Speech synthesis is the task of generating speech from some other modality like text, lip movements etc.
Please note that the leaderboards here are not really comparable between studies - as they use mean opinion score as a metric and collect different samples from Amazon Mechnical Turk.
This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain. Source: The LJ Speech Dataset Image Source: https://keithito.com/LJ-Speech-Dataset/ Audio Source: https://keithito.com/LJ-Speech-Dataset/
LibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate, prepared by Heiga Zen with the assistance of Google Speech and Google Brain team members. The LibriTTS corpus is designed for TTS research. It is derived from the original materials (mp3 audio files from LibriVox and text files from Project Gutenberg) of the LibriSpeech corpus. The main differences from the LibriSpeech corpus are listed below: - The audio files are at 24kHz sampling rate. - The speech is split at sentence breaks. - Both original and normalized texts are included. - Contextual information (e.g., neighbouring sentences) can be extracted. - Utterances with significant background noise are excluded.
THCHS-30 is a free Chinese speech database THCHS-30 that can be used to build a full-fledged Chinese speech recognition system. Source: THCHS-30 : A Free Chinese Speech Corpus
A collection of single speaker speech datasets for ten languages. It is composed of short audio clips from LibriVox audiobooks and their aligned texts. Source: CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages
PromptSpeech is a dataset that consists of speech and the corresponding prompts. We synthesize speech with 5 different style factors (gender, pitch, speaking speed, volume, and emotion) from a commercial TTS API. The emotion factor has 5 categories and the gender factor has 2 categories. Source: PromptTTS: Controllable Text-to-Speech with Text Descriptions Image Source: https://arxiv.org/pdf/2211.12171v1.pdf
The SOMOS dataset is a large-scale mean opinion scores (MOS) dataset consisting of solely neural text-to-speech (TTS) samples. It can be employed to train automatic MOS prediction systems focused on the assessment of modern synthesizers, and can stimulate advancements in acoustic model evaluation. It consists of 20K synthetic utterances of the LJ Speech voice, a public domain speech dataset which is a common benchmark for building neural acoustic models and vocoders. Utterances are generated from 200 TTS systems including vanilla neural acoustic models as well as models which allow prosodic variations.
JSUT Corpus is a free large-scale speech corpus that can be shared between academic institutions and commercial companies has an important role. However, such a corpus for Japanese speech synthesis does not exist. Source: JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis
The TON_IoT datasets are new generations of Internet of Things (IoT) and Industrial IoT (IIoT) datasets for evaluating the fidelity and efficiency of different cybersecurity applications based on Artificial Intelligence (AI). The datasets have been called ‘ToN_IoT’ as they include heterogeneous data sources collected from Telemetry datasets of IoT and IIoT sensors, Operating systems datasets of Windows 7 and 10 as well as Ubuntu 14 and 18 TLS and Network traffic datasets. The datasets were collected from a realistic and large-scale network designed at the IoT Lab of the UNSW Canberra Cyber, the School of Engineering and Information technology (SEIT), UNSW Canberra @ the Australian Defence Force Academy (ADFA). The datasets were gathered in a parallel processing to collect several normal and cyber-attack events from IoT networks. A new testbed was developed at the IoT lab to connect many virtual machine, physical systems, hacking platforms, cloud and fog platforms, IoT and IIoT sensors to mimic the complexity and scalability of industrial IoT and Industry 4.0 networks. Different hacking techniques, such as DoS, DDoS and ransomware against, were launched against web applications, IoT gateways and computer systems across the IIoT network.
The EMODB database is the freely available German emotional database. The database is created by the Institute of Communication Science, Technical University, Berlin, Germany. Ten professional speakers (five males and five females) participated in data recording. The database contains a total of 535 utterances. The EMODB database comprises of seven emotions: 1) anger; 2) boredom; 3) anxiety; 4) happiness; 5) sadness; 6) disgust; and 7) neutral. The data was recorded at a 48-kHz sampling rate and then down-sampled to 16-kHz. Citation: Felix Burkhardt, Astrid Paeschke, Miriam Rolfes, Walter Sendlmeier und Benjamin Weiss A Database of German Emotional Speech Proceedings Interspeech 2005, Lissabon, Portugal
The English data for voice building was obtained, prepared and provided the the challenge by Lessac Technologies Inc., having originally came from the publishers Voice Factory International Inc. It comprises speech from one female professional narrator & actress, Catherine ‘Bobbie’ Byers, reading the text of a collection of classic novels. These had been divided by the publishers of the original audiobooks into a number of genres, such as “Classic Novels”, “Women’s Classics”, “Young Readers” and so on.
