Create README.md

README.md

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+---
+language:
+  - ar
+  - es
+  - fr
+  - pt
+  - it
+  - 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
+---
+
+# Multilingual TEDx (mTEDx) — SLR100
+
+## Dataset Description
+
+**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**
+(Arabic, Spanish, French, Portuguese, Italian, Russian, Greek, 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 VTT 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** |
+
+> Replace the rows above with the equivalent table for each language once
+> `docs/statistics.txt` files are available. The script `create_mtedx_dataset.py`
+> reads and prints statistics automatically when run.
+
+### All Languages — Download Sizes (original tarballs)
+
+| Config | Language    | Tarball size |
+|--------|-------------|-------------:|
+| `ar`   | Arabic      | 3.6 GB       |
+| `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      |
+| `de`   | German      |  2.6 GB      |
+
+---
+
+## Dataset Structure
+
+### Schema
+
+Each example corresponds to **one audio segment** extracted from a full TEDx talk
+using the VTT timestamps.
+
+| Field        | Type              | Description                                             |
+|--------------|-------------------|---------------------------------------------------------|
+| `id`         | `string`          | Unique segment id: `<talk_stem>_<index>` (e.g. `14zpc3Nj_e4_0003`) |
+| `talk_id`    | `string`          | Source talk file stem                                   |
+| `segment_id` | `int32`           | 0-based index of the segment within its talk            |
+| `audio`      | `Audio`           | Audio float32 waveform of the segment                    |
+| `duration`   | `float32`         | Duration of the audio segment **in seconds**            |
+| `transcript` | `string`          | Transcription text from the VTT file                    |
+| `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',
+#   'talk_id':    '14zpc3Nj_e4',
+#   'segment_id': 1,
+#   'audio':      {'array': array([...], dtype=float32), 'sampling_rate': 16000},
+#   'duration':   4.16,
+#   'transcript': 'أكل العالم وغص بنخلة',
+#   'start':      9.332,
+#   'end':        13.492,
+#   'language':   'ar'
+# }
+
+# Stream a large language without downloading everything
+ds = load_dataset("your-username/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("your-username/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/` — 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 VTT 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},
+}
+```