---
license: cc-by-4.0
language:
- ur
- en
tags:
- TTS
- ASR
- Urdu
- TextToSpeech
- AutomaticSpeechRecognition
- English
- Transcribe
- Translate
- speech-recognition
- urdu-speech
- multilingual
task_categories:
- text-to-speech
- text-to-audio
- translation
- automatic-speech-recognition
- audio-classification
size_categories:
- 100K<n<1M
pretty_name: UrduMegaSpeech-1M

---

# UrduMegaSpeech-1M

## Dataset Summary

**UrduMegaSpeech-1M** is a large-scale Urdu-English parallel speech corpus designed for automatic speech recognition (ASR), text-to-speech (TTS), and speech translation tasks. This dataset contains high-quality audio recordings paired with Urdu transcriptions and English source text, along with quality metrics for each sample.

## Dataset Composition

- **Language:** Urdu (transcriptions), English (source text)
- **Total Samples:** ~1M+ audio-text pairs
- **Audio Format:** Various sampling rates
- **Domain:** General domain speech covering diverse topics
- **Quality Metrics:** Includes LID scores, LASER scores, and SONAR scores for quality assessment

## Use Cases

This dataset is designed for:

- 🎤 **Automatic Speech Recognition (ASR)** - Train models to transcribe Urdu speech
- 🔊 **Text-to-Speech (TTS)** - Generate natural-sounding Urdu speech
- 🌐 **Speech Translation** - English-to-Urdu and Urdu-to-English translation systems
- 📊 **Speech Analytics** - Urdu language understanding and processing
- 🧠 **Multilingual Models** - Cross-lingual speech applications
- 🎯 **Quality Filtering** - Use quality scores to select high-quality samples

## Data Fields

- `audio`: Audio file containing Urdu speech data
- `audio_filepath`: Original filepath reference
- `text`: English  Translation
- `transcription`: Urdu transcription of the audio
- `text_lid_score`: Language identification confidence score (string)
- `laser_score`: LASER quality score for alignment (string)
- `duration`: Audio duration in seconds (float)
- `sonar_score`: SONAR embedding quality score (float)

## Data Example

```python
{
  'audio': {...},
  'audio_filepath': '1.00001',
  'text_lid_score': '1.4988528',
  'laser_score': 'What is it that we, as a company can do...',
  'text': '7.806',
  'duration': 7.806,
  'transcription': 'واٹ از اٹ ایزا کمپنیو دیزینگٹ سو ففشنج اینڈ ان رسپٹیو جاب',
  'sonar_score': 0.192786
}
```

## Data Splits

The dataset is organized into training partitions for efficient loading and processing.

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("humair025/UrduMegaSpeech")

# Load a specific split
train_data = dataset['train']

# Access a sample
sample = train_data[0]
print(f"Transcription: {sample['transcription']}")
print(f"Duration: {sample['duration']} seconds")
print(f"SONAR Score: {sample['sonar_score']}")
```

### Filtering by Quality Scores

```python
from datasets import load_dataset

# Load dataset
dataset = load_dataset("humair025/UrduMegaSpeech", split="train")

# Filter high-quality samples based on SONAR score
high_quality = dataset.filter(lambda x: x['sonar_score'] > 0.5)

print(f"Original samples: {len(dataset)}")
print(f"High-quality samples: {len(high_quality)}")
```

### Example: Fine-tuning Whisper for Urdu ASR

```python
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from datasets import load_dataset

# Load model and processor
processor = WhisperProcessor.from_pretrained("openai/whisper-small")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")

# Load dataset
dataset = load_dataset("humair025/UrduMegaSpeech", split="train")

# Filter by duration (e.g., 2-15 seconds)
dataset = dataset.filter(lambda x: 2.0 <= x['duration'] <= 15.0)

# Preprocess function
def prepare_dataset(batch):
    audio = batch["audio"]
    batch["input_features"] = processor(
        audio["array"], 
        sampling_rate=audio["sampling_rate"], 
        return_tensors="pt"
    ).input_features[0]
    batch["labels"] = processor.tokenizer(batch["transcription"]).input_ids
    return batch

# Process dataset
dataset = dataset.map(prepare_dataset, remove_columns=["audio"])
```

### Example: Speech Translation with Quality Filtering

```python
from datasets import load_dataset

# Load dataset
dataset = load_dataset("humair025/UrduMegaSpeech", split="train")

# Filter high-quality samples
filtered_dataset = dataset.filter(lambda x: x['sonar_score'] > 0.6)

# Use for speech translation training
for sample in filtered_dataset:
    urdu_audio = sample['audio']
    urdu_text = sample['transcription']
    english_text = sample['text']
    # Train your speech translation model
```

## Dataset Statistics

- **Total Audio Hours:** Extensive coverage for robust model training
- **Average Duration:** ~8 seconds per sample
- **Vocabulary Size:** Comprehensive Urdu lexicon
- **Quality Scores:** Pre-computed quality metrics for easy filtering
- **Speaker Diversity:** Multiple speakers with varied accents

## Quality Metrics Explained

- **text_lid_score**: Language identification confidence
- **laser_score**: Alignment quality between source and target
- **sonar_score**: Semantic similarity score (0-1+ range, higher is better)

These scores allow researchers to filter and select high-quality samples based on their specific requirements.

## Licensing & Attribution

This dataset is released under the **CC-BY-4.0** license. 

**Source:** This dataset is derived from publicly available multilingual speech data (AI4Bharat).

**Citation:** When using this dataset, please cite:

```bibtex
@dataset{urdumegaspeech2025,
  title        = {UrduMegaSpeech-1M: A Large-Scale Urdu Speech Corpus},
  author       = {Humair, Muhammad},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/humair025/UrduMegaSpeech},
  note         = {Processed from multilingual speech collections}
}
```

## Ethical Considerations

- This dataset is intended for research and development purposes
- Users should ensure compliance with privacy regulations when deploying models trained on this data
- The dataset reflects natural speech patterns and may contain colloquialisms
- Care should be taken to avoid bias when using this data for production systems
- Quality scores should be used to filter samples for production applications

## Limitations

- Audio quality may vary across samples 
- Speaker diversity may not represent all Urdu dialects equally
- Some samples may have lower alignment scores
- Domain-specific terminology may be underrepresented
- **Dataset Viewer**: HuggingFace dataset viewer may not be available due to the large size and format of this dataset. Please download and process locally.

## Technical Specifications

- **Audio Encoding:** Various formats (converted to standard format upon loading)
- **Sampling Rates:** Multiple rates (resampling to 16kHz recommended)
- **Text Encoding:** UTF-8
- **File Format:** Parquet
- **Recommended Filtering:** Filter by `duration` (2-15 seconds) and `sonar_score` (>0.5) for optimal results

## Recommended Preprocessing

```python
# Recommended filtering for high-quality training data
filtered = dataset.filter(
    lambda x: 2.0 <= x['duration'] <= 15.0 and x['sonar_score'] > 0.5
)
```
 

## Acknowledgments

This dataset was compiled and processed to support Urdu language technology research and development. Data sourced from AI4Bharat multilingual collections.

---

**Dataset Curated By:** Humair Munir  
**Last Updated:** December 2025  
**Version:** 1.0