Datasets:
Tasks:
Automatic Speech Recognition
Formats:
parquet
Sub-tasks:
keyword-spotting
Size:
10K - 100K
ArXiv:
Tags:
speech-recognition
License:
| annotations_creators: | |
| - expert-generated | |
| - crowdsourced | |
| - machine-generated | |
| language_creators: | |
| - crowdsourced | |
| - expert-generated | |
| language: | |
| - en | |
| - fr | |
| - it | |
| - es | |
| - pt | |
| - de | |
| - nl | |
| - ru | |
| - pl | |
| - cs | |
| - ko | |
| - zh | |
| language_bcp47: | |
| - en | |
| - en-GB | |
| - en-US | |
| - en-AU | |
| - fr | |
| - it | |
| - es | |
| - pt | |
| - de | |
| - nl | |
| - ru | |
| - pl | |
| - cs | |
| - ko | |
| - zh | |
| license: | |
| - cc-by-4.0 | |
| multilinguality: | |
| - multilingual | |
| pretty_name: 'MInDS-14' | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - automatic-speech-recognition | |
| - speech-processing | |
| task_ids: | |
| - speech-recognition | |
| - keyword-spotting | |
| # MInDS-14 | |
| ## Dataset Description | |
| - **Fine-Tuning script:** [pytorch/audio-classification](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) | |
| - **Paper:** [Multilingual and Cross-Lingual Intent Detection from Spoken Data](https://arxiv.org/abs/2104.08524) | |
| - **Total amount of disk used:** ca. 500 MB | |
| MINDS-14 is training and evaluation resource for intent detection task with spoken data. It covers 14 | |
| intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties. | |
| ## Example | |
| MInDS-14 can be downloaded and used as follows: | |
| ```py | |
| from datasets import load_dataset | |
| minds_14 = load_dataset("PolyAI/minds14", "fr-FR") # for French | |
| # to download all data for multi-lingual fine-tuning uncomment following line | |
| # minds_14 = load_dataset("PolyAI/all", "all") | |
| # see structure | |
| print(minds_14) | |
| # load audio sample on the fly | |
| audio_input = minds_14["train"][0]["audio"] # first decoded audio sample | |
| intent_class = minds_14["train"][0]["intent_class"] # first transcription | |
| intent = minds_14["train"].features["intent_class"].names[intent_class] | |
| # use audio_input and language_class to fine-tune your model for audio classification | |
| ``` | |
| ## Dataset Structure | |
| We show detailed information the example configurations `fr-FR` of the dataset. | |
| All other configurations have the same structure. | |
| ### Data Instances | |
| **fr-FR** | |
| - Size of downloaded dataset files: 471 MB | |
| - Size of the generated dataset: 300 KB | |
| - Total amount of disk used: 471 MB | |
| An example of a datainstance of the config `fr-FR` looks as follows: | |
| ``` | |
| { | |
| "path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav", | |
| "audio": { | |
| "path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav", | |
| "array": array( | |
| [0.0, 0.0, 0.0, ..., 0.0, 0.00048828, -0.00024414], dtype=float32 | |
| ), | |
| "sampling_rate": 8000, | |
| }, | |
| "transcription": "je souhaite changer mon adresse", | |
| "english_transcription": "I want to change my address", | |
| "intent_class": 1, | |
| "lang_id": 6, | |
| } | |
| ``` | |
| ### Data Fields | |
| The data fields are the same among all splits. | |
| - **path** (str): Path to the audio file | |
| - **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio | |
| - **transcription** (str): Transcription of the audio file | |
| - **english_transcription** (str): English transcription of the audio file | |
| - **intent_class** (int): Class id of intent | |
| - **lang_id** (int): Id of language | |
| ### Data Splits | |
| Every config only has the `"train"` split containing of *ca.* 600 examples. | |
| ## Dataset Creation | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Discussion of Biases | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Other Known Limitations | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ## Additional Information | |
| ### Dataset Curators | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Licensing Information | |
| All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/). | |
| ### Citation Information | |
| ``` | |
| @article{DBLP:journals/corr/abs-2104-08524, | |
| author = {Daniela Gerz and | |
| Pei{-}Hao Su and | |
| Razvan Kusztos and | |
| Avishek Mondal and | |
| Michal Lis and | |
| Eshan Singhal and | |
| Nikola Mrksic and | |
| Tsung{-}Hsien Wen and | |
| Ivan Vulic}, | |
| title = {Multilingual and Cross-Lingual Intent Detection from Spoken Data}, | |
| journal = {CoRR}, | |
| volume = {abs/2104.08524}, | |
| year = {2021}, | |
| url = {https://arxiv.org/abs/2104.08524}, | |
| eprinttype = {arXiv}, | |
| eprint = {2104.08524}, | |
| timestamp = {Mon, 26 Apr 2021 17:25:10 +0200}, | |
| biburl = {https://dblp.org/rec/journals/corr/abs-2104-08524.bib}, | |
| bibsource = {dblp computer science bibliography, https://dblp.org} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset | |