--- annotations_creators: - expert-generated - crowdsourced - machine-generated language_creators: - crowdsourced - expert-generated language: - 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