File size: 3,017 Bytes
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============
Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus
suitable for speech research. The dataset is derived from read audiobooks
from LibriVox and consists of 8 languages - English, German, Dutch, French,
Spanish, Italian, Portuguese and Polish.
STATISTICS
==========
------------------------------------------------
Language | train(hrs) | dev(hrs) | test(hrs)
------------------------------------------------
English | 44659.74 | 15.75 | 15.55
German | 1966.51 | 14.28 | 14.29
Dutch | 1554.24 | 12.76 | 12.76
French | 1076.58 | 10.07 | 10.07
Spanish | 917.68 | 9.99 | 10.00
Italian | 247.38 | 5.18 | 5.27
Portuguese | 160.96 | 3.64 | 3.74
Polish | 103.65 | 2.08 | 2.14
------------------------------------------------
DIRECTORY STRUCTURE
===================
A typical top-level directory layout
.
├── metainfo.txt # Info about the LibriVox data used in the dataset (one row per chapter)
├── dev # Dev (validation) subset
│ ├── segments.txt
│ ├── transcripts.txt
│ └── audio
│ └── ...
├── test # Test subset
│ ├── segments.txt
│ ├── transcripts.txt
│ └── audio
│ └── ...
├── train # Train subset
│ ├── segments.txt # LibriVox audio links and segment start/end timestamps (in sec) for each audio file
│ ├── transcripts.txt # Transcripts file with audio file name as key
│ ├── audio
│ │ ├── 10148 # LibriVox speaker ID
│ │ │ ├── 10119 # LibriVox book ID
│ │ │ │ ├── 10148_10119_000000.flac # 16Khz audio file
│ │ │ │ ├── 10148_10119_000001.flac
│ │ │ │ └── ...
│ │ │ └── ...
│ │ └── ...
│ └── limited_supervision # Limited supervision training sets (10 min, 1 hr and 10 hr)
│ ├── 1hr # Data of the 1h split (made up of 6 folds of 10 min)
│ │ ├── 0 # first 10 min fold
│ │ │ └── handles.txt # List of audio handles for the split
│ │ ├── ...
│ │ └── 6 # last 10 min fold
│ │ └── handles.txt
│ └── 9hr # Remaining data of the 10h split (10h=1h+9h)
│ └── handles.txt
├── LICENSE # License info
└── README
For more information, see the paper "MLS: A Large-Scale Multilingual Dataset for Speech Research", Vineel Pratap, Qiantong Xu, Anuroop Sriram, Gabriel Synnaeve and Ronan Collobert, INTERSPEECH 2020.
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