README.md

---
license: cc-by-4.0
task_categories:
- text-to-speech
language:
- en
- fr
- de
- es
- it
- pl
- nl
- pt
tags:
- speech
- synthetic
size_categories:
- 100M<n<1B
configs:
- config_name: english
  data_files:
  - split: train
    path: english/**/train*.tar.gz
  - split: test
    path: english/**/test*.tar.gz
  - split: valid
    path: english/**/dev*.tar.gz
- config_name: german
  data_files:
  - split: train
    path: german/train*.tar.gz
  - split: test
    path: german/test*.tar.gz
  - split: valid
    path: german/dev*.tar.gz
- config_name: french
  data_files:
  - split: train
    path: french/train*.tar.gz
  - split: test
    path: french/test*.tar.gz
  - split: valid
    path: french/dev*.tar.gz
- config_name: spanish
  data_files:
  - split: train
    path: spanish/train*.tar.gz
  - split: test
    path: spanish/test*.tar.gz
  - split: valid
    path: spanish/dev*.tar.gz
- config_name: portuguese
  data_files:
  - split: train
    path: portuguese/train*.tar.gz
  - split: test
    path: portuguese/test*.tar.gz
  - split: valid
    path: portuguese/dev*.tar.gz
- config_name: italian
  data_files:
  - split: train
    path: italian/train*.tar.gz
  - split: test
    path: italian/test*.tar.gz
  - split: valid
    path: italian/dev*.tar.gz
- config_name: polish
  data_files:
  - split: train
    path: polish/train*.tar.gz
  - split: test
    path: polish/test*.tar.gz
  - split: valid
    path: polish/dev*.tar.gz
- config_name: dutch
  data_files:
  - split: train
    path: dutch/train*.tar.gz
  - split: test
    path: dutch/test*.tar.gz
  - split: valid
    path: dutch/dev*.tar.gz
---
# MLS-Sidon

## Overview
This dataset is a **cleansed version of Multilingual LibriSpeech (MLS)** with **Sidon** speech restoration mode for **Speech Synthesis** and **Spoken Language Modeling**.  

The dataset is provided in **[WebDataset](https://github.com/webdataset/webdataset) format** for efficient large-scale training.  

- **Source**: [Multilingual LibriSpeech](https://www.openslr.org/94/)
- **Languages**: English, German, French, Spanish, Italian, Polish, Dutch, Portuguese  
- **Format**: WebDataset (`.tar` shards)  
- **License**: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)

---

## Dataset Structure
Each sample in the dataset contains:  

- **`flac`** — audio file (48 kHz, single channel)  
- **`metadata.json`** *(optional)* — metadata including language, speaker ID, and original MLS reference  

Example (inside a `.tar` shard):  
```

000001.flac
000001.metadata.json
000002.flac
000002.metadata.json
...

````

---

## How to Use

### With 🤗 Datasets
You can load the WebDataset directly with Hugging Face’s `datasets` library:

```python
import datasets
from IPython.display import Audio
from huggingface_hub import hf_hub_download
import yaml

base_url = "https://huggingface.co/datasets/sarulab-speech/mls_sidon/resolve/main/"
language = 'english'
split = 'test'
data_file_path = hf_hub_download(repo_id="sarulab-speech/mls_sidon", repo_type="dataset", filename="paths.yaml")
paths = yaml.load(open(data_file_path, "r"), Loader=yaml.FullLoader)

ds = datasets.load_dataset("webdataset", data_files=[base_url + p for p in paths['english'][split]],streaming=True)['train']

sample = next(iter(ds))
audio = sample['flac']
print(sample['metadata.json'])
Audio(audio['array'], rate=audio['sampling_rate'])
````

Replace `language` with the language (e.g., `english`, `german`).



---

## Citation

If you use this dataset, please cite Sidon and the original MLS paper:

```
@misc{nakata2025sidonfastrobustopensource,
      title={Sidon: Fast and Robust Open-Source Multilingual Speech Restoration for Large-scale Dataset Cleansing}, 
      author={Wataru Nakata and Yuki Saito and Yota Ueda and Hiroshi Saruwatari},
      year={2025},
      eprint={2509.17052},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2509.17052}, 
}
```


```
@inproceedings{pratap2020mls,
  title     = {MLS: A Large-Scale Multilingual Dataset for Speech Research},
  author    = {Pratap, Vineel and Xu, Qiantong and Sriram, Anuroop and others},
  booktitle = {Interspeech},
  year      = {2020}
}
```


---

## License

This dataset is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).

---

## Acknowledgements

* **Original data**: [Multilingual LibriSpeech (MLS)](https://www.openslr.org/94/)