metadata

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-*
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        path: data/train_2-*
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        path: data/train_3-*
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        path: data/train_4-*
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        path: data/train_5-*
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        path: data/train_6-*
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        path: data/train_7-*
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        path: data/train_8-*
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        path: data/train_9-*
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        path: data/train_10-*
      - split: train_11
        path: data/train_11-*
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        path: data/train_12-*
      - split: train_13
        path: data/train_13-*
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        path: data/train_14-*
      - split: train_15
        path: data/train_15-*
      - split: train_16
        path: data/train_16-*
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        path: data/train_17-*
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        path: data/train_18-*
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        path: data/train_19-*
      - split: train_20
        path: data/train_20-*
      - split: train_21
        path: data/train_21-*
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        path: data/train_22-*
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        path: data/train_23-*
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        path: data/train_24-*
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        path: data/train_25-*
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        path: data/train_26-*
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        path: data/train_27-*
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        path: data/train_28-*
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        path: data/train_29-*
      - split: train_30
        path: data/train_30-*
      - split: train_31
        path: data/train_31-*
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        path: data/train_32-*
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        path: data/train_33-*
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        path: data/train_34-*
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        path: data/train_35-*
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        path: data/train_36-*
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        path: data/train_37-*
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        path: data/train_38-*
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        path: data/train_41-*
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        path: data/train_42-*
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        path: data/train_43-*
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        path: data/train_44-*
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
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  download_size: 7228582376
  dataset_size: 7211667407

Dataset Card for TIE_Shorts

Table of Contents
Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Repository: https://github.com/raianand1991/TIE
Paper: https://arxiv.org/abs/2307.10587
Point of Contact: [email protected]

Dataset Summary

TIE_shorts is a derived version of the Technical Indian English (TIE) dataset, a large-scale speech dataset (~ 8K hours) originally consisting of approximately 750 GB of content sourced from the NPTEL 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:

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:

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:

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

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

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

{
  '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

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 for the raw data.

Citation Information

Please cite this paper:

@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 for adding this dataset.

Dataset Card for TIE_Shorts

Table of Contents

Dataset Description

Dataset Summary

Example usage

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions