Datasets:
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ar
- de
- es
- fr
- hu
- ko
- nl
- pl
- pt
- ru
- tr
- vi
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended
task_categories:
- audio-classification
- text-classification
- zero-shot-classification
- automatic-speech-recognition
task_ids: []
pretty_name: A Multilingual Speech Dataset for SLU and Beyond
tags:
- spoken-language-understanding
- speech-translation
- speaker identification
dataset_info:
config_name: ar-SA
features:
- name: id
dtype: string
- name: locale
dtype: string
- name: partition
dtype: string
- name: scenario
dtype:
class_label:
names:
'0': social
'1': transport
'2': calendar
'3': play
'4': news
'5': datetime
'6': recommendation
'7': email
'8': iot
'9': general
'10': audio
'11': lists
'12': qa
'13': cooking
'14': takeaway
'15': music
'16': alarm
'17': weather
- name: scenario_str
dtype: string
- name: intent_idx
dtype:
class_label:
names:
'0': datetime_query
'1': iot_hue_lightchange
'2': transport_ticket
'3': takeaway_query
'4': qa_stock
'5': general_greet
'6': recommendation_events
'7': music_dislikeness
'8': iot_wemo_off
'9': cooking_recipe
'10': qa_currency
'11': transport_traffic
'12': general_quirky
'13': weather_query
'14': audio_volume_up
'15': email_addcontact
'16': takeaway_order
'17': email_querycontact
'18': iot_hue_lightup
'19': recommendation_locations
'20': play_audiobook
'21': lists_createoradd
'22': news_query
'23': alarm_query
'24': iot_wemo_on
'25': general_joke
'26': qa_definition
'27': social_query
'28': music_settings
'29': audio_volume_other
'30': calendar_remove
'31': iot_hue_lightdim
'32': calendar_query
'33': email_sendemail
'34': iot_cleaning
'35': audio_volume_down
'36': play_radio
'37': cooking_query
'38': datetime_convert
'39': qa_maths
'40': iot_hue_lightoff
'41': iot_hue_lighton
'42': transport_query
'43': music_likeness
'44': email_query
'45': play_music
'46': audio_volume_mute
'47': social_post
'48': alarm_set
'49': qa_factoid
'50': calendar_set
'51': play_game
'52': alarm_remove
'53': lists_remove
'54': transport_taxi
'55': recommendation_movies
'56': iot_coffee
'57': music_query
'58': play_podcasts
'59': lists_query
- name: intent_str
dtype: string
- name: utt
dtype: string
- name: annot_utt
dtype: string
- name: worker_id
dtype: string
- name: slot_method
sequence:
- name: slot
dtype: string
- name: method
dtype: string
- name: judgments
sequence:
- name: worker_id
dtype: string
- name: intent_score
dtype: int8
- name: slots_score
dtype: int8
- name: grammar_score
dtype: int8
- name: spelling_score
dtype: int8
- name: language_identification
dtype: string
- name: tokens
sequence: string
- name: labels
sequence: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: path
dtype: string
- name: is_transcript_reported
dtype: bool
- name: is_validated
dtype: bool
- name: speaker_id
dtype: string
- name: speaker_sex
dtype: string
- name: speaker_age
dtype: string
- name: speaker_ethnicity_simple
dtype: string
- name: speaker_country_of_birth
dtype: string
- name: speaker_country_of_residence
dtype: string
- name: speaker_nationality
dtype: string
- name: speaker_first_language
dtype: string
splits:
- name: train_115
num_bytes: 49011998
num_examples: 115
download_size: 43560799
dataset_size: 49011998
configs:
- config_name: ar-SA
data_files:
- split: train_115
path: ar-SA/train_115-*
Speech-MASSIVE
Dataset Description
Speech-MASSIVE is a multilingual Spoken Language Understanding (SLU) dataset comprising the speech counterpart for a portion of the MASSIVE textual corpus. Speech-MASSIVE covers 12 languages (Arabic, German, Spanish, French, Hungarian, Korean, Dutch, Polish, European Portuguese, Russian, Turkish, and Vietnamese) from different families and inherits from MASSIVE the annotations for the intent prediction and slot-filling tasks. MASSIVE utterances' labels span 18 domains, with 60 intents and 55 slots. Full train split is provided for French and German, and for all the 12 languages (including French and German), we provide few-shot train, dev, test splits. Few-shot train (115 examples) covers all 18 domains, 60 intents, and 55 slots (including empty slots).
Our extension is prompted by the scarcity of massively multilingual SLU datasets and the growing need for versatile speech datasets to assess foundation models (LLMs, speech encoders) across diverse languages and tasks. To facilitate speech technology advancements, we release Speech-MASSIVE publicly available with CC-BY-NC-SA-4.0 license.
