Urdu TTS Dataset
A high-quality Urdu speech dataset for Text-to-Speech (TTS) model training, built from YouTube audiobooks and narrations.
Dataset Statistics
| Property | Value |
|---|---|
| Language | Urdu (ur) |
| Total Samples | 50,326 |
| Total Duration | 79.99 hours |
| Sample Rate | 22050 Hz |
| Audio Format | WAV (16-bit PCM, mono) |
Columns
| Column | Type | Description |
|---|---|---|
audio |
Audio | WAV file at 22050 Hz |
transcript |
string | Raw Urdu transcription (Whisper) |
normalized_text |
string | Cleaned and normalized Urdu text |
audio_id |
string | Unique segment identifier |
duration_sec |
float | Clip duration in seconds |
snr_db |
float | Estimated signal-to-noise ratio |
source_file |
string | Source YouTube video filename |
language |
string | Always "ur" |
Usage
from datasets import load_dataset
dataset = load_dataset("codewithdark/UrduTTS")
print(dataset["train"][0])
Pipeline
- YouTube audio download (yt-dlp)
- Voice Activity Detection segmentation (1.5–15 sec clips)
- SNR quality filtering
- Urdu transcription (OpenAI Whisper medium/large)
- Text normalization and deduplication
- Incremental shard upload to HuggingFace
License
CC BY 4.0 — Please respect the rights of original content creators.
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