---
license: cc-by-4.0
language:
- ur
- ks
tags:
- TTS
- ASR
- Urdu
- Kashmiri
- TextToSpeech
- AutomaticSpeechRecognition
- speech-synthesis
- speech-recognition
- multilingual
- emotion
- speaking-style
task_categories:
- text-to-speech
- automatic-speech-recognition
- audio-classification
size_categories:
- 10K<n<100K
pretty_name: UrduSpeech

---

# UrduSpeech

## Dataset Summary

**UrduSpeech** is a high-quality multi-style speech corpus for Urdu and Kashmiri languages, designed for text-to-speech (TTS), automatic speech recognition (ASR), and expressive speech synthesis tasks. This dataset contains professionally recorded audio with diverse speaking styles, emotional expressions, and gender representation.

## Dataset Composition

- **Languages:** Urdu, Kashmiri
- **Total Samples:** ~51.6K audio-text pairs
  - Train: 46.5K samples
  - Test: 5.17K samples
- **Dataset Size:** 28.9 GB
- **Audio Quality:** High-quality studio recordings
- **Gender Diversity:** Male and Female speakers
- **Speaking Styles:** Conversational (CONV), Fear (FEAR), Wikipedia (WIKI), Book narration (BOOK), and more

## Source

This dataset is derived from [AI4Bharat's Rasa Dataset](https://huggingface.co/datasets/ai4bharat/Rasa), specifically the Urdu and Kashmiri language subsets.

## Use Cases

This dataset is ideal for:

- 🔊 **Text-to-Speech (TTS)** - Train high-quality Urdu/Kashmiri speech synthesis models
- 🎤 **Automatic Speech Recognition (ASR)** - Develop robust speech-to-text systems
- 🎭 **Expressive Speech Synthesis** - Generate speech with different styles and emotions
- 👥 **Multi-Speaker Models** - Build gender-aware voice systems
- 📚 **Domain-Specific TTS** - Train models for conversational, narrative, or formal speech
- 🧠 **Emotion Recognition** - Develop systems to detect emotional expression in speech
- 🎯 **Style Transfer** - Learn to generate speech in different speaking styles

## Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `filename` | string | Unique identifier for the audio sample (e.g., URD_F_CONV_00045) |
| `text` | string | Urdu/Kashmiri transcription text |
| `language` | string | Language label (Urdu/Kashmiri) |
| `gender` | string | Speaker gender (Male/Female) |
| `style` | string | Speaking style (CONV, FEAR, WIKI, BOOK, etc.) |
| `duration` | string | Audio duration in seconds |
| `wav_path` | string | Original file path reference |
| `audio` | audio | Audio data object |

## Speaking Styles

The dataset includes multiple speaking styles to enable expressive TTS:

- **CONV** (Conversational): Natural, everyday speech patterns
- **FEAR** (Fearful): Emotional expression conveying fear or anxiety
- **WIKI** (Wikipedia): Formal, encyclopedic narration style
- **BOOK** (Book Narration): Literary, storytelling style
- *Additional styles may be present in the dataset*

## Data Examples

### Example 1: Conversational Style
```python
{
  'filename': 'URD_F_CONV_00045',
  'text': 'سچ تو یہ ہے، کہ ہم ہی بیوقوف تھے۔',
  'language': 'Urdu',
  'gender': 'Female',
  'style': 'CONV',
  'duration': '2.739',
  'wav_path': '/data/TTS/ttsteam/datasets/rasa_hf/data_20251203/Urdu/Female/wavs/URD_F_CONV_00045.wav',
  'audio': {...}
}
```

### Example 2: Wikipedia Style
```python
{
  'filename': 'URD_F_WIKI_01270',
  'text': 'احمد شاہ ابدالی نے کُل سات حملے کیے جن میں سے چوتھے اور پانچویں حملے میں دہلی پہنچا۔',
  'language': 'Urdu',
  'gender': 'Female',
  'style': 'WIKI',
  'duration': '6.525',
  'wav_path': '/data/TTS/ttsteam/datasets/rasa_hf/data_20251203/Urdu/Female/wavs/URD_F_WIKI_01270.wav',
  'audio': {...}
}
```

