Thiva-indic by Cortiqa (FP16 Studio Quality)

Thiva-indic is a high-fidelity, on-device Text-to-Speech (TTS) model optimized for the Cortiqa Voice Studio ecosystem. It provides clear, natural, and expressive synthetic speech across 17 Indian languages and English.

The model is optimized in FP16 half-precision, reducing memory footprint by over 50 percent while preserving original studio acoustics.

Key Features

• 17 Languages in One Model: Generates speech in 16 major Indian languages plus English without separate voice packs.
• Studio-Grade Audio: 44.1 kHz clean sampling rate with natural breathing and human-like intonation.
• On-Device and Offline Ready: Engineered for local edge/mobile devices (Android, iOS, laptops) with zero server dependencies.
• Free for Commercial Use: Released under the permissive Apache 2.0 License.

Supported Languages

#	Language	Native Script	Code
1	Hindi	हिन्दी	hi
2	Bengali	বাংলা	bn
3	Tamil	தமிழ்	ta
4	Telugu	తెలుగు	te
5	Marathi	मराठी	mr
6	Gujarati	ગુજરાતી	gu
7	Kannada	ಕನ್ನಡ	kn
8	Malayalam	മലയാളം	ml
9	Odia	ଓଡ଼ିଆ	or
10	Punjabi	ਪੰਜਾਬੀ	pa
11	Assamese	অসমীয়া	as
12	Urdu	اردو	ur
13	Kashmiri	कॉशुर / کٲشُر	ks
14	Nepali	नेपाली	ne
15	Sanskrit	संस्कृतम्	sa
16	Sindhi	سنڌي	sd
17	English	English	en

Quickstart and Inference

import torch
import soundfile as sf
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer

device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
model = ParlerTTSForConditionalGeneration.from_pretrained('cortiqa/Thiva-indic').to(device).half()
tokenizer = AutoTokenizer.from_pretrained('cortiqa/Thiva-indic')
description_tokenizer = AutoTokenizer.from_pretrained(model.config.text_encoder._name_or_path)

prompt = 'नमस्ते! कोर्टिका के थिवा-इंडिक मॉडल में आपका स्वागत है।'
description = 'Divya speaks with a clear, pleasant, natural and articulate voice at a steady pace in a quiet studio.'

desc_inputs = description_tokenizer(description, return_tensors='pt').to(device)
prompt_inputs = tokenizer(prompt, return_tensors='pt').to(device)

with torch.no_grad():
    generation = model.generate(
        input_ids=desc_inputs.input_ids,
        attention_mask=desc_inputs.attention_mask,
        prompt_input_ids=prompt_inputs.input_ids,
        prompt_attention_mask=prompt_inputs.attention_mask,
        temperature=0.8,
        do_sample=True,
    )

audio_arr = generation.cpu().float().numpy().squeeze()
sf.write('output.wav', audio_arr, model.config.sampling_rate)
print('Audio generated: output.wav')

Voice Directing and Style Prompts

Control speaker style, gender, pacing, and mood using the description string:

• Clean Studio Female: 'Divya speaks with a clear, warm, pleasant and articulate voice at a steady pace in a quiet studio with very clean audio.'
• Punchy Studio Male: 'Rohit speaks with an energetic, modern, confident and clear male voice in a professional recording booth.'
• Formal News Narration: 'A female speaker delivers an authoritative, clear and precise news narration with deliberate pacing and zero background noise.'

Attribution and Credits

Thiva-indic is built upon and optimized from ai4bharat/indic-parler-tts developed by AI4Bharat (IIT Madras).

• Original Project: AI4Bharat Indic-Parler-TTS
• Institution: Indian Institute of Technology Madras (IIT Madras)
• License: Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)

License

This model is licensed under the Apache License, Version 2.0. Free to use, modify, distribute, and integrate into commercial applications.