--- 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-* - 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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.