---
license: apache-2.0
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- en
pretty_name: Technical Indian English
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train_0
    path: data/train_0-*
  - split: train_1
    path: data/train_1-*
  - split: train_2
    path: data/train_2-*
  - split: train_3
    path: data/train_3-*
  - split: train_4
    path: data/train_4-*
  - split: train_5
    path: data/train_5-*
  - split: train_6
    path: data/train_6-*
  - split: train_7
    path: data/train_7-*
  - split: train_8
    path: data/train_8-*
  - split: train_9
    path: data/train_9-*
  - split: train_10
    path: data/train_10-*
  - split: train_11
    path: data/train_11-*
  - split: train_12
    path: data/train_12-*
  - split: train_13
    path: data/train_13-*
  - split: train_14
    path: data/train_14-*
  - split: train_15
    path: data/train_15-*
  - split: train_16
    path: data/train_16-*
  - split: train_17
    path: data/train_17-*
  - split: train_18
    path: data/train_18-*
  - split: train_19
    path: data/train_19-*
  - split: train_20
    path: data/train_20-*
  - split: train_21
    path: data/train_21-*
  - split: train_22
    path: data/train_22-*
  - split: train_23
    path: data/train_23-*
  - split: train_24
    path: data/train_24-*
  - split: train_25
    path: data/train_25-*
  - split: train_26
    path: data/train_26-*
  - split: train_27
    path: data/train_27-*
  - split: train_28
    path: data/train_28-*
  - split: train_29
    path: data/train_29-*
  - split: train_30
    path: data/train_30-*
  - split: train_31
    path: data/train_31-*
  - split: train_32
    path: data/train_32-*
  - split: train_33
    path: data/train_33-*
  - split: train_34
    path: data/train_34-*
  - split: train_35
    path: data/train_35-*
  - split: train_36
    path: data/train_36-*
  - split: train_37
    path: data/train_37-*
  - split: train_38
    path: data/train_38-*
  - split: train_39
    path: data/train_39-*
  - split: train_40
    path: data/train_40-*
  - split: train_41
    path: data/train_41-*
  - split: train_42
    path: data/train_42-*
  - split: train_43
    path: data/train_43-*
  - split: train_44
    path: data/train_44-*
  - split: train_45
    path: data/train_45-*
  - split: train_46
    path: data/train_46-*
  - split: train_47
    path: data/train_47-*
  - split: train_48
    path: data/train_48-*
  - split: train_49
    path: data/train_49-*
  - split: train_50
    path: data/train_50-*
  - split: train_51
    path: data/train_51-*
  - split: train_52
    path: data/train_52-*
  - split: train_53
    path: data/train_53-*
  - split: train_54
    path: data/train_54-*
  - split: train_55
    path: data/train_55-*
  - split: train_56
    path: data/train_56-*
  - split: train_57
    path: data/train_57-*
  - split: train_58
    path: data/train_58-*
  - split: train_59
    path: data/train_59-*
  - split: train_60
    path: data/train_60-*
  - split: train_61
    path: data/train_61-*
  - split: train_62
    path: data/train_62-*
  - split: train_63
    path: data/train_63-*
  - split: train_64
    path: data/train_64-*
  - split: train_65
    path: data/train_65-*
  - split: train_66
    path: data/train_66-*
  - split: train_67
    path: data/train_67-*
  - split: train_68
    path: data/train_68-*
  - split: train_69
    path: data/train_69-*
  - split: train_70
    path: data/train_70-*
  - split: train_71
