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LatestMaya Research1.0
Maya ResearchText → Speech

Veena TTS Model Targets Indian Languages with Llama Base

Maya Research has released a 3-billion-parameter model designed to generate natural-sounding speech in Hindi and English.

Jun 24, 2025
NotableOther
Veena

Maya Research has introduced Veena, a new open-source model for text-to-speech (TTS) synthesis. The model is specifically designed to address the need for high-quality voice generation in major Indian languages.

What sets Veena apart is its foundation on a Llama-style architecture. The 3-billion-parameter model is trained to generate natural-sounding speech from text in both Hindi and English, catering to the nuances of Indian accents and dialects. This architectural choice leverages the powerful text-processing capabilities of large language models for the distinct task of audio generation.

The release marks a significant step for open-source AI in a region where high-quality, accessible models have been less common. By focusing on widely spoken Indian languages, Veena could enable a new range of applications, from localized voice assistants and accessibility tools to automated content creation for one of the world's largest digital audiences.

The model, its weights, and usage instructions are available for download on the Hugging Face Hub. It is released under a custom license, and potential users should review the terms before implementation.

Sources

  • maya-research/Veena

    Hugging Face

    Visit
OlderJanus-4o-7B Adds Image Generation to 7B Multimodal AIFreedomIntelligence · Any-to-Any · last yearNewerNVIDIA Fuses LLM and ASR in Canary-Qwen 2.5B ModelNVIDIA · Speech → Text · last year

Get the model

Hugging Face

Specs

Parameters3B
Sample rate24 kHz
LanguagesHindi, English
Size7.6 GB
PrecisionBF16
ArchitectureLlamaForCausalLM
LicenseOTHER
Downloads4K
Likes243

Can you run it?

Runs on a laptop — about 4.8 GB at FP8.

  • BF16 (as published)

    12 GB GPU (RTX 3060 / 4070) · 16 GB Mac

    8.6 GB
  • FP8

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    4.8 GB
Your machine

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text → Speech

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