--- annotations_creators: - expert-generated - crowdsourced - machine-generated language: - en language_creators: - crowdsourced - expert-generated license: - cc-by-4.0 - apache-2.0 - cc0-1.0 - cc-by-nc-3.0 - other multilinguality: - monolingual pretty_name: datasets size_categories: - 100K_) or converting symbolic punctuation to spelled out form (_<comma>_ to _,_). As such, no further preparation of the transcriptions is required to be used in training/evaluation scripts. Transcriptions are provided for training and validation splits. The transcriptions are **not** provided for the test splits. ESB requires you to generate predictions for the test sets and upload them to https://huggingface.co/spaces/esb/leaderboard for scoring. ### Access All eight of the datasets in ESB are accessible and licensing is freely available. Three of the ESB datasets have specific terms of usage that must be agreed to before using the data. To do so, fill in the access forms on the specific datasets' pages: * Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0 * GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech * SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech ### Diagnostic Dataset ESB contains a small, 8h diagnostic dataset of in-domain validation data with newly annotated transcriptions. The audio data is sampled from each of the ESB validation sets, giving a range of different domains and speaking styles. The transcriptions are annotated according to a consistent style guide with two formats: normalised and un-normalised. The dataset is structured in the same way as the ESB dataset, by grouping audio-transcription samples according to the dataset from which they were taken. We encourage participants to use this dataset when evaluating their systems to quickly assess performance on a range of different speech recognition conditions. For more information, visit: [esb/diagnostic-dataset](https://huggingface.co/datasets/esb/diagnostic-dataset). ## Summary of ESB Datasets | Dataset | Domain | Speaking Style | Train (h) | Dev (h) | Test (h) | Transcriptions | License | |--------------|-----------------------------|-----------------------|-----------|---------|----------|--------------------|-----------------| | LibriSpeech | Audiobook | Narrated | 960 | 11 | 11 | Normalised | CC-BY-4.0 | | Common Voice | Wikipedia | Narrated | 1409 | 27 | 27 | Punctuated & Cased | CC0-1.0 | | Voxpopuli | European Parliament | Oratory | 523 | 5 | 5 | Punctuated | CC0 | | TED-LIUM | TED talks | Oratory | 454 | 2 | 3 | Normalised | CC-BY-NC-ND 3.0 | | GigaSpeech | Audiobook, podcast, YouTube | Narrated, spontaneous | 2500 | 12 | 40 | Punctuated | apache-2.0 | | SPGISpeech | Fincancial meetings | Oratory, spontaneous | 4900 | 100 | 100 | Punctuated & Cased | User Agreement | | Earnings-22 | Fincancial meetings | Oratory, spontaneous | 105 | 5 | 5 | Punctuated & Cased | CC-BY-SA-4.0 | | AMI | Meetings | Spontaneous | 78 | 9 | 9 | Punctuated & Cased | CC-BY-4.0 | ## LibriSpeech The LibriSpeech corpus is a standard large-scale corpus for assessing ASR systems. It consists of approximately 1,000 hours of narrated audiobooks from the [LibriVox](https://librivox.org) project. It is licensed under CC-BY-4.0. Example Usage: ```python librispeech = load_dataset("esb/datasets", "librispeech") ``` Train/validation splits: - `train` (combination of `train.clean.100`, `train.clean.360` and `train.other.500`) - `validation.clean` - `validation.other` Test splits: - `test.clean` - `test.other` Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument: ```python librispeech = load_dataset("esb/datasets", "librispeech", subconfig="clean.100") ``` - `clean.100`: 100 hours of training data from the 'clean' subset - `clean.360`: 360 hours of training data from the 'clean' subset - `other.500`: 500 hours of training data from the 'other' subset ## Common Voice Common Voice is a series of crowd-sourced open-licensed speech datasets where speakers record text from Wikipedia in various languages. The speakers are of various nationalities and native languages, with different accents and recording conditions. We use the English subset of version 9.0 (27-4-2022), with approximately 1,400 hours of audio-transcription data. It is licensed under CC0-1.0. Example usage: ```python common_voice = load_dataset("esb/datasets", "common_voice", use_auth_token=True) ``` Training/validation splits: - `train` - `validation` Test splits: - `test` ## VoxPopuli VoxPopuli is a large-scale multilingual speech corpus consisting of political data sourced from 2009-2020 European Parliament event recordings. The English subset contains approximately 550 hours of speech largely from non-native English speakers. It is licensed under CC0. Example usage: ```python voxpopuli = load_dataset("esb/datasets", "voxpopuli") ``` Training/validation splits: - `train` - `validation` Test splits: - `test` ## TED-LIUM TED-LIUM consists of English-language TED Talk conference videos covering a range of different cultural, political, and academic topics. It contains approximately 450 hours of transcribed speech data. It is licensed under CC-BY-NC-ND 3.0. Example usage: ```python tedlium = load_dataset("esb/datasets", "tedlium") ``` Training/validation splits: - `train` - `validation` Test splits: - `test` ## GigaSpeech GigaSpeech is a multi-domain English speech recognition corpus created from audiobooks, podcasts and YouTube. We provide the large train set (2,500 hours) and the standard validation and test splits. It is licensed under apache-2.0. Example usage: ```python gigaspeech = load_dataset("esb/datasets", "gigaspeech", use_auth_token=True) ``` Training/validation splits: - `train` (`l` subset of training data (2,500 h)) - `validation` Test splits: - `test` Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument: ```python gigaspeech = load_dataset("esb/datasets", "spgispeech", subconfig="xs", use_auth_token=True) ``` - `xs`: extra-small subset of training data (10 h) - `s`: small subset of training data (250 h) - `m`: medium subset of training data (1,000 h) - `xl`: extra-large subset of training data (10,000 h) ## SPGISpeech SPGISpeech consists of company earnings calls that have been manually transcribed by S&P Global, Inc according to a professional style guide. We provide the large train set (5,000 hours) and the standard validation and test splits. It is licensed under a Kensho user agreement. Loading the dataset requires authorization. Example usage: ```python spgispeech = load_dataset("esb/datasets", "spgispeech", use_auth_token=True) ``` Training/validation splits: - `train` (`l` subset of training data (~5,000 h)) - `validation` Test splits: - `test` Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument: ```python spgispeech = load_dataset("esb/datasets", "spgispeech", subconfig="s", use_auth_token=True) ``` - `s`: small subset of training data (~200 h) - `m`: medium subset of training data (~1,000 h) ## Earnings-22 Earnings-22 is a 119-hour corpus of English-language earnings calls collected from global companies, with speakers of many different nationalities and accents. It is licensed under CC-BY-SA-4.0. Example usage: ```python earnings22 = load_dataset("esb/datasets", "earnings22") ``` Training/validation splits: - `train` - `validation` Test splits: - `test` ## AMI The AMI Meeting Corpus consists of 100 hours of meeting recordings from multiple recording devices synced to a common timeline. It is licensed under CC-BY-4.0. Example usage: ```python ami = load_dataset("esb/datasets", "ami") ``` Training/validation splits: - `train` - `validation` Test splits: - `test`