Datasets:

deepdml
/

mtedx

	---
	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:
	- split: train
	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:
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	path: fr-es/train-*
	- split: valid
	path: fr-es/valid-*
	- split: test
	path: fr-es/test-*
	- config_name: fr-pt
	data_files:
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	path: fr-pt/train-*
	- split: valid
	path: fr-pt/valid-*
	- split: test
	path: fr-pt/test-*
	- config_name: it
	data_files:
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	path: it/train-*
	- split: valid
	path: it/valid-*
	- split: test
	path: it/test-*
	- config_name: it-en
	data_files:
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	path: it-en/train-*
	- split: valid
	path: it-en/valid-*
	- split: test
	path: it-en/test-*
	- config_name: it-es
	data_files:
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	- split: valid
	path: it-es/valid-*
	- split: test
	path: it-es/test-*
	- config_name: pt
	data_files:
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	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:
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	path: ru-en/train-*
	- split: valid
	path: ru-en/valid-*
	- split: test
	path: ru-en/test-*
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	- name: transcript
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	---

	# Multilingual TEDx (mTEDx) — SLR100

	## Dataset Description
	- Homepage: [OpenSLR SLR100](https://www.openslr.org/100/)

	mTEDx is a multilingual speech recognition and translation corpus built from
	[TEDx Talks](https://www.ted.com/watch/tedx-talks).
	Original resource: [https://www.openslr.org/100/](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](https://creativecommons.org/licenses/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

	```python
	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](https://www.openslr.org/100/).
	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

	```bibtex
	@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},
	}
	```