Datasets:

humairawan
/

Urdu-LjSpeech

language:
  - ur
license: cc-by-4.0
task_categories:
  - text-to-speech
  - automatic-speech-recognition
pretty_name: Urdu LjSpeech
size_categories:
  - 10K<n<100K
tags:
  - audio
  - speech
  - urdu
  - tts
  - asr
  - speech-synthesis

Urdu-LjSpeech Dataset

Dataset Description

Urdu-LjSpeech is a high-quality Urdu speech dataset designed for Text-to-Speech (TTS) and Automatic Speech Recognition (ASR) tasks. The dataset contains Urdu audio recordings paired with their corresponding text transcriptions.

Dataset Summary

Language: Urdu (اردو)
Format: Audio files with text transcriptions
Audio Specifications:
- Sampling Rate: 22,050 Hz
- Format: PCM 16-bit
- Channels: Mono
Use Cases: Text-to-Speech synthesis, Speech Recognition, Voice Cloning, Prosody Analysis

Supported Tasks

Text-to-Speech (TTS): Train models to synthesize natural-sounding Urdu speech
Automatic Speech Recognition (ASR): Develop speech-to-text systems for Urdu
Voice Conversion: Train voice cloning and conversion models
Linguistic Research: Study Urdu phonetics and prosody

Dataset Structure

Data Instances

Each instance in the dataset contains:

{
    'audio': {
        'array': array([...]), # Audio waveform
        'sampling_rate': 22050
    },
    'text': 'تم ہاتھ میں پتھر اٹھاتے ہو', # Urdu transcription
    'speaker': 'alloy', # Speaker identifier
    'id': 0 # Unique sample ID
}

Data Fields

Field	Type	Description
`audio`	Audio	Audio recording at 22,050 Hz sampling rate
`text`	string	Urdu text transcription in UTF-8
`speaker`	string	Speaker identifier
`id`	int	Unique identifier for the sample

Data Splits

The dataset is organized in batches for efficient loading:

dataset/
├── batch_0/
├── batch_1/
├── batch_2/
└── ...

Each batch contains approximately 1.5-2 GB of audio data.

Dataset Creation

Source Data

This dataset is a processed and validated version of speech recordings with careful quality control measures applied.

Data Collection

Audio recordings were collected and validated for quality
Each audio file was paired with its corresponding Urdu text transcription
Quality validation includes:
- Minimum audio duration check (>0.1 seconds)
- PCM format validation
- Corrupted audio removal
- Text-audio alignment verification

Data Processing

The dataset underwent several processing steps:

Audio Validation: Each audio sample was validated for:
- Sufficient duration (minimum 0.1 seconds)
- Valid PCM format (even byte length for 16-bit samples)
- No corruption or empty data
Batch Organization: Files organized into ~1.5-2GB batches for efficient streaming and downloading
Format Standardization: All audio normalized to:
- 22,050 Hz sampling rate
- 16-bit PCM format
- Mono channel

Annotations

Text transcriptions are in standard Urdu script (UTF-8 encoded) with proper diacritical marks where applicable.

Usage

Loading the Dataset

from datasets import load_dataset
# Load the full dataset
dataset = load_dataset("humairawan/Urdu-LjSpeech")
# Load a specific batch
dataset = load_dataset("humairawan/Urdu-LjSpeech", data_dir="batch_0")
# Access samples
sample = dataset['train'][0]
print(sample['text']) # Print Urdu text
audio_array = sample['audio']['array'] # Access audio waveform
sampling_rate = sample['audio']['sampling_rate'] # Get sampling rate

Training a TTS Model

from datasets import load_dataset
from transformers import SpeechT5ForTextToSpeech, SpeechT5Processor
# Load dataset
dataset = load_dataset("humairawan/Urdu-LjSpeech")
# Initialize model and processor
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
# Your training code here...

Training an ASR Model

from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Load dataset
dataset = load_dataset("humairawan/Urdu-LjSpeech")
# Initialize model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base")
# Your training code here...

Considerations

Ethical Considerations

This dataset is intended for research and development of Urdu language technologies
Users should be aware of potential biases in speaker representation
Commercial use should respect speaker rights and consent

Citation

If you use this dataset in your research or applications, please cite it using the following BibTeX entry:

@dataset{awan2024urdu_ljspeech,
  author       = {Humair Munir},
  title        = {Urdu-LjSpeech: A High-Quality Urdu Speech Dataset for TTS and ASR},
  month        = dec,
  year         = 2024,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/humairawan/Urdu-LjSpeech},
  note         = {Derived from : https://huggingface.co/datasets/humair025/Urdu-LjSpeech}
}