Veena : India’s 1st TTS model for Hindi and Hinglish
AI model for Hindi audio generation
So finally India has arrived in the audio AI arena and released their first ever Text to Speech Model with SOTA results in Hindi language generation.
https://medium.com/media/5329f2aed98c694736d617a387642d07/href
Veena, developed by Maya Research, is India’s first serious attempt at a proper Text-to-Speech (TTS) model tailored for our context. And it’s not just some proof-of-concept lab experiment — it’s production-ready, fast, expressive, and bilingual. Hindi and English out of the box, including that messy, beautiful code-mixed Hinglish we all speak without thinking.
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What exactly is Veena?
At the core, Veena is a 3 billion parameter autoregressive transformer model. Think of it as a really smart pattern matcher that’s been trained to take text — say, “क्या हाल है?” or “Turn left after 300 meters” — and turn that into speech that sounds like a real human, not a robot from a 2005 bank IVR.
It’s built on the Llama architecture (yes, the same family of models used in language understanding tasks), but Veena flips the use case: it’s all about speaking, not just understanding.
Languages: Hindi, English
Voices: 4 distinct ones — Kavya, Agastya, Maitri, and Vinaya
Output quality: 24kHz audio using something called the SNAC codec (translation: clean, rich audio without chewing up too much bandwidth)
Latency: Sub-80ms on H100 GPUs. That’s fast enough for real-time apps.
Where it works (and shines)
If you’ve ever cursed at a clunky IVR system that couldn’t pronounce your name or understand your choice of language, Veena is here to fix that. Some places it already feels useful:
- Accessibility tools: Screen readers that don’t sound like a tired robot
- Customer support bots: The kind that don’t instantly make you want to press 9 for a human.
- Audiobooks, e-learning, dubbing: Imagine watching a Hindi-dubbed YouTube tutorial that doesn’t sound like it was voiced by a fridge.
- Cars: Think voice guidance that sounds like a local, not some GPS voice imported from California.
- Smart devices: Your speaker, fridge, AC — anything that talks back — can now do so in natural Indian voice.
Technical side
Keeping it simple
- It uses speaker tokens — like <spk_kavya>—so you can explicitly choose who’s talking.
- Training was done on 60,000+ studio-grade audio samples with 4 pro voice artists.
- It understands both narrative and conversational tone. So whether it’s reading bedtime stories or shouting traffic updates, it adapts.
And yes, all this is optimized using LoRA (Low-Rank Adaptation), which just means it’s been fine-tuned efficiently so the model doesn’t become a GPU-hogging beast. Smartly trained, cleverly compressed, and it still performs.
Benchmarks?
It is actually looking great, specially for hindi
- Mean Opinion Score (MOS): 4.2 out of 5 — which is how humans rate audio quality, and anything above 4 is considered “damn good”.
- Speaker similarity: 92% — i.e., the voice actually sounds like the original speaker.
- Intelligibility: 98% — no mumbling or garbling.
- Real-time factor: 0.05x — fast enough for anything short of time travel.
What’s missing (for now)
Let’s not sugarcoat it — Veena isn’t perfect. Yet.
- Only Hindi and English for now. So Tamil, Bengali, Marathi folks, you’re still on hold. That’s changing soon though.
- Only 4 voices. India has hundreds of accents and dialects. This is a start, not the finish line.
- Needs GPU muscle for real-time work. If you’re planning to run it on your uncle’s 10-year-old office PC, it’s going to crawl.
Also: since the training data is proprietary (read: not open-sourced), we don’t fully know what biases might’ve slipped in. Maybe it handles urban Hindi better than rural dialects. Maybe it mispronounces certain names or phrases. These are the subtle things that creep in with voice AI — and they matter.
What’s coming
The team behind Veena is already working on expanding its reach:
- New languages: Tamil, Telugu, Bengali, Marathi, and others are on the roadmap.
- More voices: with regional accents too.
- Emotion tokens: So it can sound angry, happy, sad — not just flat.
- CPU support: so it can work on edge devices without dedicated GPUs.
- Streaming support: which means smoother, uninterrupted voice generation.
The model can be used from here
maya-research/Veena · Hugging Face
Final word
Veena isn’t just another TTS model. It’s a long-overdue, culturally aware system that understands how people in India speak, not just what they say. And it does so with fluency, emotion, and speed that’s finally usable in real applications — from voice assistants to audiobooks to self-driving car dashboards.
It’s one of the first serious building blocks toward a future where Indian tech doesn’t just translate global models — it builds its own.
Maya Research, take a bow. The rest of us? Time to start building on top of this.
Veena : India’s 1st TTS model for Hindi and Hinglish was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
