Datasets:

deepdml
/

mtedx

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-*
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: 2391070227.46
        num_examples: 16420
      - name: valid
        num_bytes: 146961659.916
        num_examples: 1188
      - name: test
        num_bytes: 154468753.204
        num_examples: 1332
    download_size: 2556295615
    dataset_size: 2692500640.58
  - 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

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.

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	s ≈ 12h29m18s
valid	9	1 172	14 661	s ≈ 1h54m53s
test	9	1 166	14 289	s ≈ 1h51m55s
total	71	9 062	123 934	s ≈ 16h16m6s

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},
}