This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive. The newspaper texts were taken from Herald Glasgow, with permission from Herald & Times Group. Each speaker has a different set of the newspaper texts selected based a greedy algorithm that increases the contextual and phonetic coverage. The details of the text selection algorithms are described in the following paper: C. Veaux, J. Yamagishi and S. King, "The voice bank corpus: Design, collection and data analysis of a large regional accent speech database," https://doi.org/10.1109/ICSDA.2013.6709856. The rainbow passage and elicitation paragraph are the same for all speakers. The rainbow passage can be found at International Dialects of English Archive: (http://web.ku.edu/~idea/readings/rainbow.htm). The elicitation paragraph is identical to the one used for the speech accent archive (http://accent.gmu.edu). The details of the the speech accent archive can be found at http://www.ualberta.ca/~aacl2009/PDFs/WeinbergerKunath2009AACL.pdf. All speech data was recorded using an identical recording setup: an omni-directional microphone (DPA 4035) and a small diaphragm condenser microphone with very wide bandwidth (Sennheiser MKH 800), 96kHz sampling frequency at 24 bits and in a hemi-anechoic chamber of the University of Edinburgh. (However, two speakers, p280 and p315 had technical issues of the audio recordings using MKH 800). All recordings were converted into 16 bits, were downsampled to 48 kHz, and were manually end-pointed. Source: CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit (version 0.92)
AudioCaps is a dataset of sounds with event descriptions that was introduced for the task of audio captioning, with sounds sourced from the AudioSet dataset. Annotators were provided the audio tracks together with category hints (and with additional video hints if needed). Source: Audio Retrieval with Natural Language Queries Image source: https://audiocaps.github.io/
NSynth is a dataset of one shot instrumental notes, containing 305,979 musical notes with unique pitch, timbre and envelope. The sounds were collected from 1006 instruments from commercial sample libraries and are annotated based on their source (acoustic, electronic or synthetic), instrument family and sonic qualities. The instrument families used in the annotation are bass, brass, flute, guitar, keyboard, mallet, organ, reed, string, synth lead and vocal. Four second monophonic 16kHz audio snippets were generated (notes) for the instruments. Source: Data Augmentation for Instrument Classification Robust to Audio Effects Image Source: https://magenta.tensorflow.org/nsynth
The MAESTRO dataset contains over 200 hours of paired audio and MIDI recordings from ten years of International Piano-e-Competition. The MIDI data includes key strike velocities and sustain/sostenuto/una corda pedal positions. Audio and MIDI files are aligned with ∼3 ms accuracy and sliced to individual musical pieces, which are annotated with composer, title, and year of performance. Uncompressed audio is of CD quality or higher (44.1–48 kHz 16-bit PCM stereo). Source: https://magenta.tensorflow.org/datasets/maestro Image Source: https://www.researchgate.net/figure/Results-generated-with-the-MAESTRO-dataset_fig3_333392458
MovieNet is a holistic dataset for movie understanding. MovieNet contains 1,100 movies with a large amount of multi-modal data, e.g. trailers, photos, plot descriptions, etc.. Besides, different aspects of manual annotations are provided in MovieNet, including 1.1M characters with bounding boxes and identities, 42K scene boundaries, 2.5K aligned description sentences, 65K tags of place and action, and 92 K tags of cinematic style.
The Nintendo Entertainment System Music Database (NES-MDB) is a dataset intended for building automatic music composition systems for the NES audio synthesizer. It consists of 5278 songs from the soundtracks of 397 NES games. The dataset represents 296 unique composers, and the songs contain more than two million notes combined. It has file format options for MIDI, score and NLM (NES Language Modeling). Source: https://github.com/chrisdonahue/nesmdb Image Source: https://github.com/chrisdonahue/nesmdb Audio Source: https://github.com/chrisdonahue/nesmdb
The ADL Piano MIDI is a dataset of 11,086 piano pieces from different genres. This dataset is based on the Lakh MIDI dataset, which is a collection on 45,129 unique MIDI files that have been matched to entries in the Million Song Dataset. Most pieces in the Lakh MIDI dataset have multiple instruments, so for each file the authors of ADL Piano MIDI dataset extracted only the tracks with instruments from the "Piano Family" (MIDI program numbers 1-8). This process generated a total of 9,021 unique piano MIDI files. Theses 9,021 files were then combined with other approximately 2,065 files scraped from publicly-available sources on the internet. All the files in the final collection were de-duped according to their MD5 checksum. Source: ADL Piano MIDI
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.
dMelodies is dataset of simple 2-bar melodies generated using 9 independent latent factors of variation where each data point represents a unique melody based on the following constraints: - Each melody will correspond to a unique scale (major, minor, blues, etc.). - Each melody plays the arpeggios using the standard I-IV-V-I cadence chord pattern. - Bar 1 plays the first 2 chords (6 notes), Bar 2 plays the second 2 chords (6 notes). - Each played note is an 8th note. Source: https://github.com/ashispati/dmelodies_dataset
Audio-alpaca: A preference dataset for aligning text-to-audio models Audio-alpaca is a pairwise preference dataset containing about 15k (prompt,chosen, rejected) triplets where given a textual prompt, chosen is the preferred generated audio and rejected is the undesirable audio. ## Field details prompt: Given textual prompt chosen: The preferred audio sample rejected: The rejected audio sample
The dsd100 is a dataset of 100 full lengths of music tracks of different styles along with their isolated drums, bass, vocals, and other stems. dsd100 contains two folders, a folder with a training set: "train", composed of 50 songs, and a folder with a test set: "test", composed of 50 songs. Supervised approaches should be trained on the training set and tested on both sets. For each file, the mixture corresponds to the sum of all the signals. All signals are stereophonic and encoded at 44.1kHz.
A Brazilian Portuguese TTS dataset featuring a female voice recorded with high quality in a controlled environment, with neutral emotion and more than 20 hours of recordings. with neutral emotion and more than 20 hours of recordings. Our dataset aims to facilitate transfer learning for researchers and developers working on TTS applications: a highly professional neutral female voice can serve as a good warm-up stage for learning language-specific structures, pronunciation and other non-individual characteristics of speech, leaving to further training procedures only to learn the specific adaptations needed (e.g. timbre, emotion and prosody). This can surely help enabling the accommodation of a more diverse range of female voices in Brazilian Portuguese. By doing so, we also hope to contribute to the development of accessible and high-quality TTS systems for several use cases such as virtual assistants, audiobooks, language learning tools and accessibility solutions. Possible use cases: TTS; Voice Conversion; ASR; Speech Enhancement
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