Speech-MASSIVE is accepted at INTERSPEECH 2024 (Kos, GREECE).
Dataset Summary
dev: dev split available for all the 12 languagestest: test split available for all the 12 languagestrain_115: few-shot split available for all the 12 languages (all 115 samples are cross-lingually aligned)train: train split available for French (fr-FR) and German (de-DE)
| lang | split | # sample | # hrs | total # spk (Male/Female/Unidentified) |
|---|---|---|---|---|
| ar-SA | dev | 2033 | 2.12 | 36 (22/14/0) |
| test | 2974 | 3.23 | 37 (15/17/5) | |
| train_115 | 115 | 0.14 | 8 (4/4/0) | |
| de-DE | dev | 2033 | 2.33 | 68 (35/32/1) |
| test | 2974 | 3.41 | 82 (36/36/10) | |
| train | 11514 | 12.61 | 117 (50/63/4) | |
| train_115 | 115 | 0.15 | 7 (3/4/0) | |
| es-ES | dev | 2033 | 2.53 | 109 (51/53/5) |
| test | 2974 | 3.61 | 85 (37/33/15) | |
| train_115 | 115 | 0.13 | 7 (3/4/0) | |
| fr-FR | dev | 2033 | 2.20 | 55 (26/26/3) |
| test | 2974 | 2.65 | 75 (31/35/9) | |
| train | 11514 | 12.42 | 103 (50/52/1) | |
| train_115 | 115 | 0.12 | 103 (50/52/1) | |
| hu-HU | dev | 2033 | 2.27 | 69 (33/33/3) |
| test | 2974 | 3.30 | 55 (25/24/6) | |
| train_115 | 115 | 0.12 | 8 (3/4/1) | |
| ko-KR | dev | 2033 | 2.12 | 21 (8/13/0) |
| test | 2974 | 2.66 | 31 (10/18/3) | |
| train_115 | 115 | 0.14 | 8 (4/4/0) | |
| nl-NL | dev | 2033 | 2.14 | 37 (17/19/1) |
| test | 2974 | 3.30 | 100 (48/49/3) | |
| train_115 | 115 | 0.12 | 7 (3/4/0) | |
| pl-PL | dev | 2033 | 2.24 | 105 (50/52/3) |
| test | 2974 | 3.21 | 151 (73/71/7) | |
| train_115 | 115 | 0.10 | 7 (3/4/0) | |
| pt-PT | dev | 2033 | 2.20 | 107 (51/53/3) |
| test | 2974 | 3.25 | 102 (48/50/4) | |
| train_115 | 115 | 0.12 | 8 (4/4/0) | |
| ru-RU | dev | 2033 | 2.25 | 40 (7/31/2) |
| test | 2974 | 3.44 | 51 (25/23/3) | |
| train_115 | 115 | 0.12 | 7 (3/4/0) | |
| tr-TR | dev | 2033 | 2.17 | 71 (36/34/1) |
| test | 2974 | 3.00 | 42 (17/18/7) | |
| train_115 | 115 | 0.11 | 6 (3/3/0) | |
| vi-VN | dev | 2033 | 2.10 | 28 (13/14/1) |
| test | 2974 | 3.23 | 30 (11/14/5) | |
| train_115 | 115 | 0.11 | 7 (2/4/1) |
How to use
How to use
The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.
For example, to download the French config, simply specify the corresponding language config name (i.e., "fr-FR" for French):
from datasets import load_dataset
speech_massive_fr_train = load_dataset("FBK-MT/Speech-MASSIVE", "fr-FR", split="train", trust_remote_code=True)
In case you don't have enough space in the machine, you can stream dataset by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
from datasets import load_dataset
speech_massive_de_train = load_dataset("FBK-MT/Speech-MASSIVE", "de-DE", split="train", streaming=True, trust_remote_code=True)
list(speech_massive_de_train.take(2))
You can also load all the available languages and splits at once. And then access each split.
from datasets import load_dataset
speech_massive = load_dataset("FBK-MT/Speech-MASSIVE", "all", trust_remote_code=True)
multilingual_validation = speech_massive['validation']
Or you can load dataset's all the splits per language to separate languages more easily.
