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9 results for “text-to-speech”
Ressources for End-to-End French Text-to-Speech Blizzard challenge
<p>Here are 289 chapters of 5 audiobooks from Librivox (51:12) read by Nadine Eckert-Boulet (NEB):</p> <ol> <li>Madame Bovary (MB) by Gustave Flaubert (FL) - 3 volumes, 35 chapters<br>(original <a href="https://librivox.org/madame-bovary-french-by-gustave-flaubert">wavs</a>; <a href="https://www.gutenberg.org/cache/epub/14155/pg14155.txt">text</a>)</li> <li>Les mystères de Paris (LMP) by Eugene Sue (ES) - 4 volumes, 83 chapters (original <a href="https://librivox.org/les-mysteres-de-paris-tome-1-by-eugene-sue">wavs1</a>, <a href="https://librivox.org/les-mysteres-de-paris-tome-2-by-eugene-sue/">wavs2</a>,<a href="https://librivox.org/les-mysteres-de-paris-tome-3-by-eugene-sue"> wavs3</a>; <a href="https://www.gutenberg.org/cache/epub/18921/pg18921.txt">text1</a>, <a href="https://www.gutenberg.org/cache/epub/18922/pg18922.txt">text2</a>, <a href="https://www.gutenberg.org/cache/epub/18923/pg18923.txt">text3</a>)</li> <li>Les tribulations d'un chinois en Chine (TCC) by Jules Verne (JV) - 1 volume, 22 chapters (original <a href="https://librivox.org/les-tribulations-dun-chinois-en-chine-by-jules-verne">wavs</a>; <a href="https://www.gutenberg.org/cache/epub/14162/pg14162.txt">text</a>)</li> <li>La fille du pirate (LFDP) by Henri Émile Chevalier (EC) - 7 volumes, 121 chapters (original <a href="https://librivox.org/la-fille-du-pirate-by-henri-emile-chevalier">wavs</a>, <a href="https://www.gutenberg.org/cache/epub/18403/pg18403.txt">text)</a></li> <li>La vampire (VAMP) by Paul Féval (PF) - 1 volume, 28 chapters (original <a href="https://librivox.org/la-vampire-by-feval-paul-henry-corentin">wavs</a>, <a href="https://www.gutenberg.org/cache/epub/10053/pg10053.txt">text</a>)</li> </ol> <p>and</p> <p>2515 utterances (2:03) read by another female French speaker Aurélie Derbier (AD):</p> <ol> <li>1608 utterances extracted from various books (DIVERS_BOOK_AD*)</li> <li>907 transcripts of the sessions of the French parliament (DIVERS_PARL_01*)</li> </ol> <p>We recently added three speakers from Librivox/Litteratureaudio:</p> <ol> <li>Ezwa (EZWA): L'épouvante by Maurice Level (original <a href="https://librivox.org/lepouvante-by-maurice-level-1010/">wavs</a>; <a href="https://www.gutenberg.org/cache/epub/17794/pg17794.txt">text</a>) - 11 chapters - 4869 utterances> 03:16</li> <li>Pauline Latournerie (PL): Le pédagogue n'aime pas les enfants by Henri Roorda (<a href="https://librivox.org/le-pedagogue-naime-pas-les-enfants-by-henri-roorda/">original wavs</a>; <a href="https://ebooks-bnr.com/ebooks/pdf4/roorda_le_pedagogue_n_aime_pas_les_enfants.pdf">text</a>) - 6 chapters - 1320 utterances> 01:17</li> <li>Jean-Luc Fischer (JLF): L’Affaire Charles Dexter Ward by Howard Phillips Lovecraft (<a href="https://www.litteratureaudio.com/livre-audio-gratuit-mp3/howard-phillips-lovecraft-laffaire-charles-dexter-ward.html">original wavs</a>; <a href="https://www.litteratureaudio.com/textes/H_P_Lovecraft_L_Affaire_CDW.pdf">text</a>) - 16 chapters - 1823 utterances> 02:37</li> </ol> <p>Each .wav file (sampled at 22050Hz) corresponds to one entire chapter. The format of the filenames is:<br>{author's acronym}_{book's acronym}_{reader's acronym}_{volume's number}_{chapter's number}</p> <p>The NEB_train.csv file gives text and phonetic alignments (essentially for MB and LMP) for utterances in 4 fields separated by '|':<br>{filename}|{start_ms}|{end_ms}|{text or phonetic content}. Most utterances are separated by at least a pause of 400ms. The intervals [start_ms:end_ms] comprise leading and trailing silences of 130ms (since wavs are entire chapters, these silences are "true" ambient silences). Same for AD_train.csv.