---
license: apache-2.0
task_categories:
- automatic-speech-recognition
- text-to-speech
pretty_name: Nigerian Common Voice Dataset
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
- ha
- ig
- yo
multilinguality:
- multilingual
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.
size_categories:
- 10K<n<100K
dataset_info:
- config_name: default
  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
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    num_examples: 3
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  download_size: 1127146
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- config_name: english
  features:
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    dtype: audio
  - name: client_id
    dtype: string
  - name: path
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  - name: sentence
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  - name: accent
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  - name: locale
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  download_size: 121504884
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- config_name: hausa
  features:
  - name: audio
    dtype: audio
  - name: client_id
    dtype: string
  - name: path
    dtype: string
  - name: sentence
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  - name: accent
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  - name: locale
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  download_size: 234586970
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- config_name: igbo
  features:
  - name: audio
    dtype: audio
  - name: client_id
    dtype: string
  - name: path
    dtype: string
  - name: sentence
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  - name: accent
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  - name: locale
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- config_name: yoruba
  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
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  - name: test
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  download_size: 147489914
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configs:
- config_name: english
  data_files:
  - split: train
    path: english/train-*
  - split: validation
    path: english/validation-*
  - split: test
    path: english/test-*
- config_name: hausa
  data_files:
  - split: train
    path: hausa/train-*
  - split: validation
    path: hausa/validation-*
  - split: test
    path: hausa/test-*
- config_name: igbo
  data_files:
  - split: train
    path: igbo/train-*
  - split: validation
    path: igbo/validation-*
  - split: test
    path: igbo/test-*
- config_name: yoruba
  data_files:
  - split: train
    path: yoruba/train-*
  - split: validation
    path: yoruba/validation-*
  - split: test
    path: yoruba/test-*
---
# Dataset Card for Nigerian Common Voice Dataset

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [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)
  - [Reference/Disclaimer](#reference-disclaimer)
  - [Contributions](#contributions)

## Dataset Description

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

### Dataset Summary

The Nigerian Common Voice Dataset is a comprehensive dataset consisting of 158 hours of audio recordings and corresponding transcription (sentence). 
This dataset includes metadata like accent, locale that can help improve the accuracy of speech recognition engines. 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.

The dataset currently consists of 158 hours of audio recordings in 4 languages, but more voices and languages are always added. Contributions are welcome.


### Languages

```
English, Hausa, Igbo, Yoruba
```

## 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 Igbo config, simply specify the corresponding language config name (i.e., "igbo" for Igbo):
```python
from datasets import load_dataset
dataset = load_dataset("benjaminogbonna/nigerian_common_voice_dataset", "igbo", 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_common_voice_dataset", "igbo", split="train", streaming=True)
print(next(iter(cv_17)))
```

*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_common_voice_dataset", "igbo", 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_common_voice_dataset", "igbo", 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 Common Voice 16 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` and `locale`.

```python
{
  'client_id': 'user_5256', 
  'path': 'clips/ng_voice_igbo_5257.mp3',
  'audio': {
    'path': 'clips/ng_voice_igbo_5257.mp3', 
    'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32), 
    'sampling_rate': 48000
  },
  'sentence': 'n'ihu ọha mmadụ.', 
  'accent': 'nigerian', 
  'locale': 'igbo', 
}
```

### 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

### 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_common_voice_dataset", "igbo")
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.

### Reference/Disclaimer

Just to state it clearly, "the current languages and voices we have on the Nigerian Common Voice Dataset were not all collected from scratch".
Infact, this wasn't the problem we set out to solve initially. We were working on a speech to speech (stt & tts) conversational model for Nigeria languages, but along the way we had a bottleneck:
1. The few data (audio) available were scattered and from different sources (Kaggle, Hugging Face, and many other websites).
2. The data weren't in the format required by the models.
3. Many of the audios had wrong or no corresponding transcriptions at all.

So while training our model, we had to gather them into one repository, structure them, clean them (remove/edit wrong transcriptions), and trim most of them to 30 seconds chunks.

We figured many people had the same issue, hence we uploaded it to Hugging Face and made it public.

Secondly, we haven't found any publicly available data (audios & transcriptions) for many Nigerian languages that we need (ex. Pidgin, etc). 
So the Nigerian Common Voice Dataset will be an ongoing project to collect as many languages & voices as possible.

Next, in order to add more languages and voices:
1. We will crowd-source from volunteers and contributors.
2. Take advantage of the hundreds of hours of Nigerian movies that are publicly available in different languages.

Our goal here is just to bring this data into one central repository and make it available to the public (researchers, developers, and all).

### Contributions