metadata
language:
- es
- fr
- pt
- it
- ar
- ru
- el
- de
license: cc-by-nc-nd-4.0
task_categories:
- automatic-speech-recognition
- translation
pretty_name: Multilingual TEDx (mTEDx) — SLR100
tags:
- speech
- audio
- tedx
- multilingual
- asr
- speech-translation
configs:
- config_name: ar
data_files:
- split: train
path: ar/train-*
- split: valid
path: ar/valid-*
- split: test
path: ar/test-*
- config_name: de
data_files:
- split: train
path: de/train-*
- split: valid
path: de/valid-*
- split: test
path: de/test-*
- config_name: el
data_files:
- split: train
path: el/train-*
- split: valid
path: el/valid-*
- split: test
path: el/test-*
- config_name: el-en
data_files:
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path: el-en/train-*
- split: valid
path: el-en/valid-*
- split: test
path: el-en/test-*
- config_name: es
data_files:
- split: train
path: es/train-*
- split: valid
path: es/valid-*
- split: test
path: es/test-*
- config_name: es-en
data_files:
- split: train
path: es-en/train-*
- split: valid
path: es-en/valid-*
- split: test
path: es-en/test-*
- config_name: es-fr
data_files:
- split: train
path: es-fr/train-*
- split: valid
path: es-fr/valid-*
- split: test
path: es-fr/test-*
- config_name: es-it
data_files:
- split: train
path: es-it/train-*
- split: valid
path: es-it/valid-*
- split: test
path: es-it/test-*
- config_name: es-pt
data_files:
- split: train
path: es-pt/train-*
- split: valid
path: es-pt/valid-*
- split: test
path: es-pt/test-*
- config_name: fr
data_files:
- split: train
path: fr/train-*
- split: valid
path: fr/valid-*
- split: test
path: fr/test-*
- config_name: fr-en
data_files:
- split: train
path: fr-en/train-*
- split: valid
path: fr-en/valid-*
- split: test
path: fr-en/test-*
- config_name: fr-es
data_files:
- split: train
path: fr-es/train-*
- split: valid
path: fr-es/valid-*
- split: test
path: fr-es/test-*
- config_name: fr-pt
data_files:
- split: train
path: fr-pt/train-*
- split: valid
path: fr-pt/valid-*
- split: test
path: fr-pt/test-*
- config_name: it
data_files:
- split: train
path: it/train-*
- split: valid
path: it/valid-*
- split: test
path: it/test-*
- config_name: it-en
data_files:
- split: train
path: it-en/train-*
- split: valid
path: it-en/valid-*
- split: test
path: it-en/test-*
- config_name: it-es
data_files:
- split: train
path: it-es/train-*
- split: valid
path: it-es/valid-*
- split: test
path: it-es/test-*
- config_name: pt
data_files:
- split: train
path: pt/train-*
- split: valid
path: pt/valid-*
- split: test
path: pt/test-*
- config_name: ru
data_files:
- split: train
path: ru/train-*
- split: valid
path: ru/valid-*
- split: test
path: ru/test-*
- config_name: ru-en
data_files:
- split: train
path: ru-en/train-*
- split: valid
path: ru-en/valid-*
- split: test
path: ru-en/test-*
dataset_info:
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Multilingual TEDx (mTEDx) — SLR100
Dataset Description
- Homepage: OpenSLR SLR100
mTEDx is a multilingual speech recognition and translation corpus built from
TEDx Talks.
Original resource: https://www.openslr.org/100/
The corpus provides audio recordings and VTT transcripts for 8 languages (Spanish, French, Portuguese, Italian, Russian, Greek, Arabic, German) with aligned translations into up to 5 languages (English, Spanish, French, Portuguese, Italian).
License: CC BY-NC-ND 4.0
Contact: Elizabeth Salesky (esalesky@jhu.edu), Matthew Wiesner (wiesner@jhu.edu)
Corpus Statistics
Each row in the dataset corresponds to one segment (individual audio clip + transcript).
The table below reflects sentence counts and total audio duration as reported in docs/statistics.txt per language.