</p> <p>When phonetic alignment has been performed, 2 additional fields have been added: {aligned phones}|{durations in ms}. Each input character or phone has a corresponding aligned phone and a duration. Note that all aligned utterances start and end with an aligned phone of 130ms. The set of aligned phones comprises:</p> <ul> <li>The set of input phones</li> <li>The silence: '__'</li> <li>The symbol '_' for silent characters, e.g. "chat" is aligned with 's^ _ a _'</li> <li>29 combined aligned phones ('a&i', 'a&j', 'b&q', 'd&q','d&z', 'd&z^', 'f&q', 'g&q', 'g&z', 'j&i', 'j&u', 'j&q', 'i&j', 'k&q', 'k&s', 'k&s&q', 'l&q', 'm&q', 'n&q', 'r&w', 'r&q', 's&q', 't&q', 't&s', 't&s^', 'w&a', 'z&q', 'p&q') that align to only one character, e.g. "expatrier" is aligned with 'e^ k&s p a t r i&j e _'</li> </ul> <p>Text is in UTF8. '«»','¬', '~','""','()','[]' are respectively used for speaking quotes, turn switches, three dots, quoted expression, aside quotes, notes. Because of rare occurrences, 'ö' has been transcribed as 'oe'. Paragraphs (two consecutive carriage returns in the original text) are cued by a special character '§'. It usually ends an utterance but could be used within an utterance if its associated pause is too short.</p> <p>When available, phonetic content is given per word in curly brackets '{}'. We use 39 phonetic symbols:</p> <ul> <li><strong>oral vowels</strong>: a (f<strong><em>a</em></strong>), e (f<em><strong>ée</strong></em>), e^ (f<em><strong>ait</strong></em>), x (f<em><strong>eu</strong></em>), x^ (c<em><strong>oeu</strong></em>r), i (r<em><strong>iz</strong></em>), y (f<em><strong>ut</strong></em>), u (f<em><strong>ou</strong></em>), o (f<em><strong>aux</strong></em>), o^ (p<strong><em>o</em></strong>rc)</li> <li><strong>schwa</strong>: q (gag<strong><em>e</em></strong>)</li> <li><strong>nasal vowels</strong>: a~ (r<strong><em>an</em></strong>g), e~ (f<em><strong>in</strong></em>), x~ (<strong><em>un</em></strong>), o~ (r<em><strong>on</strong></em>d)</li> <li><strong>semi-vowels</strong>: h (h<em><strong>u</strong></em>it), w (<strong><em>ou</em></strong>ate), j (h<em><strong>i</strong></em>er)</li> <li><strong>consonants</strong>: p (<em><strong>p</strong></em>as), t (<strong><em>t</em></strong>as), k (<em><strong>c</strong></em>as), b (<strong><em>b</em></strong>as), d (<em><strong>d</strong></em>os), g (<em><strong>g</strong></em>ars), f (<em><strong>f</strong></em>aux), s (<strong><em>s</em></strong>ot) , s^ (<strong><em>ch</em></strong>at), v (<strong><em>v</em></strong>u), z (<strong><em>z</em></strong>ut), z^ (<em><strong>j</strong></em>us), r (<strong><em>r</em></strong>iz), l (<em><strong>l</strong></em>a), m (<strong><em>m</em></strong>a), n (<strong><em>n</em></strong>on), n~ (oi<strong><em>gn</em></strong>on), ng (campi<em><strong>ng</strong></em>)</li> </ul> <p> </p>
TIMIT-TTS: a Text-to-Speech Dataset for Synthetic Speech Detection
<p>With the rapid development of deep learning techniques, the generation and counterfeiting of multimedia material are becoming increasingly straightforward to perform. At the same time, sharing fake content on the web has become so simple that malicious users can create unpleasant situations with minimal effort. Also, forged media are getting more and more complex, with manipulated videos (e.g., deepfakes where both the visual and audio contents can be counterfeited) that are taking the scene over still images.<br> The multimedia forensic community has addressed the possible threats that this situation could imply by developing detectors that verify the authenticity of multimedia objects. However, the vast majority of these tools only analyze one modality at a time.