PawanKrd/asr-ckb-v2

Dataset Summary

The ASR-CKB-V2 dataset is a comprehensive collection of audio recordings and their corresponding transcriptions in Central Kurdish (Sorani). It is designed to facilitate research and development in automatic speech recognition (ASR) for the Central Kurdish language.

Dataset Structure

Features

• audio: Audio recordings with a sampling rate of 16,000 Hz.
• sentence: Textual transcriptions of the audio recordings.

Splits

The dataset is divided into the following splits:

• Train: 216,264 examples, 11,953,431,363.088 bytes
• Test: 600 examples, 113,408,682 bytes (sourced from the test split of the AsoSoft dataset razhan/asosoft-speech)

Dataset Size

• Download Size: 9,882,494,390 bytes
• Dataset Size: 12,066,840,045.088 bytes

Dataset Details

Data Preprocessing

• Audio: The audio files were standardized to a sampling rate of 16,000 Hz.
• Text: The transcriptions were cleaned and normalized to match the spoken content accurately.

Intended Uses

This dataset is intended for training, evaluating, and benchmarking automatic speech recognition systems for the Central Kurdish language. It can be used to develop models capable of transcribing Central Kurdish audio accurately.

Limitations

• Diversity: While the dataset includes a variety of accents and speaking styles, there may still be underrepresented dialects and variations.
• Noise: Some audio samples may contain background noise, which could impact the performance of ASR models.

Acknowledgements

We extend our gratitude to all contributors and annotators who made this dataset possible. Their efforts in collecting and transcribing the audio samples are invaluable.

Example Usage

To load and use the ASR-CKB-V2 dataset, you can follow the example code below:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("PawanKrd/asr-ckb-v2")

# Access the train and test splits
train_dataset = dataset["train"]
test_dataset = dataset["test"]

# Example: Play the first audio sample and print its transcription
import IPython.display as ipd

audio_sample = train_dataset[0]["audio"]
transcription = train_dataset[0]["sentence"]

# Play audio
ipd.Audio(audio_sample["array"], rate=audio_sample["sampling_rate"])

# Print transcription
print(transcription)