README.md

---
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}, 
}
```