Spanish (es)
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 988 | 102 171 | 1 676 862 | 764 301 s ≈ 212h 18m21s |
| valid | 16 | 905 | 14 327 | 7 013 s ≈ 1h 56m53s |
| test | 12 | 1 012 | 15 439 | 7 475 s ≈ 2h 4m35s |
| iwslt2021 | 15 | 996 | 16 229 | 7 365 s ≈ 2h 2m46s |
| total | 1 031 | 105 084 | 1 722 857 | 786 155 s ≈ 218h 22m35s |
French (fr)
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 949 | 116 045 | 1 838 447 | 780 355 s ≈ 216h 45m55s |
| valid | 12 | 1 036 | 16 590 | 8 033 s ≈ 2h 13m54s |
| test | 10 | 1 059 | 16 136 | 7 158 s ≈ 1h 59m18s |
| iwslt2021 | 11 | 1 041 | 16 653 | 8 342 s ≈ 2h 19m2s |
| total | 982 | 119 181 | 1 887 826 | 803 889 s ≈ 223h 18m9s |
Portuguese (pt)
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 820 | 90 244 | 1 433 073 | 642 853 s ≈ 178h 34m14s |
| valid | 9 | 1 013 | 14 457 | 6 522 s ≈ 1h 48m43s |
| test | 13 | 1 020 | 17 626 | 7 648 s ≈ 2h 7m28s |
| iwslt2021 | 11 | 1 022 | 15 498 | 7 290 s ≈ 2h 1m30s |
| total | 853 | 93 299 | 1 480 654 | 664 315 s ≈ 184h 31m55s |
Italian (it)
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 489 | 49 964 | 883 138 | 420 141 s ≈ 116h 42m22s |
| valid | 8 | 931 | 16 316 | 7 883 s ≈ 2h 11m24s |
| test | 8 | 999 | 18 359 | 7 790 s ≈ 2h 9m51s |
| iwslt2021 | 11 | 979 | 17 368 | 7 940 s ≈ 2h 12m20s |
| total | 516 | 52 873 | 935 181 | 443 756 s ≈ 123h 15m56s |
Russian (ru)
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 238 | 29 161 | 400 666 | 205 222 s ≈ 57h 0m23s |
| valid | 7 | 973 | 13 739 | 7 258 s ≈ 2h 0m58s |
| test | 9 | 1 132 | 14 598 | 7 554 s ≈ 2h 5m55s |
| total | 254 | 31 266 | 429 003 | 220 035 s ≈ 61h 7m15s |
Greek (el)
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 113 | 12 965 | 221 625 | 104 084 s ≈ 28h 54m45s |
| valid | 10 | 982 | 18 586 | 9 412 s ≈ 2h 36m53s |
| test | 8 | 1 027 | 17 164 | 8 493 s ≈ 2h 21m33s |
| total | 131 | 14 974 | 257 375 | 121 991 s ≈ 33h 53m11s |
Arabic (ar)
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 95 | 11 821 | 115 259 | 68 310 s ≈ 18h 58m |
| valid | 7 | 1 079 | 9 374 | 5 280 s ≈ 1h 28m |
| test | 7 | 1 066 | 8 964 | 5 187 s ≈ 1h 26m |
| total | 109 | 13 966 | 133 597 | 78 778 s ≈ 21h 53m |
German (de)
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 53 | 6 764 | 94 984 | 44 958 s ≈ 12h 29m18s |
| valid | 9 | 1 172 | 14 661 | 6 893 s ≈ 1h 54m53s |
| test | 9 | 1 166 | 14 289 | 6 715 s ≈ 1h 51m55s |
| total | 71 | 9 062 | 123 934 | 58 566 s ≈ 16h 16m6s |
el-en
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 35 | 4 384 | 73 886 | 34 042 s ≈ 9h 27m22s |
| valid | 10 | 982 | 18 586 | 9 412 s ≈ 2h 36m53s |
| test | 8 | 1 027 | 17 164 | 8 493 s ≈ 2h 21m33s |
| total | 53 | 6 393 | 109 636 | 51 948 s ≈ 14h 25m48s |
es-en
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 378 | 36 263 | 596 484 | 27 9645 s ≈ 77h 40m45s |
| valid | 16 | 905 | 14 327 | 7 013 s ≈ 1h 56m53s |
| test | 12 | 1 012 | 15 439 | 7 475 s ≈ 2h 4m35s |