    path: data/train_71-*
  - split: train_72
    path: data/train_72-*
  - split: train_73
    path: data/train_73-*
  - split: train_74
    path: data/train_74-*
  - split: train_75
    path: data/train_75-*
  - split: train_76
    path: data/train_76-*
  - split: train_77
    path: data/train_77-*
  - split: train_78
    path: data/train_78-*
  - split: test_0
    path: data/test_0-*
  - split: test_1
    path: data/test_1-*
  - split: test_2
    path: data/test_2-*
  - split: test_3
    path: data/test_3-*
  - split: test_4
    path: data/test_4-*
dataset_info:
  features:
  - name: audio
    struct:
    - name: array
      sequence:
        sequence: float32
    - name: path
      dtype: string
    - name: sampling_rate
      dtype: int64
  - name: split
    dtype: string
  - name: ID
    dtype: string
  - name: Transcript
    dtype: string
  - name: Normalised_Transcript
    dtype: string
  - name: Speech_Duration_seconds
    dtype: float64
  - name: Speaker_ID
    dtype: int64
  - name: Gender
    dtype: string
  - name: Caste
    dtype: string
  - name: Year_Class
    dtype: string
  - name: Speech_Class
    dtype: string
  - name: Discipline_Group
    dtype: string
  - name: Native_Region
    dtype: string
  - name: Topic
    dtype: string
  splits:
  - name: train_0
    num_bytes: 159596908
    num_examples: 100
  - name: train_1
    num_bytes: 154466417
    num_examples: 100
  - name: train_2
    num_bytes: 164830755
    num_examples: 100
  - name: train_3
    num_bytes: 163846670
    num_examples: 100
  - name: train_4
    num_bytes: 158878351
    num_examples: 100
  - name: train_5
    num_bytes: 161562786
    num_examples: 100
  - name: train_6
    num_bytes: 168529715
    num_examples: 100
  - name: train_7
    num_bytes: 163769246
    num_examples: 100
  - name: train_8
    num_bytes: 152866617
    num_examples: 100
  - name: train_9
    num_bytes: 171234967
    num_examples: 100
  - name: train_10
    num_bytes: 155676874
    num_examples: 100
  - name: train_11
    num_bytes: 166546675
    num_examples: 100
  - name: train_12
    num_bytes: 154204346
    num_examples: 100
  - name: train_13
    num_bytes: 161604831
    num_examples: 100
  - name: train_14
    num_bytes: 163285492
    num_examples: 100
  - name: train_15
    num_bytes: 156010091
    num_examples: 100
  - name: train_16
    num_bytes: 155817421
    num_examples: 100
  - name: train_17
    num_bytes: 165098083
    num_examples: 100
  - name: train_18
    num_bytes: 170197491
    num_examples: 100
  - name: train_19
    num_bytes: 155464475
    num_examples: 100
  - name: train_20
    num_bytes: 155351724
    num_examples: 100
  - name: train_21
    num_bytes: 159715260
    num_examples: 100
  - name: train_22
    num_bytes: 158236240
    num_examples: 100
  - name: train_23
    num_bytes: 159682266
    num_examples: 100
  - name: train_24
    num_bytes: 166115920
    num_examples: 100
  - name: train_25
    num_bytes: 157975696
    num_examples: 100
  - name: train_26
    num_bytes: 163387926
    num_examples: 100
  - name: train_27
    num_bytes: 156164315
    num_examples: 100
  - name: train_28
    num_bytes: 163665051
    num_examples: 100
  - name: train_29
    num_bytes: 161448207
    num_examples: 100
  - name: train_30
    num_bytes: 152968507
    num_examples: 100
  - name: train_31
    num_bytes: 158547084
    num_examples: 100
  - name: train_32
    num_bytes: 159756851
