---
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:
```python
{
    '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:
1. **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
2. **Batch Organization:** Files organized into ~1.5-2GB batches for efficient streaming and downloading
3. **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
```python
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
```python
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
```python
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}
}
```