| iwslt2021 | 15 | 996 | 16 229 | 7365.96 s ≈ 2h2m46s |
| total | 421 | 39 176 | 642 479 | 301 499 s ≈ 83h 44m59s |
es-fr
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 43 | 3 663 | 61 547 | 27 427 s ≈ 7h 37m8s |
| valid | 16 | 905 | 14 327 | 7 013 s ≈ 1h 56m53s |
| test | 12 | 1 012 | 15 439 | 7 475 s ≈ 2h 4m35s |
| iwslt2021 | 15 | 996 | 16 229 | 7 365 s ≈ 2h 2m46s |
| total | 86 | 6 576 | 107 542 | 49 282 s ≈ 13h 41m22s |
es-it
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 57 | 5600 | 92 182 | 42 529 s ≈ 11h 48m49s |
| valid | 1 | 16 | 156 | 110 s ≈ 0h 1m50s |
| test | 3 | 267 | 3 812 | 2 079 s ≈ 0h 34m40s |
| iwslt2021 | 3 | 225 | 3 539 | 1 625 s ≈ 0h 27m6s |
| total | 64 | 6 108 | 99 689 | 46 345 s ≈ 12h 52m25s |
es-pt
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 225 | 21 107 | 351 555 | 162260 s ≈ 45h 4m20s |
| valid | 16 | 905 | 14 327 | 7013 s ≈ 1h 56m53s |
| test | 12 | 1 012 | 15 439 | 7475 s ≈ 2h 4m35s |
| iwslt2021 | 15 | 996 | 16 229 | 7365 s ≈ 2h 2m46s |
| total | 268 | 24 020 | 397 550 | 184 114 s ≈ 51h 8m34s |
fr-en
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 250 | 30 171 | 477 516 | 199 524 s ≈ 55h 25m25s |
| valid | 12 | 1 036 | 16 590 | 8 033 s ≈ 2h 13m54s |
| test | 10 | 1 059 | 16 136 | 7 158 s ≈ 1h 59m18s |
| iwslt2021 | 11 | 1 041 | 16 653 | 8 342 s ≈ 2h 19m2s |
| total | 283 | 33 307 | 526 895 | 223 058 s ≈ 61h 57m38s |
fr-es
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 196 | 20 826 | 331 569 | 144 016 s ≈ 40h 0m17s |
| valid | 12 | 1 036 | 16 590 | 8 033 s ≈ 2h 13m54s |
| test | 10 | 1 059 | 16 136 | 7 158 s ≈ 1h 59m18s |
| iwslt2021 | 11 | 1 041 | 16 653 | 8 342 s ≈ 2h 19m2s |
| total | 229 | 23 962 | 380 948 | 167 550 s ≈ 46h 32m30s |
fr-pt
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 112 | 13 286 | 209 488 | 88 800 s ≈ 24h 40m1s |
| valid | 12 | 1 036 | 16 590 | 8 033 s ≈ 2h 13m54s |
| test | 10 | 1 059 | 16 136 | 7 158 s ≈ 1h 59m18s |
| iwslt2021 | 11 | 1 041 | 16 653 | 8 342 s ≈ 2h 19m2s |
| total | 145 | 16 422 | 258 867 | 112 334 s = 31h 12m14s |
it-en
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 221 | 24 576 | 425 219 | 199 294 s ≈ 55h 21m34s |
| valid | 8 | 931 | 16 316 | 7 883 s ≈ 2h 11m24s |
| test | 8 | 999 | 18 359 | 7 790 s ≈ 2h 9m51s |
| iwslt2021 | 11 | 979 | 17 368 | 7 940 s ≈ 2h 12m20s |
| total | 248 | 27 485 | 477 262 | 222 908 s ≈ 61h 55m8s |
it-es
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 27 | 2 261 | 42 161 | 20 636 s ≈ 5h43m57s |
| valid | 8 | 931 | 16 316 | 7 883 s ≈ 2h11m24s |
| test | 8 | 999 | 18 359 | 7 790 s ≈ 2h9m51s |
| iwslt2021 | 11 | 979 | 17 368 | 7 940 s ≈ 2h12m20s |
| total | 54 | 5 170 | 94 204 | 44 251 s ≈ 12h 17m31s |
ru-en
| Split | Talks | Sentences | Words | Duration |
|---|---|---|---|---|
| train | 40 | 4 921 | 66 021 | 33 160 s ≈ 9h 12m41s |
| valid | 7 | 973 | 13 740 | 7 258 s ≈ 2h 0m58s |