    num_examples: 100
  - name: train_33
    num_bytes: 162052446
    num_examples: 100
  - name: train_34
    num_bytes: 169312452
    num_examples: 100
  - name: train_35
    num_bytes: 170415545
    num_examples: 100
  - name: train_36
    num_bytes: 159185426
    num_examples: 100
  - name: train_37
    num_bytes: 155372992
    num_examples: 100
  - name: train_38
    num_bytes: 156961021
    num_examples: 100
  - name: train_39
    num_bytes: 155754650
    num_examples: 100
  - name: train_40
    num_bytes: 164206647
    num_examples: 100
  - name: train_41
    num_bytes: 153346275
    num_examples: 100
  - name: train_42
    num_bytes: 152080502
    num_examples: 100
  - name: train_43
    num_bytes: 158419068
    num_examples: 100
  - name: train_44
    num_bytes: 158057125
    num_examples: 100
  - name: train_45
    num_bytes: 165164816
    num_examples: 100
  - name: train_46
    num_bytes: 157659132
    num_examples: 100
  - name: train_47
    num_bytes: 158897047
    num_examples: 100
  - name: train_48
    num_bytes: 168559462
    num_examples: 100
  - name: train_49
    num_bytes: 167699018
    num_examples: 100
  - name: train_50
    num_bytes: 159117923
    num_examples: 100
  - name: train_51
    num_bytes: 157182317
    num_examples: 100
  - name: train_52
    num_bytes: 159672528
    num_examples: 100
  - name: train_53
    num_bytes: 152821680
    num_examples: 100
  - name: train_54
    num_bytes: 164752542
    num_examples: 100
  - name: train_55
    num_bytes: 165649574
    num_examples: 100
  - name: train_56
    num_bytes: 164706387
    num_examples: 100
  - name: train_57
    num_bytes: 154830453
    num_examples: 100
  - name: train_58
    num_bytes: 161133030
    num_examples: 100
  - name: train_59
    num_bytes: 154735208
    num_examples: 100
  - name: train_60
    num_bytes: 164090726
    num_examples: 100
  - name: train_61
    num_bytes: 156685845
    num_examples: 100
  - name: train_62
    num_bytes: 159936561
    num_examples: 100
  - name: train_63
    num_bytes: 160654183
    num_examples: 100
  - name: train_64
    num_bytes: 161032032
    num_examples: 100
  - name: train_65
    num_bytes: 155268183
    num_examples: 100
  - name: train_66
    num_bytes: 164158067
    num_examples: 100
  - name: train_67
    num_bytes: 168308047
    num_examples: 100
  - name: train_68
    num_bytes: 168014390
    num_examples: 100
  - name: train_69
    num_bytes: 161971102
    num_examples: 100
  - name: train_70
    num_bytes: 156137089
    num_examples: 100
  - name: train_71
    num_bytes: 148956376
    num_examples: 100
  - name: train_72
    num_bytes: 155518828
    num_examples: 100
  - name: train_73
    num_bytes: 166295901
    num_examples: 100
  - name: train_74
    num_bytes: 151141940
    num_examples: 100
  - name: train_75
    num_bytes: 158780014
    num_examples: 100
  - name: train_76
    num_bytes: 158061024
    num_examples: 100
  - name: train_77
    num_bytes: 155858659
    num_examples: 100
  - name: train_78
    num_bytes: 131617110
    num_examples: 84
  - name: test_0
    num_bytes: 152436572
    num_examples: 100
  - name: test_1
    num_bytes: 161351141
    num_examples: 100
  - name: test_2
    num_bytes: 160833508
    num_examples: 100
  - name: test_3
    num_bytes: 154454493
    num_examples: 100
  - name: test_4
    num_bytes: 164697965
    num_examples: 100
  download_size: 13449529304
  dataset_size: 13420508280
---



