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The Expresso Dataset
[paper] [demo samples] [Original repository]
Introduction
The Expresso dataset is a high-quality (48kHz) expressive speech dataset that includes both expressively rendered read speech (8 styles, in mono wav format) and improvised dialogues (26 styles, in stereo wav format). The dataset includes 4 speakers (2 males, 2 females), and totals 40 hours (11h read, 30h improvised). The transcriptions of the read speech are also provided.
You can listen to samples from the Expresso Dataset at this website.
Data Statistics
Here are the statistics of Expresso’s expressive styles:
| Style | Read (min) | Improvised (min) | total (hrs) |
|---|---|---|---|
| angry | - | 82 | 1.4 |
| animal | - | 27 | 0.4 |
| animal_directed | - | 32 | 0.5 |
| awe | - | 92 | 1.5 |
| bored | - | 92 | 1.5 |
| calm | - | 93 | 1.6 |
| child | - | 28 | 0.4 |
| child_directed | - | 38 | 0.6 |
| confused | 94 | 66 | 2.7 |
| default | 133 | 158 | 4.9 |
| desire | - | 92 | 1.5 |
| disgusted | - | 118 | 2.0 |
| enunciated | 116 | 62 | 3.0 |
| fast | - | 98 | 1.6 |
| fearful | - | 98 | 1.6 |
| happy | 74 | 92 | 2.8 |
| laughing | 94 | 103 | 3.3 |
| narration | 21 | 76 | 1.6 |
| non_verbal | - | 32 | 0.5 |
| projected | - | 94 | 1.6 |
| sad | 81 | 101 | 3.0 |
| sarcastic | - | 106 | 1.8 |
| singing* | - | 4 | .07 |
| sleepy | - | 93 | 1.5 |
| sympathetic | - | 100 | 1.7 |
| whisper | 79 | 86 | 2.8 |
| Total | 11.5h | 34.4h | 45.9h |
*singing is the only improvised style that is not in dialogue format.
Audio Quality
The audio was recorded in a professional recording studio with minimal background noise at 48kHz/24bit. The files for read speech and singing are in a mono wav format; and for the dialog section in stereo (one channel per actor), where the original flow of turn-taking is preserved.
Read Speech
The read config contains all the read speech and singing style.
License
The Expresso dataset is distributed under the CC BY-NC 4.0 license.
Reference
For more information, see the paper: EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis, Tu Anh Nguyen*, Wei-Ning Hsu*, Antony D'Avirro*, Bowen Shi*, Itai Gat, Maryam Fazel-Zarani, Tal Remez, Jade Copet, Gabriel Synnaeve, Michael Hassid, Felix Kreuk, Yossi Adi⁺, Emmanuel Dupoux⁺, INTERSPEECH 2023.
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