parler-tts/libritts_r_filtered

---
license: cc-by-4.0
task_categories:
- text-to-speech
- automatic-speech-recognition
language:
- en
size_categories:
- 10K<n<100K
dataset_info:
- config_name: clean
features:
- name: audio
dtype:
audio:
sampling_rate: 24000
- name: text_normalized
dtype: string
- name: text_original
dtype: string
- name: speaker_id
dtype: string
- name: path
dtype: string
- name: chapter_id
dtype: string
- name: id
dtype: string
splits:
- name: dev.clean
num_bytes: 1506311977.8882804
num_examples: 5589
- name: test.clean
num_bytes: 1432099582.6705585
num_examples: 4689
- name: train.clean.100
num_bytes: 8985618654.720787
num_examples: 32215
- name: train.clean.360
num_bytes: 31794257100.91056
num_examples: 112326
download_size: 44461321972
dataset_size: 43718287316.190186
- config_name: other
features:
- name: audio
dtype:
audio:
sampling_rate: 24000
- name: text_normalized
dtype: string
- name: text_original
dtype: string
- name: speaker_id
dtype: string
- name: path
dtype: string
- name: chapter_id
dtype: string
- name: id
dtype: string
splits:
- name: dev.other
num_bytes: 1042714063.4789225
num_examples: 4342
- name: test.other
num_bytes: 1061489621.2561874
num_examples: 4716
- name: train.other.500
num_bytes: 50718457351.73659
num_examples: 194626
download_size: 54153699917
dataset_size: 52822661036.471695
configs:
- config_name: clean
data_files:
- split: dev.clean
path: clean/dev.clean-*
- split: test.clean
path: clean/test.clean-*
- split: train.clean.100
path: clean/train.clean.100-*
- split: train.clean.360
path: clean/train.clean.360-*
- config_name: other
data_files:
- split: dev.other
path: other/dev.other-*
- split: test.other
path: other/test.other-*
- split: train.other.500
path: other/train.other.500-*
pretty_name: Filtered LibriTTS-R
---

# Dataset Card for Filtered LibriTTS-R

This is a filtered version of [LibriTTS-R](https://huggingface.co/datasets/mythicinfinity/libritts_r). It has been filtered based on two sources:
1. LibriTTS-R paper [1], which lists samples for which speech restoration have failed
2. LibriTTS-P [2] list of [excluded speakers](https://github.com/line/LibriTTS-P/blob/main/data/excluded_spk_list.txt) for which multiple speakers have been detected.

LibriTTS-R [1] is a sound quality improved version of the [LibriTTS corpus](http://www.openslr.org/60/) which is a multi-speaker English corpus of approximately
585 hours of read English speech at 24kHz sampling rate, published in 2019.

## Usage

### Example

Loading the `clean` config with only the `train.clean.360` split.
```py
from datasets import load_dataset

load_dataset("blabble-io/libritts_r", "clean", split="train.clean.100")
```

Streaming is also supported.
```py
from datasets import load_dataset

load_dataset("blabble-io/libritts_r", streaming=True)
```

### 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

### 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_R/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_R/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/141/
- **Paper:** https://arxiv.org/abs/2305.18802

## 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{Koizumi2023-hs,
title = "{LibriTTS-R}: A restored multi-speaker text-to-speech corpus",
author = "Koizumi, Yuma and Zen, Heiga and Karita, Shigeki and Ding,
Yifan and Yatabe, Kohei and Morioka, Nobuyuki and Bacchiani,
Michiel and Zhang, Yu and Han, Wei and Bapna, Ankur",
abstract = "This paper introduces a new speech dataset called
``LibriTTS-R'' designed for text-to-speech (TTS) use. It is
derived by applying speech restoration to the LibriTTS
corpus, which consists of 585 hours of speech data at 24 kHz
sampling rate from 2,456 speakers and the corresponding
texts. The constituent samples of LibriTTS-R are identical
to those of LibriTTS, with only the sound quality improved.
Experimental results show that the LibriTTS-R ground-truth
samples showed significantly improved sound quality compared
to those in LibriTTS. In addition, neural end-to-end TTS
trained with LibriTTS-R achieved speech naturalness on par
with that of the ground-truth samples. The corpus is freely
available for download from
\textbackslashurl\{http://www.openslr.org/141/\}.",
month = may,
year = 2023,
copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
archivePrefix = "arXiv",
primaryClass = "eess.AS",
eprint = "2305.18802"
}
```
```
@misc{kawamura2024librittspcorpusspeakingstyle,
title={LibriTTS-P: A Corpus with Speaking Style and Speaker Identity Prompts for Text-to-Speech and Style Captioning},
author={Masaya Kawamura and Ryuichi Yamamoto and Yuma Shirahata and Takuya Hasumi and Kentaro Tachibana},
year={2024},
eprint={2406.07969},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/2406.07969},
}
```