# Dataset Card for TIE_Shorts

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description
- **Repository:** https://github.com/raianand1991/TIE
- **Paper:** https://arxiv.org/abs/2307.10587
- **Point of Contact:** [raianand.1991@gmail.com](mailto:rainanad.1991@gmail.com)

### Dataset Summary

TIE_shorts is a derived version of the [Technical Indian English (TIE)](https://github.com/raianand1991/TIE) dataset, a large-scale speech dataset (~ 8K hours) originally consisting of approximately 750 GB of content 
sourced from the [NPTEL](https://nptel.ac.in/) platform. The original TIE dataset contains around 9.8K technical lectures in English delivered by instructors from various regions across India, 
with each lecture averaging about 50 minutes. These lectures cover a wide range of technical subjects and capture diverse linguistic features characteristic of Indian 
English.

The TIE_shorts version (~ 70 hours audio and 600K ground-truth tokens) was created to facilitate efficient training and usage in speech processing tasks by providing shorter audio samples. In TIE_shorts, 
consecutive audio snippets from the original dataset were merged based on timestamps, with a condition that the final merged audio should not exceed 30 seconds in duration.
This process results in 25–30 second audio clips, each accompanied by a corresponding ground-truth transcript. This approach retains the linguistic diversity of the original 
dataset while significantly reducing the size and complexity, making TIE_shorts ideal for Automatic Speech Recognition (ASR) and other speech-to-text applications. 
As the dataset consisting of approximately 9.8K files spoken by 331 speakers from diverse demographics across the Indian population, this data is also well-suited for speaker identification and text-to-speech (TTS) training applications.

### Example usage

VoxPopuli contains labelled data for 18 languages. To load a specific language pass its name as a config name:

```python
from datasets import load_dataset

voxpopuli_croatian = load_dataset("facebook/voxpopuli", "hr")
```

To load all the languages in a single dataset use "multilang" config name:

```python
voxpopuli_all = load_dataset("facebook/voxpopuli", "multilang")
```

To load a specific set of languages, use "multilang" config name and pass a list of required languages to `languages` parameter:

```python
voxpopuli_slavic = load_dataset("facebook/voxpopuli", "multilang", languages=["hr", "sk", "sl", "cs", "pl"])
```

To load accented English data, use "en_accented" config name:

```python
voxpopuli_accented = load_dataset("facebook/voxpopuli", "en_accented")
```

**Note that L2 English subset contains only `test` split.**


### Supported Tasks and Leaderboards

* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).

Accented English subset can also be used for research in ASR for accented speech (15 L2 accents)

### Languages

VoxPopuli contains labelled (transcribed) data for 18 languages:

| Language | Code | Transcribed Hours | Transcribed Speakers | Transcribed Tokens |
|:---:|:---:|:---:|:---:|:---:|
| English | En | 543 | 1313 | 4.8M |
| German | De | 282 | 531 | 2.3M |
| French | Fr | 211 | 534 | 2.1M |
| Spanish | Es | 166 | 305 | 1.6M |
| Polish | Pl | 111 | 282 | 802K |
| Italian | It | 91 | 306 | 757K |
| Romanian | Ro | 89 | 164 | 739K |
| Hungarian | Hu | 63 | 143 | 431K |
| Czech | Cs | 62 | 138 | 461K |
| Dutch | Nl | 53 | 221 | 488K |
| Finnish | Fi | 27 | 84 | 160K |
| Croatian | Hr | 43 | 83 | 337K |
| Slovak | Sk | 35 | 96 | 270K |
| Slovene | Sl | 10 | 45 | 76K |
| Estonian | Et | 3 | 29 | 18K |
| Lithuanian | Lt | 2 | 21 | 10K |
| Total | | 1791 | 4295 | 15M |


Accented speech transcribed data has 15 various L2 accents:

| Accent | Code | Transcribed Hours | Transcribed Speakers |
|:---:|:---:|:---:|:---:|
| Dutch | en_nl | 3.52 | 45 |
| German | en_de | 3.52 | 84 |
| Czech | en_cs | 3.30 | 26 |
| Polish | en_pl | 3.23 | 33 |
| French | en_fr | 2.56 | 27 |
| Hungarian | en_hu | 2.33 | 23 |
| Finnish | en_fi | 2.18 | 20 |
| Romanian | en_ro | 1.85 | 27 |
| Slovak | en_sk | 1.46 | 17 |
| Spanish | en_es | 1.42 | 18 |
| Italian | en_it | 1.11 | 15 |
| Estonian | en_et | 1.08 | 6 |
| Lithuanian | en_lt | 0.65 | 7 |
| Croatian | en_hr | 0.42 | 9 |
| Slovene | en_sl | 0.25 | 7 |

