urdu-s2s-mvp

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.