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:
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data_files:
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dataset_info:
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struct:
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- name: Year_Class
dtype: string
- name: Speech_Class
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- name: Discipline_Group
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- name: Native_Region
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Dataset Card for TIE_Shorts
Table of Contents
- 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 segmentlanguage(datasets.ClassLabel) - numerical id of audio segmentaudio(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 textnormalized_text(string) - normalized audio segment transcriptiongender(string) - gender of speakerspeaker_id(string) - id of speakeris_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.