The data set contains several speakers. The 5 largest are listed individually, the rest are summarized as other. All audio files have a sampling rate of 44.1kHz. For each speaker, there is a clean variant in addition to the full data set, where the quality is even higher. Furthermore, there are various statistics. The dataset can also be used for automatic speech recognition (ASR) if audio files are converted to 16 kHz.
A special corpus of Indian languages covering 13 major languages of India. It comprises of 10000+ spoken sentences/utterances each of mono and English recorded by both Male and Female native speakers. Speech waveform files are available in .wav format along with the corresponding text. We hope that these recordings will be useful for researchers and speech technologists working on synthesis and recognition. You can request zip archives of the entire database here.
The Tongue and Lips (TaL) corpus is a multi-speaker corpus of ultrasound images of the tongue and video images of lips. This corpus contains synchronised imaging data of extraoral (lips) and intraoral (tongue) articulators from 82 native speakers of English. The TaL corpus consists of two datasets: * TaL1 is a single-speaker dataset containing data of one professional voice talent, a male native speaker of English, over six recording sessions. * TaL80 is a multi-speaker dataset contains recording sessions of 81 native speakers of English without voice talent experience. Each speaker was recording over a single recording session. Image source: https://ultrasuite.github.io/data/tal_corpus/
JVS-MuSiC is a Japanese multispeaker singing-voice corpus called "JVS-MuSiC" with the aim to analyze and synthesize a variety of voices. The corpus consists of 100 singers' recordings of the same song, Katatsumuri, which is a Japanese children's song. It also includes another song that is different for each singer.
RUSLAN is a Russian spoken language corpus for text-to-speech task. RUSLAN contains 22,200 audio samples with text annotations – more than 31 hours of high-quality speech of one person – being one of the largest annotated Russian corpus in terms of speech duration for a single speaker. Source: RUSLAN: Russian Spoken Language Corpus for Speech Synthesis
Tilde MODEL Corpus is a multilingual corpora for European languages – particularly focused on the smaller languages. The collected resources have been cleaned, aligned, and formatted into a corpora standard TMX format useable for developing new Language technology products and services. It contains over 10M segments of multilingual open data. The data has been collected from sites allowing free use and reuse of its content, as well as from Public Sector web sites. Source: Tilde MODEL - Multilingual Open Data for EU Languages
VocBench is a framework that benchmark the performance of state-of-the art neural vocoders. VocBench uses a systematic study to evaluate different neural vocoders in a shared environment that enables a fair comparison between them.
A database containing high sampling rate recordings of a single speaker reading sentences in Brazilian Portuguese with neutral voice, along with the corresponding text corpus. Intended for speech synthesis and automatic speech recognition applications, the dataset contains text extracted from a popular Brazilian news TV program, totalling roughly 20 h of audio spoken by a trained individual in a controlled environment. The text was normalized in the recording process and special textual occurrences (e.g. acronyms, numbers, foreign names etc.) were replaced by their phonetic translation to a readable text in Portuguese. There are no noticeable accidental sounds and background noise has been kept to a minimum in all audio samples. Intended for: TTS ASR Speech Enhancement Voice Conversion
The Jejueo Single Speaker Speech (JSS) dataset consists of 10k high-quality audio files recorded by a native Jejueo speaker and a transcript file. Source: https://arxiv.org/abs/1911.12071
The United-Syn-Med dataset is a specialized medical speech dataset designed to evaluate and improve Automatic Speech Recognition (ASR) systems within the healthcare domain. It comprises English medical speech recordings, with a particular focus on medical terminology and clinical conversations. The dataset is well-suited for various ASR tasks, including speech recognition, transcription, and classification, facilitating the development of models tailored for medical contexts. This dataset supports a broad range of applications, including medical documentation automation, transcription of doctor-patient conversations, and medical knowledge extraction from audio data.