<br> This was not a problem as long as still images were considered the most widely edited media, but now, since manipulated videos are becoming customary, performing monomodal analyses could be reductive. Nonetheless, there is a lack in the literature regarding multimodal detectors (systems that consider both audio and video components). This is due to the difficulty of developing them but also to the scarsity of datasets containing forged multimodal data to train and test the designed algorithms.</p> <p>In this paper we focus on the generation of an audio-visual deepfake dataset.<br> First, we present a general pipeline for synthesizing speech deepfake content from a given real or fake video, facilitating the creation of counterfeit multimodal material. The proposed method uses Text-to-Speech (TTS) and Dynamic Time Warping (DTW) techniques to achieve realistic speech tracks. Then, we use the pipeline to generate and release TIMIT-TTS, a synthetic speech dataset containing the most cutting-edge methods in the TTS field. This can be used as a standalone audio dataset, or combined with DeepfakeTIMIT and VidTIMIT video datasets to perform multimodal research. Finally, we present numerous experiments to benchmark the proposed dataset in both monomodal (i.e., audio) and multimodal (i.e., audio and video) conditions.<br> This highlights the need for multimodal forensic detectors and more multimodal deepfake data.</p> <ul> <li>For the initial version of TIMIT-TTS <strong>v1.0</strong> <ul> <li>Arxiv: https://arxiv.org/abs/2209.08000</li> <li>TIMIT-TTS Database v1.0: https://zenodo.org/record/6560159</li> </ul> </li> </ul>
Lada: Ukrainian High-Quality Female Text-to-Speech Dataset
<p>The dataset has high-quality data recorded in a professional studio. </p> <p>Archives with a <strong>trimmed</strong> tag are having removed silence (aligned) using <a href="https://github.com/proger/uk">https://github.com/proger/uk</a> </p> <p><strong>Features</strong></p> <ul> <li>Quality: high</li> <li>Duration: 10h37m</li> <li>Audio formats: OPUS/WAV</li> <li>Text format: JSONL (a <code>metadata.jsonl</code> file)</li> <li>Frequency: 16000/22050/48000 Hz</li> </ul>
Preprocessed Data and Pretrained Models for Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings
<p>This is preprocessed data and pretrained models from two of our papers:</p> <p>"Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings," by Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, and Junichi Yamagishi. (ICASSP 2020)<br> <a href="https://arxiv.org/abs/1910.10838">https://arxiv.org/abs/1910.10838</a></p> <p> "Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis," by Erica Cooper, Xin Wang, Yi Zhao, Yusuke Yasuda, and Junichi Yamagishi. (arXiv) <a href="https://arxiv.org/abs/2011.04839">https://arxiv.org/abs/2011.04839</a></p> <p>This data is meant to be used with our open-source implementation, which can be found here: https://github.com/nii-yamagishilab/multi-speaker-tacotron</p> <p>More information about the directory structure and how to use the data can be found in the READMEs on GitHub.</p>
Synthetic Göttingen Sentence Test material created with a text-to-speech system
<p>The speech material of the German Göttingen Sentence Test [1] was synthesized using a commercial text-to-speech system (Acapela Cloud Service). More details will be found in [2].</p> <p>Files:</p> <p>goesa_synth_female.zip<br> contains all 200 sentences with a synthetic female voice and the corresponding speech adjusted noise, which was generated by superimposing the speech material 30 times according to [3].</p> <p>goesa_synth_male.zip<br> contains all 200 sentences with a synthetic male voice and the corresponding speech adjusted noise, which was generated by superimposing the speech material 30 times according to [3].</p> <p> </p>
Training a Text-to-Speech System for Dialectal Arabic with a Focus on the Iraqi Dialect