| test | 9 | 1 132 | 14 605 | 7 554 s ≈ 2h 5m55s |
| total | 56 | 7 026 | 94 366 | 47 973 s ≈ 13h 19m33s |
All Languages — Download Sizes (original tarballs)
| Config | Language | Tarball size |
|---|---|---|
es |
Spanish | 35 GB |
fr |
French | 34 GB |
pt |
Portuguese | 29 GB |
it |
Italian | 19 GB |
ru |
Russian | 10 GB |
el |
Greek | 5.5 GB |
ar |
Arabic | 3.6 GB |
de |
German | 2.6 GB |
Dataset Structure
Schema
Each example corresponds to one audio segment extracted from a full TEDx talk using the Kaldi segments timestamps file.
| Field | Type | Description |
|---|---|---|
id |
string |
Unique segment id: <talk_stem>_<index> (e.g. 14zpc3Nj_e4_0003) |
audio |
Audio |
Audio float32 waveform of the segment |
transcript |
string |
Transcription text |
translation |
string |
Translated text, if translated dataset |
duration |
float32 |
Duration of the audio segment in seconds |
talk_id |
string |
Source talk file stem |
segment_id |
int32 |
0-based index of the segment within its talk |
start |
float32 |
Segment start time within the source talk (seconds) |
end |
float32 |
Segment end time within the source talk (seconds) |
Splits
| Split | Description |
|---|---|
train |
Training set |
valid |
Validation / development set |
test |
Test set |
Usage
from datasets import load_dataset
# Load Arabic training split
ds = load_dataset("deepdml/mtedx", "ar", split="train")
print(ds[0])
# {
# 'id': '14zpc3Nj_e4_0001',
# 'audio': {'array': array([...], dtype=float32), 'sampling_rate': 16000},
# 'transcript': 'أكل العالم وغص بنخلة',
# 'duration': 4.16,
# 'talk_id': '14zpc3Nj_e4',
# 'segment_id': 1,
# 'start': 9.332,
# 'end': 13.492,
# 'language': 'ar'
# }
# Stream a large language without downloading everything
ds = load_dataset("deepdml/mtedx", "es", split="train", streaming=True)
for sample in ds:
audio = sample["audio"]["array"] # numpy float32 array @ 16 kHz
text = sample["transcript"]
dur = sample["duration"] # seconds
break
# ASR fine-tuning example (Whisper / wav2vec2)
ds = load_dataset("deepdml/mtedx", "fr", split="train")
ds = ds.select_columns(["audio", "transcript", "duration"])
Source Data
Downloaded from OpenSLR SLR100.
Each language pack (mtedx_<lang>.tgz) contains:
data/<split>/wav/— Full-talk FLAC audio filesdata/<split>/vtt/— WebVTT transcript files (<id>.<lang>.vtt)data/<split>/txt/— Segments and Plain-text transcriptsdocs/statistics.txt— Per-split statistics
The upload script (create_mtedx_dataset.py) slices the full-talk FLAC files
into individual segments using the kaldi segments timestamps and discards segments shorter
than 0.5 s or longer than 30 s.
Citation
@inproceedings{salesky2021mtedx,
title = {Multilingual TEDx Corpus for Speech Recognition and Translation},
author = {Elizabeth Salesky and Matthew Wiesner and Jacob Bremerman and
Roldano Cattoni and Matteo Negri and Marco Turchi and
Douglas W. Oard and Matt Post},
booktitle = {Proceedings of Interspeech},
year = {2021},
}