from datasets import load_dataset, interleave_datasets, concatenate_datasets
# creating full train set by interleaving between German and French
speech_massive_de = load_dataset("FBK-MT/Speech-MASSIVE", "de-DE", trust_remote_code=True)
speech_massive_fr = load_dataset("FBK-MT/Speech-MASSIVE", "fr-FR", trust_remote_code=True)
speech_massive_train_de_fr = interleave_datasets([speech_massive_de['train'], speech_massive_fr['train']])
# creating train_115 few-shot set by concatenating Korean and Russian
speech_massive_ko = load_dataset("FBK-MT/Speech-MASSIVE", "ko-KR", trust_remote_code=True)
speech_massive_ru = load_dataset("FBK-MT/Speech-MASSIVE", "ru-RU", trust_remote_code=True)
speech_massive_train_115_ko_ru = concatenate_datasets([speech_massive_ko['train_115'], speech_massive_ru['train_115']])
Dataset Structure
Data configs
all: load all the 12 languages in one single dataset instancelang: load onlylangin the dataset instance, by specifying one of below languagesar-SA, de-DE, es-ES, fr-FR, hu-HU, ko-KR, nl-NL, pl-PL, pt-PT, ru-RU, tr-TR, vi-VN
Data Splits
validation: validation(dev) split available for all the 12 languagestrain_115: few-shot (115 samples) split available for all the 12 languagestrain: train split available for French (fr-FR) and German (de-DE)
testsplit is uploaded as a separate dataset on HF to prevent possible data contamination
- ⚠️
test: available only in the separate HF dataset repository ⚠️
Data Instances
{
// Start of the data collected in Speech-MASSIVE
'audio': {
'path': 'train/2b12a21ca64a729ccdabbde76a8f8d56.wav',
'array': array([-7.80913979e-...7259e-03]),
'sampling_rate': 16000},
'path': '/path/to/wav/file.wav',
'is_transcript_reported': False,
'is_validated': True,
'speaker_id': '60fcc09cb546eee814672f44',
'speaker_sex': 'Female',
'speaker_age': '25',
'speaker_ethnicity_simple': 'White',
'speaker_country_of_birth': 'France',
'speaker_country_of_residence': 'Ireland',
'speaker_nationality': 'France',
'speaker_first_language': 'French',
// End of the data collected in Speech-MASSIVE
// Start of the data extracted from MASSIVE
// (https://huggingface.co/datasets/AmazonScience/massive/blob/main/README.md#data-instances)
'id': '7509',
'locale': 'fr-FR',
'partition': 'train',
'scenario': 2,
'scenario_str': 'calendar',
'intent_idx': 32,
'intent_str': 'calendar_query',
'utt': 'après les cours de natation quoi d autre sur mon calendrier mardi',
'annot_utt': 'après les cours de natation quoi d autre sur mon calendrier [date : mardi]',
'worker_id': '22',
'slot_method': {'slot': ['date'], 'method': ['translation']},
'judgments': {
'worker_id': ['22', '19', '0'],
'intent_score': [1, 2, 1],
'slots_score': [1, 1, 1],
'grammar_score': [4, 4, 4],
'spelling_score': [2, 1, 2],
'language_identification': ['target', 'target', 'target']
},
'tokens': ['après', 'les', 'cours', 'de', 'natation', 'quoi', 'd', 'autre', 'sur', 'mon', 'calendrier', 'mardi'],
'labels': ['Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'date'],
// End of the data extracted from MASSIVE
}
Data Fields
audio.path: Original audio file name
audio.array: Read audio file with the sampling rate of 16,000
audio.sampling_rate: Sampling rate
path: Original audio file full path
is_transcript_reported: Whether the transcript is reported as 'syntatically wrong' by crowd-source worker
is_validated: Whether the recorded audio has been validated to check if the audio matches transcript exactly by crowd-source worker
speaker_id: Unique hash id of the crowd source speaker
speaker_sex: Speaker's sex information provided by the crowd-source platform (Prolific)
- Male
- Female
- Unidentified : Information not available from Prolific
speaker_age: Speaker's age information provided by Prolific
- age value (
str) - Unidentified : Information not available from Prolific
speaker_ethnicity_simple: Speaker's ethnicity information provided by Prolific
- ethnicity value (
str) - Unidentified : Information not available from Prolific
speaker_country_of_birth: Speaker's country of birth information provided by Prolific
- country value (
str) - Unidentified : Information not available from Prolific
speaker_country_of_residence: Speaker's country of residence information provided by Prolific
- country value (
str) - Unidentified : Information not available from Prolific
speaker_nationality: Speaker's nationality information provided by Prolific
- nationality value (
str) - Unidentified : Information not available from Prolific
speaker_first_language: Speaker's first language information provided by Prolific
- language value (
str) - Unidentified : Information not available from Prolific
Limitations
As Speech-MASSIVE is constructed based on the MASSIVE dataset, it inherently retains certain grammatical errors present in the original MASSIVE text. Correcting these errors was outside the scope of our project. However, by providing the is_transcripted_reported attribute in Speech-MASSIVE, we enable users of the dataset to be aware of these errors.
License
All datasets are licensed under the CC-BY-NC-SA-4.0 license.
Citation Information
You can access the Speech-MASSIVE paper at link to be added. Please cite the paper when referencing the Speech-MASSIVE corpus as:
Citation to be added