spoken-language-understanding-research-datasets

Overview

This repository contains the license and instructions relative to the open datasets mentioned in this publication:

Saade A. et al., 2019, "Spoken Language Understanding on the Edge",

accepted for publication at the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing colocated with NeurIPS 2019.

These datasets are made publicly available in the interest of reproducibility and in the hope that they can prove useful to the SLU community.

More specifically, they contain up to a few thousand text queries with their supervision, i.e. intent and slots, collected using an in-house data generation pipeline. Recordings of these sentences are then crowdsourced and one spoken utterance is collected for each text query in the dataset. Far-field datasets are created by playing these utterances with a neutral speaker and recording them using a microphone array positioned at a distance of 2 meters.

WARNING: For various reasons, some text queries might not have recordings. Handlers are provided in dataset.py to deal with this special case (see below).

Description of the Data Sets

The datasets cover two domains of increasing complexity:

• a SmartLights assistant meant to be used in cross validation, comprising 6 intents allowing to turn on or off the light, or change its brightness or color, with a vocabulary size of approximately 400 words:

  • DecreaseBrightness (296 queries, slots: room),
  • IncreaseBrightness (296 queries, slots: room),
  • SetLightBrightness (296 queries, slots: room, brightness),
  • SetLightColor (300 queries, slots: room, color),
  • SwitchLightOff (299 queries, slots: room),
  • SwitchLightOn (278 queries, slots: room)

• two SmartSpeaker assistants in English and French meant to be used in train/test. The training set comprises 9 intents (8 in French) allowing to control a smart speaker through playback control (volume control, track navigation, etc), but also play music from large libraries of artists, tracks and albums. The assistants have a vocabulary size of more than 65k words in English and 70k words in French. For this assistant, recordings are only available for the test set. The English ontology is the following:

  • NextSong (200 queries, no slot),
  • PreviousSong (199 queries, no slot),
  • SpeakerInterrupt (172 queries, no slot),
  • ResumeMusic (200 queries, no slot),
  • VolumeDown (215 queries, slots: volume_level_absolute),
  • VolumeUp (260 queries, slots: volume_level_absolute),
  • VolumeSet (100 queries, slots: volume_level_absolute, volume_level_percent),
  • GetInfos (199 queries, slots: music_item),
  • PlayMusic (1508 queries, slots: song_name, artist_name, album_name, playlist_mode, playlist_name)

  The French ontology slightly differs for the volume control intents:

  • NextSong (126 queries, no slot),
  • PreviousSong (62 queries, no slot),
  • SpeakerInterrupt (421 queries, no slot),
  • ResumeMusic (107 queries, no slot),
  • VolumeShift (437 queries, slots: volume_action)
  • VolumeSet (229 queries, slots: volume_level_absolute, volume_level_percent, volume_level_relative),
  • GetInfos (62 queries, no slot),
  • PlayMusic (548 queries in train, 1500 queries in test, slots: song_name, artist_name, album_name, playlist_mode, playlist_name)

  The English and French test sets consist in 1,500 queries of the form ``play some music by #ARTIST'', where we sample #ARTIST from a publicly available list of the most streamed artists on Spotify, updated based on daily and weekly statistics (from https://kworb.net/spotify/artists.html, visited on 9/10/18). The list, divided in 3 tiers of popularity, will be available with the Data Sets.

Handlers

Minimal handlers are provided for convenience in dataset_handler.py. They depend on future for Python 2/3 compatibility. Here is an example of how to use them

from dataset_handler import TrainTestDataset, CrossValDataset

data_dir = "/path/to/smart-lights/data/folder"
dataset = CrossValDataset.from_dir(data_dir)

print(dataset.get_audio_file(
    "Set lights to twenty two percent in the basement"))
print(dataset.get_labels_from_text(
    "Set lights to twenty two percent in the basement"))
    
print(dataset.get_transcript("0.wav"))
print(dataset.get_labels_from_wav("0.wav"))

data_dir = "/path/to/smart-speaker/data/folder"
dataset = TrainTestDataset.from_dir(data_dir)

print(dataset.get_audio_file("I'd like to listen to Drake")
print(dataset.get_labels_from_text("I'd like to listen to Drake")

print(dataset.get_transcript("0.wav"))
print(dataset.get_labels_from_wav("0.wav"))

License summary

Use only for academic and/or research purposes. No commercial use. Re-publication permitted only if the datasets are unmodified and subject to the same license terms. Any publication must include a full citation to the paper in which the datasets were initially published by Snips¹:

Saade A. et al., 2019, "Spoken Language Understanding on the Edge",

accepted for publication at the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing colocated with NeurIPS 2019.

Please read the full License Terms before accessing the datasets.

Data Set access

To access the data, please fill the following form:

https://forms.gle/JtmFYM7xK1SaMfZYA

You will be granted access shortly.

¹ The Snips team has joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email [email protected] with any question.