## Data Splits

| Split | Samples | Description |
|-------|---------|-------------|
| train | 46,500 | Training data for model development |
| test | 5,170 | Held-out test set for evaluation |

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load full dataset
dataset = load_dataset("humairawan/UrduSpeech")

# Load specific split
train_data = dataset['train']
test_data = dataset['test']

# Access a sample
sample = train_data[0]
print(f"Text: {sample['text']}")
print(f"Gender: {sample['gender']}")
print(f"Style: {sample['style']}")
print(f"Duration: {sample['duration']} seconds")
```

### Filter by Language

```python
from datasets import load_dataset

# Load dataset
dataset = load_dataset("humairawan/UrduSpeech", split="train")

# Filter for Urdu samples only
urdu_data = dataset.filter(lambda x: x['language'] == 'Urdu')

# Filter for Kashmiri samples only
kashmiri_data = dataset.filter(lambda x: x['language'] == 'Kashmiri')

print(f"Urdu samples: {len(urdu_data)}")
print(f"Kashmiri samples: {len(kashmiri_data)}")
```

### Filter by Gender and Style

```python
from datasets import load_dataset

dataset = load_dataset("humairawan/UrduSpeech", split="train")

# Get female conversational speech
female_conv = dataset.filter(
    lambda x: x['gender'] == 'Female' and x['style'] == 'CONV'
)

# Get male Wikipedia narration
male_wiki = dataset.filter(
    lambda x: x['gender'] == 'Male' and x['style'] == 'WIKI'
)

print(f"Female conversational samples: {len(female_conv)}")
print(f"Male Wikipedia samples: {len(male_wiki)}")
```

### Filter by Duration

```python
from datasets import load_dataset

dataset = load_dataset("humairawan/UrduSpeech", split="train")

# Get samples between 2-10 seconds
filtered = dataset.filter(
    lambda x: 2.0 <= float(x['duration']) <= 10.0
)

print(f"Filtered samples: {len(filtered)}")
```

### Example: Fine-tuning TTS Models

```python
from datasets import load_dataset
import torch
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech

# Load model and processor
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")

# Load dataset
dataset = load_dataset("humairawan/UrduSpeech", split="train")

# Filter high-quality samples (2-10 seconds)
dataset = dataset.filter(lambda x: 2.0 <= float(x['duration']) <= 10.0)

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

# Process dataset
dataset = dataset.map(prepare_dataset)
```

### Example: Style-Conditioned TTS Training

```python
from datasets import load_dataset

# Load dataset
dataset = load_dataset("humairawan/UrduSpeech", split="train")

# Create style-specific subsets
styles = ['CONV', 'WIKI', 'BOOK', 'FEAR']
style_datasets = {
    style: dataset.filter(lambda x: x['style'] == style)
    for style in styles
}

# Train separate models or use style embeddings
for style, style_data in style_datasets.items():
    print(f"{style}: {len(style_data)} samples")
```

### Example: ASR Training with Whisper

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

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

# Load dataset
dataset = load_dataset("humairawan/UrduSpeech", split="train")

# Preprocessing function
def prepare_asr_dataset(batch):
    audio = batch["audio"]
    # Process audio
    batch["input_features"] = processor(
        audio["array"],
        sampling_rate=audio["sampling_rate"],
        return_tensors="pt"
    ).input_features[0]
    # Process text
    batch["labels"] = processor.tokenizer(batch["text"]).input_ids
    return batch

# Apply preprocessing
dataset = dataset.map(prepare_asr_dataset, remove_columns=["audio"])
```

## Dataset Statistics

- **Total Duration:** Extensive audio coverage across both languages
- **Average Duration:** ~5-7 seconds per sample
- **Gender Distribution:** Balanced male and female representation
- **Style Diversity:** Multiple speaking styles for expressive synthesis
- **Language Coverage:** Comprehensive Urdu and Kashmiri lexicon
- **Audio Quality:** High-fidelity studio recordings

