--- dataset_info: - config_name: dutch features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 98269862937.51833 num_examples: 231177 - name: dev num_bytes: 745162483.6791213 num_examples: 1641 - name: test num_bytes: 797726105.9099672 num_examples: 1661 download_size: 101597337669 dataset_size: 99812751527.10742 - config_name: french features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 43465714817.56579 num_examples: 99997 - name: dev num_bytes: 1052062781.1487837 num_examples: 2293 - name: test num_bytes: 1086922137.0305457 num_examples: 2378 download_size: 45319112381 dataset_size: 45604699735.74512 - config_name: german features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 243597781025.5856 num_examples: 527484 - name: dev num_bytes: 1666941056.5423663 num_examples: 3628 - name: test num_bytes: 1602978607.4473794 num_examples: 3592 download_size: 247092068354 dataset_size: 246867700689.57535 - config_name: italian features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 19924022474.291084 num_examples: 47133 - name: dev num_bytes: 394008759.92521244 num_examples: 786 - name: test num_bytes: 470119421.61416894 num_examples: 958 download_size: 20512119431 dataset_size: 20788150655.830467 - config_name: polish features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 6113517251.351246 num_examples: 15136 - name: dev num_bytes: 235417382.99062133 num_examples: 564 - name: test num_bytes: 272458802.487715 num_examples: 603 download_size: 6528857087 dataset_size: 6621393436.829582 - config_name: portuguese features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 10141245922.630423 num_examples: 25732 - name: dev num_bytes: 192600490.04761904 num_examples: 352 - name: test num_bytes: 130106396.77409406 num_examples: 265 download_size: 10770507545 dataset_size: 10463952809.452135 - config_name: spanish features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 68818227269.88977 num_examples: 153150 - name: dev num_bytes: 1032288709.7028614 num_examples: 1897 - name: test num_bytes: 922532713.3814805 num_examples: 1662 download_size: 67248870262 dataset_size: 70773048692.97412 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} } ```