<p>This research introduces a novel approach to Text-to-Speech (TTS) synthesis, focusing on the phonetic complexities of Arabic dialects, with particular emphasis on the Iraqi dialect. While existing Arabic speech corpora provide substantial coverage of Modern Standard Arabic (MSA), they fall short in capturing the phonetic richness of regional dialects. To address this gap, we utilized Nawar Halabi's Arabic Speech Corpus as a base dataset and enriched it with custom-recorded samples of the Iraqi dialect, incorporating distinctive phonemes such as گ ,ڤ ,پ ,چ ,ۆ ,ڵ ,ێ, and using the Tatweel character (ـ) as a vowel. Our approach, powered by the FastPitch model and a customized phonetiser, successfully synthesized the Iraqi dialect while also demonstrating adaptability to other Arabic dialects, including Egyptian, Khaliji, Syrian, and more. The results of this research signify a promising advancement in Arabic TTS technology, expanding its scope to authentically represent the diverse linguistic landscape of the Arabic-speaking world.</p>
SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis
<p>This is the public release of the Samsung Open Mean Opinion Scores (SOMOS) dataset for the evaluation of neural text-to-speech (TTS) synthesis, which consists of audio files generated with a public domain voice from trained TTS models based on bibliography, and numbers assigned to each audio as quality (naturalness) evaluations by several crowdsourced listeners.<br><br><strong>Description</strong><br><br>The SOMOS dataset contains 20,000 synthetic utterances (wavs), 100 natural utterances and 374,955 naturalness evaluations (human-assigned scores in the range 1-5). The synthetic utterances are single-speaker, generated by training several Tacotron-like acoustic models and an LPCNet vocoder on the LJ Speech voice public dataset. 2,000 text sentences were synthesized, selected from Blizzard Challenge texts of years 2007-2016, the LJ Speech corpus as well as Wikipedia and general domain data from the Internet.<br>Naturalness evaluations were collected via crowdsourcing a listening test on Amazon Mechanical Turk in the US, GB and CA locales. The records of listening test participants (workers) are fully anonymized. Statistics on the reliability of the scores assigned by the workers are also included, generated through processing the scores and validation controls per submission page.</p> <p>To listen to audio samples of the dataset, please see <a href="https://innoetics.github.io/publications/somos-dataset/index.html">our Github page</a>.</p> <p>The dataset release comes with a carefully designed train-validation-test split (70%-15%-15%) with unseen systems, listeners and texts, which can be used for experimentation on MOS prediction.</p> <p><em>This version also contains the necessary resources to obtain the transcripts corresponding to all dataset audios.</em></p> <p><strong>Terms of use</strong></p> <ul> <li>The dataset may be used for <strong>research</strong> purposes only, for <strong>non-commercial</strong> purposes only, and may be distributed with the same terms.</li> <li>Every time you produce research that has used this dataset, please <strong>cite </strong>the dataset appropriately.</li> </ul> <p>Cite as:</p> <pre><code>@inproceedings{maniati22_interspeech, author={Georgia Maniati and Alexandra Vioni and Nikolaos Ellinas and Karolos Nikitaras and Konstantinos Klapsas and June Sig Sung and Gunu Jho and Aimilios Chalamandaris and Pirros Tsiakoulis}, title={{SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis}}, year=2022, booktitle={Proc. Interspeech 2022}, pages={2388--2392}, doi={10.21437/Interspeech.2022-10922} } </code></pre> <p><br><strong>References of resources & models used</strong></p> <p>Voice & synthesized texts:<br>K. Ito and L. Johnson, “The LJ Speech Dataset,” https://keithito.com/LJ-Speech-Dataset/, 2017.