README.md

---
license: apache-2.0
task_categories:
- automatic-speech-recognition
- text-to-speech 

language:
- en
- af
pretty_name: Nigerian Accent English Speech Data 1.0
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
size_categories:
- 1K<n<10K
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not
  attempt to determine the identity of speakers in the Common Voice dataset.
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: client_id
    dtype: string
  - name: path
    dtype: string
  - name: sentence
    dtype: string
  - name: accent
    dtype: string
  - name: locale
    dtype: string
  - name: segment
    dtype: float64
  splits:
  - name: train
    num_bytes: 102146340.008
    num_examples: 2721
  - name: validation
    num_bytes: 12091191.0
    num_examples: 340
  - name: test
    num_bytes: 11585096.0
    num_examples: 341
  download_size: 121509986
  dataset_size: 125822627.008
---

# Dataset Card for Nigerian Accent English Speech Data 1.0

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
- [Additional Information](#additional-information)
  - [Contributions](#contributions)

## Dataset Description

- **Repository:** https://github.com/
- **Point of Contact:** [Benjamin Ogbonna](mailto:onyedikachi1997@gmail.com)

### Dataset Summary

The Nigerian Accent Speech Data is a comprehensive dataset of about 8 hours of audio recordings featuring speakers from various regions of Nigeria, 
capturing the rich diversity of Nigerian accents. This dataset is specifically curated to address the gap in speech and language 
datasets for African accents, making it a valuable resource for researchers and developers working on Automatic Speech Recognition (ASR), 
Speech-to-text (STT), Text-to-Speech (TTS), Accent recognition, and Natural language processing (NLP) systems.

### Languages

```
Afrikaans, English
```

## 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:
```python
from datasets import load_dataset
dataset = load_dataset("benjaminogbonna/nigerian_accented_english_dataset", split="train")
```

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.
```python
from datasets import load_dataset
dataset = load_dataset("benjaminogbonna/nigerian_accented_english_dataset", split="train", streaming=True)
print(next(iter(dataset)))
```

*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).

### Local

```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
dataset = load_dataset("benjaminogbonna/nigerian_accented_english_dataset", split="train")
batch_sampler = BatchSampler(RandomSampler(dataset), batch_size=32, drop_last=False)
dataloader = DataLoader(dataset, batch_sampler=batch_sampler)
```

### Streaming

```python
from datasets import load_dataset
from torch.utils.data import DataLoader
dataset = load_dataset("benjaminogbonna/nigerian_accented_english_dataset", split="train")
dataloader = DataLoader(dataset, batch_size=32)
```

To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).

### Example scripts

Train your own CTC or Seq2Seq Automatic Speech Recognition models on Nigerian Accent English Speech Data with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).

## Dataset Structure

### Data Instances

A typical data point comprises the `path` to the audio file and its `sentence`. 
Additional fields include `accent`, `client_id`, `locale` and `segment`.

```python
{
  'client_id': 'user_3279', 
  'path': 'clips/audio_sample_3280.mp3',
  'audio': {
    'path': 'clips/audio_sample_1.mp3', 
    'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32), 
    'sampling_rate': 48000
  },
  'sentence': 'Its images were used by among others Palestinians in their protest against Israel', 
  'accent': 'nigerian', 
  'locale': 'en', 
  'segment': None
}
```

### Data Fields

`client_id` (`string`): An id for which client (voice) made the recording

`path` (`string`): The path to the audio file

`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. 
Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. 
Decoding and resampling of a large number of audio files might take a significant amount of time. 
Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0]

`sentence` (`string`): The sentence the user was prompted to speak

`accent` (`string`): Accent of the speaker

`locale` (`string`): The locale of the speaker

`segment` (`string`): Usually an empty field

### Data Splits

The dataset has been subdivided into portions for dev, train and test.

## Data Preprocessing Recommended by Hugging Face

The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. 

Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.

In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.

```python
from datasets import load_dataset
ds = load_dataset("benjaminogbonna/nigerian_accented_english_dataset")
def prepare_dataset(batch):
  """Function to preprocess the dataset with the .map method"""
  transcription = batch["sentence"]
  
  if transcription.startswith('"') and transcription.endswith('"'):
    # we can remove trailing quotation marks as they do not affect the transcription
    transcription = transcription[1:-1]
  
  if transcription[-1] not in [".", "?", "!"]:
    # append a full-stop to sentences that do not end in punctuation
    transcription = transcription + "."
  
  batch["sentence"] = transcription
  
  return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```

### Personal and Sensitive Information

The dataset consists of people who have donated their voice online.  You agree to not attempt to determine the identity of speakers in the Common Voice dataset.

### Social Impact of Dataset

The dataset consists of people who have donated their voice online.  You agree to not attempt to determine the identity of speakers in the Common Voice dataset.

### Contributions