Model Card for MMS TTS Urdu
Model Details
Model Description
This is a Urdu text-to-speech (TTS) model fine-tuned from Facebook MMS (Massively Multilingual Speech) VITS for Urdu written in Arabic script.
The model converts Urdu text into natural-sounding speech using a non-autoregressive VITS-based architecture.
- Developed by: hamza-amin
- Model type: Text-to-Speech (TTS)
- Language(s): Urdu (ur)
- Finetuned from model: facebook/mms-tts-urd-script_arabic
- Framework: 🤗 Transformers
Model Sources
- Base model: facebook/mms-tts-urd-script_arabic
- Architecture paper: https://arxiv.org/abs/1910.09700
Uses
Direct Use
- Generating Urdu speech from Unicode Urdu text
- Offline or server-side Urdu TTS inference
- Research and educational purposes
Out-of-Scope Use
- Roman Urdu input
- Languages other than Urdu
- Voice cloning or speaker identification
Bias, Risks, and Limitations
- Output quality depends heavily on training data quality.
- The model reflects linguistic and pronunciation patterns present in the dataset.
- Not evaluated for fairness across regional accents or dialects.
Recommendations
Users should perform qualitative evaluation before deployment in user-facing applications.
How to Get Started with the Model
from transformers import VitsModel, AutoTokenizer, AutoFeatureExtractor
import torch, soundfile as sf
model_id = "hamza-amin/mms-tts-urd-fine-tuned"
tokenizer = AutoTokenizer.from_pretrained(model_id)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
model = VitsModel.from_pretrained(model_id)
inputs = tokenizer("میں اردو بول سکتا ہوں۔", return_tensors="pt")
with torch.no_grad():
output = model(**inputs)
sf.write(
"output.wav",
output.waveform.squeeze().numpy(),
feature_extractor.sampling_rate
)
Training Details
Training Data
- Dataset: codewithdark/urdu-tts
- Language: Urdu (Arabic script)
Training Procedure
- Fine-tuned using Hugging Face
finetune-hf-vits - Generator-only model exported for inference
Training Hyperparameters
- Training regime: fp32
Evaluation
No formal quantitative evaluation was performed. Evaluation was based on qualitative listening tests.
Technical Specifications
Model Architecture and Objective
- VITS (Variational Inference with Adversarial Learning for End-to-End TTS)
- Objective: text-to-waveform generation
Software
- transformers
- PyTorch
Citation
If you use this model, please also cite the MMS and VITS papers:
@article{pratap2023mms,
title={Scaling Speech Technology to 1,000+ Languages},
author={Pratap et al.},
journal={arXiv preprint arXiv:2305.13516},
year={2023}
}
Model Card Authors
hamza-amin
Model Card Contact
Via Hugging Face profile: https://huggingface.co/hamza-amin