</p> <p>Vocoder:<br>J.-M. Valin and J. Skoglund, “LPCNet: Improving neural speech synthesis through linear prediction,” in Proc. ICASSP, 2019.<br>R. Vipperla, S. Park, K. Choo, S. Ishtiaq, K. Min, S. Bhattacharya, A. Mehrotra, A. G. C. P. Ramos, and N. D. Lane, “Bunched lpcnet: Vocoder for low-cost neural text-to-speech systems,” in Proc. Interspeech, 2020.</p> <p>Acoustic models:<br>N. Ellinas, G. Vamvoukakis, K. Markopoulos, A. Chalamandaris, G. Maniati, P. Kakoulidis, S. Raptis, J. S. Sung, H. Park, and P. Tsiakoulis, “High quality streaming speech synthesis with low, sentence-length-independent latency,” in Proc. Interspeech, 2020.<br>Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio et al., “Tacotron: Towards End-to-End Speech Synthesis,” in Proc. Interspeech, 2017.<br>J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan et al., “Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions,” in Proc. ICASSP, 2018.<br>J. Shen, Y. Jia, M. Chrzanowski, Y. Zhang, I. Elias, H. Zen, and Y. Wu, “Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling,” arXiv preprint arXiv:2010.04301, 2020.<br>M. Honnibal and M. Johnson, “An Improved Non-monotonic Transition System for Dependency Parsing,” in Proc. EMNLP, 2015.<br>M. Dominguez, P. L. Rohrer, and J. Soler-Company, “PyToBI: A Toolkit for ToBI Labeling Under Python,” in Proc. Interspeech, 2019.<br>Y. Zou, S. Liu, X. Yin, H. Lin, C. Wang, H. Zhang, and Z. Ma, “Fine-grained prosody modeling in neural speech synthesis using ToBI representation,” in Proc. Interspeech, 2021.<br>K. Klapsas, N. Ellinas, J. S. Sung, H. Park, and S. Raptis, “WordLevel Style Control for Expressive, Non-attentive Speech Synthesis,” in Proc. SPECOM, 2021.<br>T. Raitio, R. Rasipuram, and D. Castellani, “Controllable neural text-to-speech synthesis using intuitive prosodic features,” in Proc. Interspeech, 2020.</p> <p>Synthesized texts from the Blizzard Challenges 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2016:<br>M. Fraser and S. King, "The Blizzard Challenge 2007," in Proc. SSW6, 2007.<br>V. Karaiskos, S. King, R. A. Clark, and C. Mayo, "The Blizzard Challenge 2008," in Proc. Blizzard Challenge Workshop, 2008.<br>A. W. Black, S. King, and K. Tokuda, "The Blizzard Challenge 2009," in Proc. Blizzard Challenge, 2009.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2010," 2010.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2011," 2011.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2012," 2012.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2013," 2013.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2016," 2016.</p> <p><strong>Contact</strong></p> <p>Alexandra Vioni - a.vioni@samsung.com</p> <ul> <li>If you have any questions or comments about the dataset, please feel free to write to us.</li> <li>We are interested in knowing if you find our dataset useful! If you use our dataset, please email us and tell us about your research.</li> </ul>
Personalization of Visual Characters and Text-to-Speech (TTS) to Support Learning in Students With Reading Difficulties
ClinicalTrials.gov study NCT06947512. IPD Sharing: YES. Countries: 1. Publications: 0.
Synthetic phrase-based test material created with a text-to-speech system
<p>New speech material consisting of phrases was synthesized using a commercial text-to-speech system (Acapela Cloud Service). More details will be found in [1].</p> <p>Files:</p> <p>syntheticPhrases.zip<br>contains all 772 phrases with a synthetic female voice and the corresponding speech adjusted noise, which was generated by superimposing the speech material 30 times according to [2].</p>
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