---
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:
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    path: de/train-*
  - split: valid
    path: de/valid-*
  - split: test
    path: de/test-*
- config_name: el
  data_files:
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    path: el/train-*
  - split: valid
    path: el/valid-*
  - split: test
    path: el/test-*
- config_name: es
  data_files:
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    path: es/train-*
  - split: valid
    path: es/valid-*
  - split: test
    path: es/test-*
- config_name: fr
  data_files:
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    path: fr/train-*
  - split: valid
    path: fr/valid-*
  - split: test
    path: fr/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: 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-*
dataset_info:
- config_name: ar
  features:
  - name: id
    dtype: string
  - name: audio
    dtype: audio
  - name: transcript
    dtype: string
  - name: duration
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  - name: talk_id
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  - name: segment_id
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  - name: start
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  - name: end
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- 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
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  download_size: 2035086468
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- config_name: el
  features:
  - name: id
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  - name: audio
    dtype: audio
  - name: transcript
    dtype: string
  - name: duration
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  - name: talk_id
    dtype: string
  - name: segment_id
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  - name: start
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  - name: end
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  download_size: 4035781133
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- config_name: es
  features:
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  - name: audio
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  - name: transcript
    dtype: string
  - name: duration
    dtype: float32
  - name: talk_id
    dtype: string
  - name: segment_id
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  - name: start
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  - name: end
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  download_size: 25062015078
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  - name: transcript
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  - name: duration
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  - name: talk_id
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  download_size: 24748069249
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  features:
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  - name: transcript
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  - name: duration
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  - name: talk_id
    dtype: string
  - name: segment_id
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  - name: start
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  - name: end
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  download_size: 14005414812
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- config_name: pt
  features:
  - name: id
    dtype: string
  - name: audio
    dtype: audio
  - name: transcript
    dtype: string
  - name: duration
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  - name: talk_id
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  download_size: 21173051837
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- config_name: ru
  features:
  - name: id
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  - name: audio
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  - name: transcript
    dtype: string
  - name: duration
    dtype: float32
  - name: talk_id
    dtype: string
  - name: segment_id
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  - name: start
    dtype: float32
  - name: end
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  - name: valid
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  - name: test
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  download_size: 7547655322
  dataset_size: 9191109774.911
---

# 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** |

### 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

```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},
}
```