199 machine learning datasets
199 dataset results
The LibriSpeech corpus is a collection of approximately 1,000 hours of audiobooks that are a part of the LibriVox project. Most of the audiobooks come from the Project Gutenberg. The training data is split into 3 partitions of 100hr, 360hr, and 500hr sets while the dev and test data are split into the ’clean’ and ’other’ categories, respectively, depending upon how well or challenging Automatic Speech Recognition systems would perform against. Each of the dev and test sets is around 5hr in audio length. This corpus also provides the n-gram language models and the corresponding texts excerpted from the Project Gutenberg books, which contain 803M tokens and 977K unique words.
Speech Commands is an audio dataset of spoken words designed to help train and evaluate keyword spotting systems .
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:
MUSAN is a corpus of music, speech and noise. This dataset is suitable for training models for voice activity detection (VAD) and music/speech discrimination. The dataset consists of music from several genres, speech from twelve languages, and a wide assortment of technical and non-technical noises.
AISHELL-1 is a corpus for speech recognition research and building speech recognition systems for Mandarin.
WSJ0-2mix is a speech recognition corpus of speech mixtures using utterances from the Wall Street Journal (WSJ0) corpus.
LibriMix is an open-source alternative to wsj0-2mix. Based on LibriSpeech, LibriMix consists of two- or three-speaker mixtures combined with ambient noise samples from WHAM!.
The WSJ0 Hipster Ambient Mixtures (WHAM!) dataset pairs each two-speaker mixture in the wsj0-2mix dataset with a unique noise background scene. It has an extension called WHAMR! that adds artificial reverberation to the speech signals in addition to the background noise.
VoxPopuli is a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 16 languages and their aligned oral interpretations into 5 other languages totaling 5.1K hours.
GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised and unsupervised training.
AISHELL-2 contains 1000 hours of clean read-speech data from iOS is free for academic usage.
Continuous speech separation (CSS) is an approach to handling overlapped speech in conversational audio signals. A real recorded dataset, called LibriCSS, is derived from LibriSpeech by concatenating the corpus utterances to simulate a conversation and capturing the audio replays with far-field microphones.
Multimodal Opinionlevel Sentiment Intensity (MOSI) contains: (1) multimodal observations including transcribed speech and visual gestures as well as automatic audio and visual features, (2) opinion-level subjectivity segmentation, (3) sentiment intensity annotations with high coder agreement, and (4) alignment between words, visual and acoustic features.
CN-Celeb is a large-scale speaker recognition dataset collected `in the wild'. This dataset contains more than 130,000 utterances from 1,000 Chinese celebrities, and covers 11 different genres in real world.
End-to-end speech-to-text translation (ST) has recently witnessed an increased interest given its system simplicity, lower inference latency and less compounding errors compared to cascaded ST (i.e. speech recognition + machine translation). End-to-end ST model training, however, is often hampered by the lack of parallel data. Thus, we created CoVoST, a large-scale multilingual ST corpus based on Common Voice, to foster ST research with the largest ever open dataset. Its latest version covers translations from English into 15 languages---Arabic, Catalan, Welsh, German, Estonian, Persian, Indonesian, Japanese, Latvian, Mongolian, Slovenian, Swedish, Tamil, Turkish, Chinese---and from 21 languages into English, including the 15 target languages as well as Spanish, French, Italian, Dutch, Portuguese, Russian. It has total 2,880 hours of speech and is diversified with 78K speakers.
ESD is an Emotional Speech Database for voice conversion research. The ESD database consists of 350 parallel utterances spoken by 10 native English and 10 native Chinese speakers and covers 5 emotion categories (neutral, happy, angry, sad and surprise). More than 29 hours of speech data were recorded in a controlled acoustic environment. The database is suitable for multi-speaker and cross-lingual emotional voice conversion studies.
VOCASET is a 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio. The dataset has 12 subjects and 480 sequences of about 3-4 seconds each with sentences chosen from an array of standard protocols that maximize phonetic diversity.
WenetSpeech is a multi-domain Mandarin corpus consisting of 10,000+ hours high-quality labeled speech, 2,400+ hours weakly labelled speech, and about 10,000 hours unlabeled speech, with 22,400+ hours in total. The authors collected the data from YouTube and Podcast, which covers a variety of speaking styles, scenarios, domains, topics, and noisy conditions. An optical character recognition (OCR) based method is introduced to generate the audio/text segmentation candidates for the YouTube data on its corresponding video captions.
WHAMR! is a dataset for noisy and reverberant speech separation. It extends WHAM! by introducing synthetic reverberation to the speech sources in addition to the existing noise. Room impulse responses were generated and convolved using pyroomacoustics. Reverberation times were chosen to approximate domestic and classroom environments (expected to be similar to the restaurants and coffee shops where the WHAM! noise was collected), and further classified as high, medium, and low reverberation based on a qualitative assessment of the mixture’s noise recording.
Europarl-ST is a multilingual Spoken Language Translation corpus containing paired audio-text samples for SLT from and into 9 European languages, for a total of 72 different translation directions. This corpus has been compiled using the debates held in the European Parliament in the period between 2008 and 2012.