## Dataset Structure

### Data Instances

```python
{
  'audio_id': '20180206-0900-PLENARY-15-hr_20180206-16:10:06_5',
  'language': 11,  # "hr"
  'audio': {
    'path': '/home/polina/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/train_part_0/20180206-0900-PLENARY-15-hr_20180206-16:10:06_5.wav',
    'array': array([-0.01434326, -0.01055908,  0.00106812, ...,  0.00646973], dtype=float32),
    'sampling_rate': 16000
  },
  'raw_text': '',
  'normalized_text': 'poast genitalnog sakaenja ena u europi tek je jedna od manifestacija takve tetne politike.',
  'gender': 'female',
  'speaker_id': '119431',
  'is_gold_transcript': True,
  'accent': 'None'
}
```

### Data Fields

* `audio_id` (string) - id of audio segment
* `language` (datasets.ClassLabel) - numerical id of audio segment 
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `raw_text` (string) - original (orthographic) audio segment text
* `normalized_text` (string) - normalized audio segment transcription
* `gender` (string) - gender of speaker
* `speaker_id` (string) - id of speaker
* `is_gold_transcript` (bool) - ?
* `accent` (string) - type of accent, for example "en_lt", if applicable, else "None".

### Data Splits

All configs (languages) except for accented English contain data in three splits: train, validation and test. Accented English `en_accented` config contains only test split.

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home)

#### Initial Data Collection and Normalization

The VoxPopuli transcribed set comes from aligning  the full-event source speech audio with the transcripts for plenary sessions. Official timestamps
are available for locating speeches by speaker in the full session, but they are frequently inaccurate, resulting in truncation of the speech or mixture
of fragments from the preceding or the succeeding speeches. To calibrate the original timestamps,
we perform speaker diarization (SD) on the full-session audio using pyannote.audio (Bredin et al.2020) and adopt the nearest SD timestamps (by L1 distance to the original ones) instead for segmentation. 
Full-session audios are segmented into speech paragraphs by speaker, each of which has a transcript available.

The speech paragraphs have an average duration of 197 seconds, which leads to significant. We hence further segment these paragraphs into utterances with a
maximum duration of 20 seconds. We leverage speech recognition (ASR) systems to force-align speech paragraphs to the given transcripts.
The ASR systems are TDS models (Hannun et al., 2019) trained with ASG criterion (Collobert et al., 2016) on audio tracks from in-house deidentified video data.

The resulting utterance segments may have incorrect transcriptions due to incomplete raw transcripts or inaccurate ASR force-alignment. 
We use the predictions from the same ASR systems as references and filter the candidate segments by a maximum threshold of 20% character error rate(CER).

#### Who are the source language producers?

Speakers are participants of the European Parliament events, many of them are EU officials.

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

Gender speakers distribution is imbalanced, percentage of female speakers is mostly lower than 50% across languages, with the minimum of 15% for the Lithuanian language data.

VoxPopuli includes all available speeches from the 2009-2020 EP events without any selections on the topics or speakers.
The speech contents represent the standpoints of the speakers in the EP events, many of which are EU officials.


### Other Known Limitations


## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

The dataset is distributet under CC0 license, see also [European Parliament's legal notice](https://www.europarl.europa.eu/legal-notice/en/) for the raw data.

### Citation Information

Please cite this paper:

```bibtex
@inproceedings{wang-etal-2021-voxpopuli,
    title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation",
    author = "Wang, Changhan  and
      Riviere, Morgane  and
      Lee, Ann  and
      Wu, Anne  and
      Talnikar, Chaitanya  and
      Haziza, Daniel  and
      Williamson, Mary  and
      Pino, Juan  and
      Dupoux, Emmanuel",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.80",
    pages = "993--1003",
}
```

### Contributions

Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.