共有3种配置，每种配置限定`load_dataset()`函数将下载的分割集范围。默认配置为"all"。
- "dev"：仅包含"dev.clean"分割集（适合快速测试数据集）
- "clean"：仅包含所有"clean"类分割集
- "other"：仅包含所有"other"类分割集
- "all"：包含所有分割集
### 示例
加载`clean`配置下的`train.clean.100`分割集：
load_dataset("blabble-io/libritts", "clean", split="train.clean.100")
流式加载也受支持：
load_dataset("blabble-io/libritts", streaming=True)
### 数据字段
{
"audio": datasets.Audio(sampling_rate=24_000),
"text_normalized": datasets.Value("string"),
"text_original": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"path": datasets.Value("string"),
"chapter_id": datasets.Value("string"),
"id": datasets.Value("string"),
}
### 示例数据条目
{
'audio': {
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS/dev-clean/3081/166546/3081_166546_000028_000002.wav',
'array': ...,
'sampling_rate': 24000
},
'text_normalized': 'How quickly he disappeared!"',
'text_original': 'How quickly he disappeared!"',
'speaker_id': '3081',
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS/dev-clean/3081/166546/3081_166546_000028_000002.wav',
'chapter_id': '166546',
'id': '3081_166546_000028_000002'
}
## 数据集详情
### 数据集描述
- **许可协议**：知识共享署名4.0（CC BY 4.0）
### 数据集来源[可选]
<!-- 提供数据集的基础链接 -->
- **主页**：https://www.openslr.org/60/
- **论文**：https://arxiv.org/abs/1904.02882
## 引用信息
<!-- 若有介绍该数据集的论文或博客文章，需在此处提供APA及BibTeX格式的引用信息 -->
@ARTICLE{Zen2019-kz,
title = "{LibriTTS}: A corpus derived from {LibriSpeech} for
text-to-speech",
author = "Zen, Heiga and Dang, Viet and Clark, Rob and Zhang, Yu and
Weiss, Ron J and Jia, Ye and Chen, Zhifeng and Wu, Yonghui",
abstract = "This paper introduces a new speech corpus called
``LibriTTS'' designed for text-to-speech use. It is derived
from the original audio and text materials of the
LibriSpeech corpus, which has been used for training and
evaluating automatic speech recognition systems. The new
corpus inherits desired properties of the LibriSpeech corpus
while addressing a number of issues which make LibriSpeech
less than ideal for text-to-speech work. The released corpus
consists of 585 hours of speech data at 24kHz sampling rate
from 2,456 speakers and the corresponding texts.
Experimental results show that neural end-to-end TTS models
trained from the LibriTTS corpus achieved above 4.0 in mean
opinion scores in naturalness in five out of six evaluation
speakers. The corpus is freely available for download from
http://www.openslr.org/60/.",
month = apr,
year = 2019,
copyright = "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
archivePrefix = "arXiv",
primaryClass = "cs.SD",
eprint = "1904.02882"
}
提供机构：
mythicinfinity
