README.md

metadata

language:
  - es
  - fr
  - pt
  - it
  - ru
  - el
  - ar
  - 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: es
    data_files:
      - split: train
        path: es/train-*
      - split: valid
        path: es/valid-*
      - split: test
        path: es/test-*
  - config_name: fr
    data_files:
      - split: train
        path: fr/train-*
      - split: valid
        path: fr/valid-*
      - split: test
        path: fr/test-*
  - config_name: it
    data_files:
      - split: train
        path: it/train-*
      - split: valid
        path: it/valid-*
      - split: test
        path: it/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-*
dataset_info:
  - config_name: ar
    features:
      - name: id
        dtype: string
      - name: audio
        dtype: audio
      - name: transcript
        dtype: string
      - name: duration
        dtype: float32
      - name: talk_id
        dtype: string
      - name: segment_id
        dtype: int32
      - name: start
        dtype: float32
      - name: end
        dtype: float32
    splits:
      - name: train
        num_bytes: 2178889228.384
        num_examples: 11129
      - name: valid
        num_bytes: 142140185.32
        num_examples: 1024
      - name: test
        num_bytes: 132688587.25
        num_examples: 1025
    download_size: 2472218095
    dataset_size: 2453718000.954
  - config_name: de
    features:
      - name: id
        dtype: string
      - name: audio
        dtype: audio
      - name: transcript
        dtype: string
      - name: duration
        dtype: float32
      - name: talk_id
        dtype: string
      - name: segment_id
        dtype: int32
      - name: start
        dtype: float32
      - name: end
        dtype: float32
    splits:
      - name: train
        num_bytes: 1765760361.764
        num_examples: 6764
      - name: valid
        num_bytes: 256591446.396
        num_examples: 1172
      - name: test
        num_bytes: 245484939.996
        num_examples: 1126
    download_size: 2035086468
    dataset_size: 2267836748.1559997
  - config_name: el
    features:
      - name: id
        dtype: string
      - name: audio
        dtype: audio
      - name: transcript
        dtype: string
      - name: duration
        dtype: float32
      - name: talk_id
        dtype: string
      - name: segment_id
        dtype: int32
      - name: start
        dtype: float32
      - name: end
        dtype: float32
    splits:
      - name: train
        num_bytes: 2568039234.829
        num_examples: 12817
      - name: valid
        num_bytes: 304746052
        num_examples: 972
      - name: test
        num_bytes: 276022477.156
        num_examples: 1018
    download_size: 4035781133
    dataset_size: 3148807763.985
  - config_name: es
    features:
      - name: id
        dtype: string
      - name: audio
        dtype: audio
      - name: transcript
        dtype: string
      - name: duration
        dtype: float32
      - name: talk_id
        dtype: string
      - name: segment_id
        dtype: int32
      - name: start
        dtype: float32
      - name: end
        dtype: float32
    splits:
      - name: train
        num_bytes: 26561374066.116
        num_examples: 100198
      - name: valid
        num_bytes: 223052682
        num_examples: 894
      - name: test
        num_bytes: 245397409.388
        num_examples: 1003
    download_size: 25062015078
    dataset_size: 27029824157.504
  - config_name: fr
    features:
      - name: id
        dtype: string
      - name: audio
        dtype: audio
      - name: transcript
        dtype: string
      - name: duration
        dtype: float32
      - name: talk_id
        dtype: string
      - name: segment_id
        dtype: int32
      - name: start
        dtype: float32
      - name: end
        dtype: float32
    splits:
      - name: train
        num_bytes: 20224863779.006
        num_examples: 113954
      - name: valid
        num_bytes: 281149192.02
        num_examples: 1020
      - name: test
        num_bytes: 221199082.174
        num_examples: 1046
    download_size: 24748069249
    dataset_size: 20727212053.2
  - config_name: it
    features:
      - name: id
        dtype: string
      - name: audio
        dtype: audio
      - name: transcript
        dtype: string
      - name: duration
        dtype: float32
      - name: talk_id
        dtype: string
      - name: segment_id
        dtype: int32
      - name: start
        dtype: float32
      - name: end
        dtype: float32
    splits:
      - name: train
        num_bytes: 14681799161.35
        num_examples: 49043
      - name: valid
        num_bytes: 239961988
        num_examples: 920
      - name: test
        num_bytes: 249234907
        num_examples: 992
    download_size: 14005414812
    dataset_size: 15170996056.35
  - config_name: pt
    features:
      - name: id
        dtype: string
      - name: audio
        dtype: audio
      - name: transcript
        dtype: string
      - name: duration
        dtype: float32
      - name: talk_id
        dtype: string
      - name: segment_id
        dtype: int32
      - name: start
        dtype: float32
      - name: end
        dtype: float32
    splits:
      - name: train
        num_bytes: 23704544310.012
        num_examples: 88798
      - name: valid
        num_bytes: 184985513.136
        num_examples: 1002
      - name: test
        num_bytes: 234352081.034
        num_examples: 1006
    download_size: 21173051837
    dataset_size: 24123881904.182003
  - config_name: ru
    features:
      - name: id
        dtype: string
      - name: audio
        dtype: audio
      - name: transcript
        dtype: string
      - name: duration
        dtype: float32
      - name: talk_id
        dtype: string
      - name: segment_id
        dtype: int32
      - name: start
        dtype: float32
      - name: end
        dtype: float32
    splits:
      - name: train
        num_bytes: 8638149553.84
        num_examples: 28824
      - name: valid
        num_bytes: 269571389
        num_examples: 965
      - name: test
        num_bytes: 283388832.071
        num_examples: 1117
    download_size: 7547655322
    dataset_size: 9191109774.911

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

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
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 files
• data/<split>/vtt/ — WebVTT transcript files (<id>.<lang>.vtt)
• data/<split>/txt/ — Segments and Plain-text transcripts
• docs/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},
}