CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages

By Kyubyong Park, Thomas Mulc. 2019

117 citations recorded by the source corpus.

We describe our development of CSS10, a collection of single speaker speech datasets for ten languages. It is composed of short audio clips from LibriVox audiobooks and their aligned texts. To validate its quality we train two neural text-to-speech models on each dataset. Subsequently, we conduct Mean Opinion Score tests on the synthesized speech samples. We make our datasets, pre-trained models, and test resources publicly available. We hope they will be used for future speech tasks.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

A Survey on Neural Speech Synthesis (2021, neutral): “1 German India Corpus [103] 39 253 48 Multilingual M-AILABS [85] 1000 / 16 Multilingual MLS [272] 51K 6K 16 Multilingual CSS10 [258] 140 1 22.”
Textless Speech-to-Speech Translation on Real Data (2021, background): “Though the reference speech data is created synthetically in our study, we believe that collecting a maximum of 10-hr speech from a single speaker is reasonable in the real-world scenario as well (Ito and Johnson, 2017; Park and Mulc, 2019).”
Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations (2021, methodology): “To train NANSY on multi-language, we used CSS103 dataset [32].”
Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement (2020, methodology): “For cross-lingual adaptation, we used LJSpeech and InterMale for the English and German source models, respectively, and the single speaker datasets from CSS10 for the rest of the languages.”
One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech (2020, methodology): “Training setup: We used our cleaned CSS10 dataset for training; 64 randomly selected samples per language were reserved for validation and another 64 for testing.”
An Empirical Study on L2 Accents of Cross-lingual Text-to-Speech Systems via Vowel Space (2022, methodology): “We utilize part of the CSS10 datasets [17] along with the LJ Speech dataset [18] and our in-house datasets recorded by professional voice actors.”
Gaussian Lpcnet for Multisample Speech Synthesis (2020, unknown): “CSS10 [22] German Female 17h 20 4.”
HUI-Audio-Corpus-German: A high quality TTS dataset (2021, background): “CSS10 - German [13] is a collection of single speaker speech datasets in ten languages.”
Universal MelGAN: A Robust Neural Vocoder for High-Fidelity Waveform Generation in Multiple Domains (2020, unknown): “[26] K. Park and T. Mulc, “CSS10: A collection of single speaker speech datasets for 10 languages,” arXiv preprint arXiv:1903.11269, 2019.”
Review of end-to-end speech synthesis technology based on deep learning (2021, unknown): “Review of end-to-end speech synthesis technology based on deep learning 25 Table 6 Details of each corpus Corpus Language Number of speakers Hours Labeling method Sampling Rate (kHz) VCTK English 109 44 Characters 48 LJ Speech English 1 24 Original and stan- dardized characters and phonemes 22.05 Li”