---
license: other
language:
- ur
- hi
tags:
- urdu
- pakistani-urdu
- speech-to-speech
- text-to-speech
- conversational-ai
- voice-assistant
- gradio
- zerogpu
pipeline_tag: text-to-speech
inference: false
---

# Urdu S2S MVP

Urdu S2S MVP is an experimental Pakistani Urdu voice assistant model release for
conversational speech-to-speech and text-to-speech testing.

The runnable demo is available as a Hugging Face Space:

<https://huggingface.co/spaces/sufinity/urdu-s2s-mvp>

## What It Does

- Accepts recorded or uploaded Urdu speech and returns a spoken Urdu response.
- Generates speech from typed Urdu, Roman Urdu, or Devanagari text.
- Focuses on short assistant-style replies rather than long-form narration.
- Uses a warm female assistant voice style.
- Prioritizes Pakistani Urdu phrasing and pronunciation preferences.
- Avoids assuming the user's gender in normal assistant replies.

## Architecture

Urdu S2S MVP uses a modular speech pipeline with dedicated components for speech
recognition, Urdu response generation, pronunciation-aware speech preparation, and
neural speech synthesis. The user-facing experience is a single speech-to-speech model
demo, while the underlying design lets each part improve independently as stronger Urdu
speech models become available.

At a high level:

- incoming audio is normalized and transcribed
- the dialogue layer produces a concise Urdu assistant response
- a pronunciation layer prepares the response for stable speech generation
- the synthesis layer renders the final spoken voice

This architecture is designed to optimize Urdu conversational quality, pronunciation,
pacing, and voice consistency without requiring a full system retrain for every
improvement.

## Intended Use

This release is intended for product evaluation, demos, and internal iteration on Urdu
voice assistant quality. It is most useful for short everyday assistant interactions:

- greetings
- clarification requests
- customer support prompts
- helpdesk-style prompts
- scheduling drafts
- travel or account-support style questions

## Best Input Format

For speech-to-speech, use clear audio between 2 and 10 seconds. Avoid heavy background
noise, long recordings, overlapping speakers, or very dense factual prompts.

For text-to-speech, Urdu script, Roman Urdu, and Devanagari are accepted. Devanagari
input may give the most stable pronunciation in the current MVP.

## Current Status

This is an MVP model release. The public Hugging Face Model repo documents the model
behavior and links to the runnable Space. Standalone fine-tuned checkpoint weights are
not published in this repo yet.

Future releases may add:

- standalone fine-tuned checkpoint weights
- larger Urdu evaluation sets
- more voice options
- lower-latency serving packages
- pronunciation patches for named entities and mixed English/Urdu phrases

## Known Limitations

- Noisy or clipped recordings can cause transcription errors.
- Long prompts may produce slower responses.
- Mixed Urdu/English prompts can still have occasional pronunciation mistakes.
- Rare names, addresses, IDs, and acronyms may require normalization.
- The first generation after the Space wakes up can be slower while the speech model loads.

## Evaluation Notes

The current release was evaluated manually against a 200-prompt Urdu speech benchmark
focused on short conversational assistant turns. Review focused on:

- Urdu naturalness
- pronunciation
- pacing and pauses
- assistant reply quality
- gender-neutral user addressing
- comparison against frontier speech-to-speech behavior for Urdu

## License And Usage

This repository is published as a model card and demo release. Check the linked Space and
underlying dependencies before using it in production or commercial workflows.