metadata

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: transcription
      dtype: string
    - name: up_votes
      dtype: int64
    - name: down_votes
      dtype: int64
    - name: age
      dtype: string
    - name: gender
      dtype: string
    - name: accents
      dtype: string
    - name: variant
      dtype: float64
    - name: locale
      dtype: string
    - name: segment
      dtype: float64
  splits:
    - name: train
      num_bytes: 133629462.356
      num_examples: 5324
    - name: validation
      num_bytes: 1039373547.526
      num_examples: 42418
    - name: test
      num_bytes: 107435663.014
      num_examples: 4031
  download_size: 1266451644
  dataset_size: 1280438672.896
task_categories:
  - conversational
  - translation
language:
  - ur
tags:
  - code

Dataset Card for AudioDataset-15

Table of Contents
Dataset Description
- Dataset Summary
- Languages
Dataset Structure

Dataset Description

Dataset Summary

The dataset in question is an audio dataset consisting of recordings in the Urdu language. It has been sourced from Mozilla's Common Voice, a publicly available voice dataset that relies on the contributions of volunteers from various parts of the world. The primary purpose of this dataset is to support the development of voice applications by providing a valuable resource for training machine learning models.

The dataset's intended use is to facilitate voice-to-text conversion in the Urdu language. By utilizing this dataset, researchers, developers, and anyone interested in voice technology can train models that accurately convert spoken Urdu words into written text. This can have significant applications in various domains, such as speech recognition, transcription services, language learning tools, and more.

Languages

The dataset consists of audio recordings in the Urdu language. Urdu is a language primarily spoken in Pakistan and parts of India. It is one of the 22 officially recognized languages in India and is also widely spoken by the Pakistani diaspora around the world.

The dataset is primarily focused on spoken Urdu, which encompasses a wide range of topics and genres. It is important to note that the dataset's content may vary, covering conversations, speeches, interviews, narratives, and other forms of vocal communication in the Urdu language.

Dataset Structure

Data Instances

{ "client_id": "0c9690e5a2d1bb3ce418954a2b70acae53153708f6c3a21c9e8fe7e3912d97ba805ace5091772c8d4e16dc07fc906ca4956335b87821c244eee8129a15fcb0cf", "file_name": "data/test/common_voice_ur_26641307.mp3", "transcription": "تو ان کے حلاج مدلوں کا کیا حال ہے؟", "up_votes": 2, "down_votes": 0, "age": "twenties", "gender": "female", "accent": "", "locale": "ur", "segment": "" }

Data Fields

client_id: A unique identifier for the client or contributor who provided the recording. (Data Type: String)

file_name: The file name or path of the audio file. (Data Type: String)

transcription: The transcription of the spoken content in the Urdu language. (Data Type: String)

up_votes: The number of upvotes received for the recording. (Data Type: Integer)

down_votes: The number of downvotes received for the recording. (Data Type: Integer)

age: The age group of the speaker. (Data Type: String)

gender: The gender of the speaker. (Data Type: String)

accent: The accent of the speaker, if applicable. (Data Type: String)

locale: The locale or language code, which is "ur" for Urdu in this case. (Data Type: String)

segment: Additional segment information, if available. (Data Type: String)

Data Splits

The dataset is divided into three splits: train, test, and validation. The training set is used to train the model, the validation set is used to tune hyperparameters and evaluate model performance during training, and the test set is used to evaluate the final model's performance after training.

	train	validation	test
Amount	5324	42418	4031