---
language:
- pa
license: apache-2.0
task_categories:
- text-to-speech
- automatic-speech-recognition
- audio-classification
multilinguality:
- monolingual
size_categories:
- n<1K
tags:
- punjabi
- gurmukhi
- punjabi-voice
- punjabi-audio
- synthetic-speech
- tts
- pure-punjabi
- speech-recognition
pretty_name: "Punjabi (Gurmukhi) Synthetic Multi-Generation Voice Corpus"
---

# 🎙️ Punjabi (Gurmukhi) Synthetic Multi-Generation Voice Corpus
## ☬ ਸ਼ੁੱਧ ਗੁਰਮੁਖੀ ਮਹਾਨ ਕੋਸ਼ ਸਿੰਥੈਟਿਕ ਵੌਇਸ ਡਾਟਾਸੈੱਟ

> ⚠️ **Corrected 2026-09-02.** The original README described this as
> "5 distinct acoustic age & gender profiles" of "Studio Master" recordings.
> That was false. This is **100% synthetic, machine-generated speech** —
> not a multi-speaker human recording. See "How the audio was made" below.

A Punjabi (Gurmukhi) synthetic speech corpus built from **two underlying
text-to-speech engines**, covering everyday conversational, health-coach,
and smart-device-prompt style sentences in pure Gurmukhi vocabulary
(minimizing Hindi loan words).

---

## 🔊 How the audio was actually made

* **Base voice A (female):** Google's cloud TTS via `gTTS`, Punjabi (`pa`).
* **Base voice B (male):** Meta's `facebook/mms-tts-pan` VITS model.
* **"Child", "Elder Male / Baba Ji", "Elder Female / Bibi Ji" variants are
  NOT separate speakers or separate TTS models.** They are the same base
  gTTS voice, pitch/tempo-shifted with ffmpeg audio filters
  (e.g. `asetrate=16000*1.28,atempo=1/1.28` for the "child" variant,
  `asetrate=16000*0.82,atempo=1/0.82` for "elder male"). This is a DSP
  trick applied to one voice, not a real distinct demographic speaker.

If you need genuinely distinct human speakers for voice-model training,
this dataset is not that — treat it as labeled synthetic TTS output only.

---

## 📊 Dataset Inventory
* **Total Audio Clips:** synthetic WAV files (16kHz Mono PCM), generated via gTTS + MMS-TTS as described above.
* **Metadata:** `metadata.csv` with Gurmukhi text, category, and audio durations.

---

## 🚀 Quick Usage in Python
```python
from datasets import load_dataset

dataset = load_dataset("Nam-toon-studio/Punjabi-Studio-Voice-Corpus")
print(dataset['train'][0])
```