Datasets:

PolyAI
/

minds14

	---
	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
	license:
	- cc-by-4.0
	multilinguality:
	- multilingual
	size_categories:
	- 10K<n<100K
	task_categories:
	- automatic-speech-recognition
	- speech-processing
	task_ids:
	- speech-recognition
	- keyword-spotting
	pretty_name: MInDS-14
	language_bcp47:
	- en
	- en-GB
	- en-US
	- en-AU
	- fr
	- it
	- es
	- pt
	- de
	- nl
	- ru
	- pl
	- cs
	- ko
	- zh
	dataset_info:
	config_name: nl-NL
	features:
	- name: audio
	dtype:
	audio:
	sampling_rate: 8000
	- name: transcription
	dtype: string
	- name: english_transcription
	dtype: string
	- name: intent_class
	dtype:
	class_label:
	names:
	'0': abroad
	'1': address
	'2': app_error
	'3': atm_limit
	'4': balance
	'5': business_loan
	'6': card_issues
	'7': cash_deposit
	'8': direct_debit
	'9': freeze
	'10': high_value_payment
	'11': joint_account
	'12': latest_transactions
	'13': pay_bill
	- name: lang_id
	dtype:
	class_label:
	names:
	'0': cs-CZ
	'1': de-DE
	'2': en-AU
	'3': en-GB
	'4': en-US
	'5': es-ES
	'6': fr-FR
	'7': it-IT
	'8': ko-KR
	'9': nl-NL
	'10': pl-PL
	'11': pt-PT
	'12': ru-RU
	'13': zh-CN
	splits:
	- name: train
	num_bytes: 51394899.0
	num_examples: 654
	download_size: 46537391
	dataset_size: 51394899.0
	configs:
	- config_name: nl-NL
	data_files:
	- split: train
	path: nl-NL/train-*
	---

	# 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