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arabic-dialect-identification

Fine-grained, country-level Arabic dialect identification (17 Arabic countries)

This repository is to provide YouTube id for Arabic Dialect Identification (ADI) track of the fifth edition of the Multi-Genre Broadcast Challenge(MGB-5). Training example of end-to-end dialect identification system is also available.

You can also find more detail on this dataset at here (http://groups.csail.mit.edu/sls/downloads/adi17/)

ADI Task

The task of ADI is dialect identification of speech from YouTube to one of the 17 dialects (ADI17). The previous studies on Arabic dialect identification using audio signal is limited to 5 dialect classes by lack of speech corpus. To present a fine-grained analysis on the Arabic dialect speech, we collected Arabic dialect from YouTube.

ADI17 dataset

(Araibic dialect identification speech dataset of 17 Arabic countries)

For Train set, about 3,000 hours of Arabic dialect speech data from 17 countries on the Arabic world was collected from YouTube. Since we collected the speech by considering the YouTube channels in a specific country, certain that the dataset might have some labeling errors. For this reason, we have two sub-tracks for the ADI task, supervised learning track and unsupervised track. Thus, the label of the train set can be either used or not and it completely depends on the choice of participants.

For the Dev and Test set, about 280 hours speech data was collected from YouTube. After automatic speaker linking and dialect labeling by human annotators, we selected 57 hours of speech dataset to use as Dev and Test set for performance evaluation. The test dataset was considered to have three sub-categories by the segment duration to represent short (under 5 sec), medium(between 5 sec and 20 sec), long duration (over 20 sec) of the dialectal speech.

How to download Youtube dataset

All the YouTube unique id is available in the data folder. First, clone this repository and then run the script as below

python scripts/crawl.py data/dev/utt2lang

Or, if you participate MGB-5 Challenge, you can request segmented wav files to organizer at here.

How to use data

Each data folder (dev, train) consisted of "segments", "utt2lang", "wav.scp" files. These file format is exactly same as Kaldi data preparation. You don't need "segments" file if you downloaded ADI17 dataset from MGB-5 organizer (use dev_segments and train_segments folder in this case).

segments: segment-level id, YouTube id, start, end time stamp(of segment)
utt2lang: YouTube id(or segment-level id), dialect label (one of 17 dialects)
wav.scp: YouTube id, wav file location (This file will be generated automatically in the example code)

You can also find more examples at here (http://kaldi-asr.org/doc/data_prep.html)

Supervised or semi-supervised task

Since the ADI17 dataset has dialect label, supervised learning is a proper way to maximize the dataset efficiency. However, we also encourage to explore the semi-supervised setting to discover more interesting approach to discover robust dialect/language representation.

*If you want to explore semi-supervised setting, discard all dialect label information on the Train set and use dialect label in the Dev set only.

Submission of result file

You should submit dialect identification result in segment-level in the test set. (test set will be available along the timeline of MGB-5 challenge)

Example result on Dev set is available on the "result_dev.csv" file. The format are [segment id],[score of 1st dialect],...,[score of 17th dialect]. The order of dialects should follows the "data/language_id_initial" file. For example,

2XwBQJ7eHKs_055962-056391,3.096405,1.777897,1.452979,-2.077643,-0.419325,-0.345230,-1.016110,-1.385472,3.511110,1.943294,-1.195900,-2.251954,-1.363119,2.556440,-1.121042,0.347785,-2.648575
2XwBQJ7eHKs_056439-057583,12.810358,2.679880,4.258311,-1.582249,-0.770333,-1.986860,-3.571980,-0.695768,0.929641,3.286271,-0.159408,-4.281460,-3.185079,-1.564716,-0.437594,2.433181,-5.155079
2XwBQJ7eHKs_057651-057966,7.816638,0.154628,8.818256,-0.711897,-0.899901,-1.057132,-1.626845,-3.524030,-1.554264,-2.776326,-0.864858,2.107841,-2.718826,-1.714218,2.685726,3.349209,-4.997468

The scores should represent likelihood score P(O|L_i), a conditional probability for observed data (O) given each language model (L_i). You can also find the example code to generate this "result_dev.csv" file in the "scripts/baseline_dev.py" file at the line 82.

Performance evaluation (Accuracy and Cost_average)

Primary performance measure is accuracy (%) and alternative measure will be average EER for each dialects. You can check the performance on the dev set as below (need result_dev.csv file).

python scripts/measure_accuracy.py --label data/dev_segments/utt2lang --score result_dev.csv --duration

and the result on Dev set will show

Accuracy = 83.03%

To follow the standard measurement on Language ID research area, we also provide code to measure cost average that defined on NIST LRE17.

python scripts/measure_cavg.py --label data/dev_segments/utt2lang --score result_dev.csv --softmax --duration

and the result on Dev set will show

Cavg*100 = 11.65

Baseline example code with pre-trained model

A baseline using end-to-end dialect identification system is provided and you can run the example code as below

python scripts/baseline_dev.py

The example code extract MFCC feature from wav file and feed it to pre-trained end-to-end dialect identification system. Finally, the extracted output layer activations saved in CSV format (result_dev.csv).

We also provide the training example code as below.

python run_training.py  # Use when you downloaded original wav directly from YouTube.
python run_training_segmented.sh # Use when you downloaded the segmented files from MGB5 organizer. All wav file was already segmented and you don't need segments file information.

You can find more information about this baseline system in the paper below

Suwon Shon, Ahmed Ali, James Glass,
Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition,
Proc. Odyssey 2018 The Speaker and Language Recognition Workshop, 98-104
https://arxiv.org/abs/1803.04567

Requirements (for example training code and baseline code)

Python 2.7
tensorflow (python library, tested on 1.12)
librosa (python library, tested on 0.6.0)

Contact

Please email to organizer if you have question.
Suwon Shon (swshon@mit.edu)
Ahmed Ali (amali@hbku.edu.qa)

https://arabicspeech.org/mgb5

Citing

@inproceedings{ali2019mgb5,
  title={The MGB-5 Challenge: Recognition and Dialect Identification of Dialectal Arabic Speech},
  author={Ali, Ahmed and Shon, Suwon and Samih, Younes and Mubarak, Hamdy and Abdelali, Ahmed and Glass, James and Renals, Steve and Choukri, Khalid},
  booktitle={IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)},
  year={2019},
}

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