## Naming Convention

Audio filenames follow the pattern: `{LANGUAGE}_{GENDER}_{STYLE}_{ID}`

Example: `URD_F_CONV_00045`
- `URD` = Urdu language
- `F` = Female speaker
- `CONV` = Conversational style
- `00045` = Sample ID

## Licensing & Attribution

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

**Source:** Derived from [AI4Bharat's Rasa Dataset](https://huggingface.co/datasets/ai4bharat/Rasa)

**Citation:**

```bibtex
@dataset{urduspeech2025,
  title        = {UrduSpeech: Multi-Style Urdu and Kashmiri Speech Corpus},
  author       = {Humair Munir},
  author       = {humair025},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/humairawan/UrduSpeech},
  note         = {Derived from AI4Bharat Rasa Dataset}
}
```

Please also cite the original Rasa dataset:

```bibtex
@misc{ai4bharat2024rasa,
  title        = {Rasa: Building Expressive Speech Synthesis Systems for Indian Languages in Low-Resource Settings},
  author       = {AI4Bharat},
  year         = {2024},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/ai4bharat/Rasa}
}
```

## Ethical Considerations

- This dataset is intended for research and development purposes
- Users should ensure compliance with privacy regulations when deploying models
- The dataset includes emotional expressions (fear) - use responsibly
- Consider cultural context when deploying applications built with this data
- Ensure appropriate content warnings for applications using emotional speech

## Limitations

- Speaker diversity is limited to the original Rasa dataset speakers
- Not all speaking styles may be evenly distributed
- Kashmiri language samples may be fewer than Urdu
- Domain coverage is limited to the source material styles
- Some emotional expressions may not represent all cultural contexts

## Technical Specifications

- **Audio Format:** WAV format
- **Recommended Sampling Rate:** 16kHz or 22kHz for TTS applications
- **Text Encoding:** UTF-8 (Urdu script)
- **File Format:** Parquet
- **Total Size:** 28.9 GB

## Recommended Preprocessing

```python
# Recommended filtering for TTS training
filtered = dataset.filter(
    lambda x: 2.0 <= float(x['duration']) <= 10.0
)

# For ASR, you might want shorter samples
asr_filtered = dataset.filter(
    lambda x: 1.0 <= float(x['duration']) <= 15.0
)

# For style-specific models
style_filtered = dataset.filter(
    lambda x: x['style'] in ['CONV', 'WIKI', 'BOOK']
)
```

## Related Datasets

### UrduMegaSpeech-1M

For large-scale Urdu speech recognition and translation tasks, see our companion dataset:

🔗 **[UrduMegaSpeech-1M](https://huggingface.co/datasets/humair025/UrduMegaSpeech)** - A large-scale Urdu-English parallel speech corpus with 1M+ samples

**When to use which dataset:**

| Feature | UrduSpeech (This Dataset) | UrduMegaSpeech-1M |
|---------|---------------------------|-------------------|
| **Size** | 51.6K samples | 1M+ samples |
| **Primary Use** | TTS, Expressive Speech | ASR, Translation |
| **Languages** | Urdu, Kashmiri | Urdu-English parallel |
| **Speaking Styles** | Multiple (CONV, WIKI, BOOK, FEAR) | General domain |
| **Quality** | High-quality studio recordings | Variable quality with scores |
| **Best For** | Voice synthesis, style transfer | Large-scale ASR, translation |

**Recommendation:**
- Use **UrduSpeech** for text-to-speech, expressive synthesis, and style-conditioned models
- Use **UrduMegaSpeech-1M** for robust ASR training, speech translation, and when you need large-scale data

## Acknowledgments

This dataset is curated from AI4Bharat's Rasa project, which aims to build expressive speech synthesis systems for Indian languages. We acknowledge their contribution to low-resource language technology.

---

**Dataset Curated By:** Humair Munir  
**Original Source:** AI4Bharat Rasa Dataset  
**Last Updated:** December 2025  
**Version:** 1.0