cawoylel/FulaSpeechCorpora-splited-noise_augmented

Sources of the data

This dataset is created from FulaSpeechCorpora dataset and splitted into train, test and dev sets. It has been augmented using audiomentations library https://iver56.github.io/audiomentations/.

The augmentation process includes the following tranformations:

• Add background noise to simulate an environment where background noise is present
• Add gaussian noise to the samples
• Multiply the audio by a random amplitude factor to reduce or increase the volume. This technique can help a model become somewhat invariant to the overall gain of the input audio.
• Change the speed or duration of the signal without changing the pitch.

How to augment

Download noise and background sounds

wget https://www.openslr.org/resources/17/musan.tar.gz
tar -zxvf musan.tar.gz
rm -r ./musan/speech

Augment

from audiomentations import *

def get_transformations():
  """
  Define audio augmentation transformations
  """
  musan_dir = "./musan"
  augment = Compose([
      Gain(min_gain_in_db=-6, max_gain_in_db=6, p=0.1),
      PitchShift(min_semitones=-4, max_semitones=4, p=0.5),
      TimeStretch(min_rate=0.9, max_rate=1.1, p=0.4, leave_length_unchanged=False),
      OneOf([
      AddBackgroundNoise(
                      sounds_path=musan_dir, min_snr_in_db=1.0, max_snr_in_db=5.0, noise_transform=PolarityInversion(), p=1.
                  ),
      AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.015, p=1.),],  p=1.0)

      ])

  return augment

def augment_dataset(batch):
  sample = batch['audio']

  augmentation = get_transformations()
    # apply augmentation
  augmented_waveform = augmentation(sample["array"], sample_rate=sample["sampling_rate"])
  batch['audio']["array"] = augmented_waveform
  return batch

Fula varieties in this dataset

This dataset contains 8 varrieties:

• Pulaar: spoken in Senegal, Mauritania and West-Mali.
• Pular: spoken in Guinea.
• Maacina: spoken in the Center and East of Mali.
• Liptako: spoken in Burkina Faso and Niger.
• Caka: spoken in the Central Nigeria.
• Bororro: a very nomad group living in Cameroon, Central African Republic and Tchad.
• Borgu: spoken in Togo and Benin.
• Adamawa: spoken in Cameroon and South-East Nigeria.

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 training set, simply specify the corresponding split name:

from datasets import load_dataset
train = load_dataset("cawoylel/FulaSpeechCorpora-splited-noise_augmented", split = "train")

You can also load all the dataset:

from datasets import load_dataset
data = load_dataset("cawoylel/FulaSpeechCorpora-splited-noise_augmented")

Using the datasets library, you can also stream the dataset on-the-fly 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
data = load_dataset("cawoylel/FulaSpeechCorpora-splited-noise_augmented", streaming=True)
print(next(iter(data)))

Data Fields

The data fields are the same among all splits.

• dialect (str): The name of the dialect
• audio (dict): Audio object including loaded audio array, sampling rate and path to audio
• transcription (str): Transcription of the audio file

Social Impact of Dataset

Under-representation, dialectal and cultural diversity.

Discussion of Biases

Gender biases, noise robustness

Additional Information

All datasets are licensed under the Creative Commons license (CC-BY).

Citation Information

Please cite us when using the FulaSpeechCorpora:

@article{fleurs2022arxiv,
  title = {FulaSpeechCorpora-splited-noise_augmented: A multidialectal speech dataset for Fula.},
  author = {Sy, Yaya and Doucouré, Dioula},
  url = {https://huggingface.co/datasets/cawoylel/FulaSpeechCorpora-splited-noise_augmented},
  year = {2023},