---
language:
- ur
license: mit
task_categories:
- audio-classification
tags:
- urdu
- turn-detection
- voice-activity-detection
- conversational-ai
- end-of-turn
- smart-turn
- audio
- dataset
---

# 🗣️ Urdu Turn Detection (Audio Dataset V2)

This is the official dataset for the model **[PuristanLabs1/urdu-turn-v2]**(https://huggingface.co/PuristanLabs1/urdu-turn-v2), a high precision, low latency system for detecting the end of a conversational turn in Urdu speech. 

It contains **11,479 audio clips** (balanced between `Complete` and `Incomplete`) specifically designed to train robust models for realtime Voice AI applications like "Smart Turn" or "Barge-in" detection.

## 🚀 How This Dataset Was Compiled

The creation of this dataset involved a multi stage pipeline combining human validated Urdu text, synthetic dialogue generation, and advanced acoustic augmentation.

### 1. The Core Text Corpus (10,000 Samples)
- **Source**: 2,825 human validated Urdu sentences (Gold Standard) combined with 7,175 synthetic samples generated by **Google Gemini 2.5 Flash Lite**.
- **Diversity**: Covers daily life, news, formal requests, and casual "thinking out loud" scenarios.
- **Language**: 100% Urdu script (Nastaliq/Arabic), strictly filtered to remove English or Roman Urdu artifacts.

### 2. Acoustic Realization (VAD Prep)
- We converted the text into audio using high quality Urdu TTS endpoints.
- Each sentence was transformed into a 16kHz mono `.wav` file.
- **Problem**: Standard TTS audio ends perfectly. To simulate realworld speech, we needed to simulate "trailing off."

### 3. Negative Sampling & Truncation
To teach the model what an **Incomplete** turn sounds like, we implemented a strategic truncation algorithm:
- **Full Clips (Label 1)**: Sentences played to completion with natural closure.
- **Truncated Clips (Label 0)**: Sentences cut off mid-phrase at semantic boundaries (e.g., stopping after "میں..." or "اگر وہ..."). This forces the model to learn the prosodic and phonetic cues of a non-terminal sound.

### 4. Fixing the "Silence Bias" (V2 Upgrade)
A critical step taken was the injection of **Synthetic Silence & Thinking Noise**:
- **Silence Injection**: Added 1,500+ clips of pure, varied energy silence labeled as **Incomplete (0)**.
- **Strategic Truncation**: Sentences were cut off mid phrase at semantic boundaries to create negative samples.
- **Thinking Noise**: Injected "hmmm" and "uhh" fillers to ensure the model doesn't trigger a turn end just because the speaker is thinking. Basically Pure silence and "thinking noise" (umms/ahhs) were labeled as **Incomplete (0)** to prevent false triggers during pauses.
- **Padding Masking**: All audio is padded to 3.0 seconds, with an explicit `attention_mask` generated to teach the model to ignore non-voice segments.
- **Gemini 2.5 Augmentation**: Synthetic dialogues were generated to broaden the linguistic variety beyond standard text corpora.

## 📊 Dataset Statistics

| Feature | Details |
| :--- | :--- |
| **Total Clips**| 11,479 |
| **Sampling Rate** | 16,000 Hz |
| **Format** | Mono WAV / PCM_16(WebDataset TAR Shards) |
| **Labels** | `0: INCOMPLETE`, `1: COMPLETE` |
| **Avg Duration** | 2.5 seconds |
| **Balance** | 57% Complete (6,539) / 43% Incomplete (4,940) |

## 📁 Repository Structure

The dataset is stored in **WebDataset** format (TAR shards) for optimal streaming:
- `train-0000.tar` to `train-0005.tar`: Audio shards.
- `metadata.csv`: Combined metadata.
- Each sample inside a shard contains:
  - `XXXX.wav`: Audio clip.
  - `XXXX.txt`: Urdu transcription.
  - `XXXX.cls`: Label (0 or 1).

## 🛠️ Installation & Usage

### ⚙️ Dependencies (Crucial for Windows)

The `datasets` library uses `torchcodec` for audio decoding. **Windows users often face issues** because `torchcodec` depends on FFmpeg DLLs.

#### 1. Install Python Packages:
```bash
pip install datasets librosa torchcodec
or

!pip install -q datasets[audio] librosa Transformers

```

#### 2. Install FFmpeg (Required for Windows):
If you see `ImportError: To support decoding audio data, please install 'torchcodec'` or `invalid header` errors:
- **Using Chocolatey**: `choco install ffmpeg-full`
- **Manual**: Download the "full-shared" build from [gyan.dev](https://www.gyan.dev/ffmpeg/builds/), extract it, and add the `bin` folder to your System PATH.

---

## 🚀 Loading the Dataset

### Option 1: Streaming Mode (Recommended)
This is the fastest way to start. It fetches audio on the fly without downloading the whole 1GB archive.

```python
from datasets import load_dataset
import io
import librosa

# Load in streaming mode
ds = load_dataset("PuristanLabs1/urdu-turn-detection-audio-v2", streaming=True, split="train")

for sample in ds.take(5):
    # Access audio data
    audio_array = sample["wav"]["array"]
    sampling_rate = sample["wav"]["sampling_rate"]
    
    # Access metadata
    text = sample["txt"]
    label = int(sample["cls"]) # 0: Incomplete, 1: Complete
    
    print(f"Text: {text} | Label: {label}")

### 🔊 Playing the Audio

Once you have the `audio_array` from the sample:

#### **On Google Colab:**
```python
import IPython.display as ipd
ipd.display(ipd.Audio(audio_array, rate=sampling_rate))
```

#### **On Local Machine (Laptop):**
*   **Windows**: `pip install sounddevice`
*   **Linux (Ubuntu/Debian)**: `sudo apt-get install libportaudio2` then `pip install sounddevice`
```python
import sounddevice as sd
sd.play(audio_array, sampling_rate)
sd.wait() # Wait for audio to finish
```

### **Option 2: Full Download** 
Download the entire dataset for offline training.
```python
from datasets import load_dataset

ds = load_dataset("PuristanLabs1/urdu-turn-detection-audio-v2")
print(f"Total samples: {len(ds['train'])}")
```

---

## 🤝 Acknowledgments & Credits

- **Curated By**: [PuristanLabs](https://huggingface.co/PuristanLabs1).
- **Base Audio**: Custom TTS on our [PuristanLabs1/Urdu-Turn-Detection-10k](https://huggingface.co/datasets/PuristanLabs1/Urdu-Turn-Detection-10k) & Common Voice 13 (Urdu Subset)
- **Augmentation**: Synthetic generation via Google Gemini 2.5.
- **Core Engine**: Optimized for the [`urdu-turn-detector`](https://github.com/PuristanLabs1/urdu-turn-detection) library.

---
**License**: MIT. Free for research and commercial use.