README.md

---
dataset_info:
- config_name: dutch
  features:
  - name: audio
    dtype: audio
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  - name: text
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  - name: transcript_wav2vec
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  - name: speaker_id
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- config_name: french
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  - name: wav_filesize
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  - name: text
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  - name: transcript_wav2vec
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  - name: levenshtein
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  - name: duration
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  - name: speaker_id
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  - name: levenshtein
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- config_name: italian
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- config_name: polish
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- config_name: portuguese
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- config_name: spanish
  features:
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  - name: wav_filesize
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  - name: text
    dtype: string
  - name: transcript_wav2vec
    dtype: string
  - name: levenshtein
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  - name: duration
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  - name: speaker_id
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configs:
- config_name: dutch
  data_files:
  - split: train
    path: dutch/train-*
  - split: dev
    path: dutch/dev-*
  - split: test
    path: dutch/test-*
- config_name: french
  data_files:
  - split: train
    path: french/train-*
  - split: dev
    path: french/dev-*
  - split: test
    path: french/test-*
- config_name: german
  data_files:
  - split: train
    path: german/train-*
  - split: dev
    path: german/dev-*
  - split: test
    path: german/test-*
- config_name: italian
  data_files:
  - split: train
    path: italian/train-*
  - split: dev
    path: italian/dev-*
  - split: test
    path: italian/test-*
- config_name: polish
  data_files:
  - split: train
    path: polish/train-*
  - split: dev
    path: polish/dev-*
  - split: test
    path: polish/test-*
- config_name: portuguese
  data_files:
  - split: train
    path: portuguese/train-*
  - split: dev
    path: portuguese/dev-*
  - split: test
    path: portuguese/test-*
- config_name: spanish
  data_files:
  - split: train
    path: spanish/train-*
  - split: dev
    path: spanish/dev-*
  - split: test
    path: spanish/test-*
license: cc-by-4.0
task_categories:
- text-to-speech
language:
- fr
- de
- nl
- pl
- pt
- es
- it
---


# Dataset Card for Filtred and CML-TTS


**This dataset is a filtred version of a [CML-TTS](https://huggingface.co/datasets/ylacombe/cml-tts) [1].** 

[CML-TTS](https://huggingface.co/datasets/ylacombe/cml-tts) [1] CML-TTS is a recursive acronym for CML-Multi-Lingual-TTS, a Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG). CML-TTS is a dataset comprising audiobooks sourced from the public domain books of Project Gutenberg, read by volunteers from the LibriVox project. The dataset includes recordings in Dutch, German, French, Italian, Polish, Portuguese, and Spanish, all at a sampling rate of 24kHz.

This dataset was used alongside the [LibriTTS-R English dataset](https://huggingface.co/datasets/blabble-io/libritts_r) and the [Non English subset of MLS](https://huggingface.co/datasets/facebook/multilingual_librispeech) to train [Parler-TTS Multilingual [Mini v1.1]((https://huggingface.co/ylacombe/p-m-e)).
A training recipe is available in [the Parler-TTS library](https://github.com/huggingface/parler-tts).


## Motivation

This dataset was filtered to remove problematic samples.
In the original dataset, some samples (especially short ones) had incomplete or incorrect transcriptions. To ensure quality, all rows with a Levenshtein similarity ratio below 0.9 were removed.

**Note on Levenshtein distance:** the Levenshtein distance measures how different two strings are by counting the minimum number of single-character edits (insertions, deletions, or substitutions) needed to transform one string into another.



## Usage

Here is an example on how to oad the `clean` config with only the `train.clean.360` split.

```py
from datasets import load_dataset

load_dataset("https://huggingface.co/datasets/PHBJT/cml-tts-cleaned-levenshtein", "french", split="train")
```


### Dataset Description

- **License:** CC BY 4.0

### Dataset Sources

- **Homepage:** https://www.openslr.org/141/
- **Paper:** https://arxiv.org/abs/2305.18802


@misc{oliveira2023cmltts,
      title={CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages}, 
      author={Frederico S. Oliveira and Edresson Casanova and Arnaldo Cândido Júnior and Anderson S. Soares and Arlindo R. Galvão Filho},
      year={2023},
      eprint={2306.10097},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}
```