---
language:
- iba
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
pretty_name: Iban Speech Corpus
size_categories:
- 1K<n<10K
tags:
- audio
- speech
- iban
- sarawak
- low-resource
---

###
# Iban Data collected by Sarah Samson Juan and Laurent Besacier
# Prepared by Sarah Samson Juan and Laurent Besacier
# Created in GETALP, Grenoble, France
###


## INTRODUCTION 
```
This package has iban text and speech corpora used for Automatic Speech Recognition (ASR) experiments. Data is available in the subdirectories of /data. The subdirectories contain:
a. train - train transcript for training ASR system using Kaldi ASR (http://kaldi.sourceforge.net/)
b. test - test transcript for testing ASR system (also Kaldi ASR format)
c. wav - speech corpus

We have provided text corpus and language model in the /LM directory, while, the pronunciation dictionary in /lang directory.
```
## PUBLICATION ON IBAN DATA AND ASR 
```
Details on the corpora and the our experiments on iban ASR can be found in the following list of publication. We appreciate if you cite them if you intend to publish.

@inproceedings{Juan14,
	Author = {Sarah Samson Juan and Laurent Besacier and Solange Rossato},
	Booktitle = {Proceedings of Workshop for Spoken Language Technology for Under-resourced (SLTU)},
	Month = {May},
	Title = {Semi-supervised G2P bootstrapping and its application to ASR for a very under-resourced language: Iban},
	Year = {2014}}

@inproceedings{Juan2015,
  	Title = {Using Resources from a closely-Related language to develop ASR for a very under-resourced Language: A case study for Iban},
  	Author = {Sarah Samson Juan and Laurent Besacier and Benjamin Lecouteux and Mohamed Dyab},
  	Booktitle = {Proceedings of INTERSPEECH},
  	Year = {2015},
  	Address = {Dresden, Germany},
  	Month = {September}}

```
## IBAN SPEECH CORPUS
```
News data provided by a local radio station in Sarawak, Malaysia.

Directory: data/train
Files: text (training transcript), wav.scp (file id and path to audio file), utt2spk (file id and audio id), spk2utt(audio id and file id), wav (.wav files).
For more information about the format, please refer to Kaldi website http://kaldi.sourceforge.net/data_prep.html
Description: training data in Kaldi format about 7 hours. Note: The path of wav files in wav.scp MUST BE MODIFIED to point to the actual location.

Directory: data/test
Files: text (test transcript), wav.scp (file id and path to audio file), utt2spk (file id and audio id), spk2utt(audio id and file id), wav (.wav files).
Description: testing data in Kaldi format about 1 hour. Note: The path of wav files in wav.scp MUST BE MODIFIED to point to the actual location.

The audio files have the format:
ib[m|f]_SPK_UTT where, m refers to male and f refers to female speaker, SPK denotes speaker id and UTT is the utterance id.
```
## IBAN TEXT CORPUS
```
Directory: /LM/
Files: iban-bp-2012.txt, iban-lm-o3.arpa

  # /iban-bp-2012.txt
  Contains 2 M Words. Full text data crawled from an online newspaper and cleaned as much as we could.

  # /iban-lm-o3.arpa
  The language model build on SRILM (http://www.speech.sri.com/projects/srilm/) using iban-bp-2012.txt
```

## LEXICON/PRONUNCIATION DICTIONARY
```
Directory: /lang
Files : lexicon.txt (lexicon), nonsilence_phones.txt (speech phones), optional_silence.txt (silence phone)
Description: lexicon contains words and their respective pronunciation, non-speech sound and noise in Kaldi format. Details on the development of the dictionary can be found in our papers. (For this package, we provided the Iban-Hybrid version.)

# TO DOWNLOAD THE REPOSITORY
You first need to install Subversion (SVN). Then type into a shell:

svn co https://github.com/sarahjuan/iban
```

## SCRIPTS
```
In /kaldi-scripts, you can find all scripts that can be used to train and test models from the existing data and lang directory. Note: Path needs to changed to make it work in your own directory.

You can launch run.sh to prepare data & language model, make mfccs and train acoustic models.

WER RESULTS OBTAINED USING OUR CORPORA AND SETTINGS. RESULTS OBTAINED AFTER UPDATING TEST TRANSCRIPT. THE ONES REPORTED IN OUR PAPERS WERE BEFORE THIS UPDATE

AM                WER(%)
---------------------------
monophone         41.1
triphone          35.3
triphone+del+del  36.0
tri+LDA+MLLT      25.9
tri+SAT+fMLLR     19.7
SGMM              16.6
DNN               15.8
```
HOW TO USE THIS DATASET
=====================================

# OPTION 1 - Load in Python (recommended)
```
Install the datasets library first:
  pip install datasets

Then load the dataset:
  from datasets import load_dataset
  ds = load_dataset("SaLTUNIMAS/iban-speech")

Access a single sample:
  sample = ds['train'][0]
  print(sample['transcription'])   # the transcript
  print(sample['duration'])        # audio duration in seconds
  print(sample['speaker_id'])      # speaker identifier
  print(sample['gender'])          # male or female
  print(sample['source'])          # SM1 or SM2

Play audio in Jupyter or Google Colab:
  import IPython.display as ipd
  ipd.Audio(sample['audio']['array'],
            rate=sample['audio']['sampling_rate'])

Load only one specific split
  train_ds = load_dataset("SaLTUNIMAS/iban-speech", split="train")
  test_ds  = load_dataset("SaLTUNIMAS/iban-speech", split="test")

Load both split
  from datasets import concatenate_datasets
  all_data = concatenate_datasets([ds['train'], ds['test']])
  print(f"Total: {len(all_data)} utterances")

Filter by gender:
  female = ds['train'].filter(lambda x: x['gender'] == 'female')
  male   = ds['train'].filter(lambda x: x['gender'] == 'male')

Filter by source corpus:
  sm1 = ds['train'].filter(lambda x: x['source'] == 'SM1_original_corpus')
  sm2 = ds['train'].filter(lambda x: x['source'] == 'SM2_new_recording')
```

 # OPTION 2 - Save to disk (download once, use offline)
 ```
  from datasets import load_dataset, load_from_disk
  
  # Download and save
  ds = load_dataset("SaLTUNIMAS/iban-speech")
  ds.save_to_disk("iban-dataset")
  
  # Next time load from disk (no internet needed)
  ds = load_from_disk("iban_dataset")
```

# OPTION 3 - Download all files via Python
```
  from huggingface_hub import snapshot_download
  
  snapshot_download(
      repo_id="SaLTUNIMAS/iban speech",
      repo_type="dataset",
      local_dir="./iban_dataset"
  )
```
# OPTION 4 - Manual download
```
  Go to the Files and versions tab on this page and download
  the parquet files directly. Audio is embedded inside the
  parquet files so no separate download is needed.
```

## ACKNOWLEDGEMENT 
```
We would like to thank the Ministry of Higher Education Malaysia for providing financial support to conduct this study. We also thank The Borneo Post news agency for providing online materials for building the text corpus and also to Radio Televisyen Malaysia (RTM), Sarawak, Malaysia, for providing the news data.