text to speech

Indian voices. Generated naturally.

Q: What makes this text-to-speech model different from generic TTS systems?

Most text-to-speech systems are trained on limited datasets and struggle with the linguistic complexity of Indian languages. This model is built for multilingual Indian speech, handling regional accents, code-switching, pronunciation, and conversational delivery with greater naturalness and accuracy.

Q: Can it handle Hindi-English and multilingual conversations naturally?

Yes. The model is designed to handle code-switched speech, allowing it to seamlessly generate speech across multiple languages within the same conversation without unnatural transitions or pronunciation errors.

Q: Is it optimized for contact centres, IVRs, and voice agents?

Yes. The model is built for production voice applications including AI agents, collections, reminders, customer support, IVRs, and outbound engagement. It is optimized for telephony environments and real-world customer interactions.

Q: How fast is speech generation for real-time applications?

The model supports streaming speech generation with low latency, making it suitable for conversational AI systems where responsiveness is critical. Audio generation begins in milliseconds, enabling natural back-and-forth interactions.

Q: How can developers integrate text-to-speech into their applications?

Developers can access speech synthesis capabilities through APIs and SDKs for real-time and batch use cases. Common implementations include voice agents, conversational AI platforms, contact centres, customer engagement systems, and enterprise workflow automation.

Q: Can responses be streamed in real time?

Yes. The WebSocket API begins delivering PCM audio chunks before the full sentence is synthesized. Average first-byte latency is among the lowest of any provider, enabling genuine real-time conversational AI pipelines.

Real-time speech synthesis for multilingual Indian conversations.

See How it Works

<

250 ms

p95 latency

10

Indian Languages

4 .

23

MOS score

Generate Speech

Explore our Audio Library

हिन्दी

BFSI

Collections Reminder

Northern accent · BFSI

A horizontal black and white soundwave or audio waveform pattern on a white background.

தமிழ்

Banking

Banking IVR

Chennai accent · Retail

Hi+हि

Finance

Loan Services

Urban Hinglish · Finance

తెలుగు

Telecom

Telecom IVR

Andhra accent · Activation

मराठी

NBFC

Loan Reminder

Pune accent · NBFC

বাংলা

Health

Healthcare Reminder

Kolkata accent · Healthcare

ગુજરાતી

Fintech

Payment Alert

Ahmedabad accent · Fintech

हिन्दी

BFSI

Collections Reminder

Northern accent · BFSI

தமிழ்

Banking

Banking IVR

Chennai accent · Retail

Hi+हि

Finance

Loan Services

Urban Hinglish · Finance

తెలుగు

Telecom

Telecom IVR

Andhra accent · Activation

मराठी

NBFC

Loan Reminder

Pune accent · NBFC

বাংলা

Health

Healthcare Reminder

Kolkata accent · Healthcare

ગુજરાતી

Fintech

Payment Alert

Ahmedabad accent · Fintech

India is not for

beginners.

India is not for generic model providers.

6 things that break low-corpus, non-Indic voice models and how Gnani.ai handles each of them.

Names like Shanmugapriya and Satinder.

Indian names are phonologically dense and regionally specific. Generic models guess phonetically and get it wrong every time. One mispronounced name signals inauthenticity before the message even lands.

Gnani Timbre V2.5

BETA

"Shanmugapriya amma, ungal policy kal expire aagum."

Region-aware Tamil phonology - correct on first pass

Generic TTS

"Shan-moo-gah-pree-yah calling about your insurance policy."

Phonetic guess - 3 syllables wrong

Hinglish is a feature, not a bug.

500 million Indians switch between Hindi and English mid-sentence naturally. A model that treats code-switching as noise produces audible seams at every language boundary, the acoustic equivalent of a dropped call.

Gnani Timbre V2.5

BETA

"Hi Arjun, your account mein kuch chhoti si issue aayi hai, main abhi resolve kar deta hoon."

Seamless code-switch handling - single acoustic model

Generic TTS

"Mera account mein balance check karna hai"

‍

Language boundary artifacts - audible acoustic seam

Pacing that doesn't sound like a press release.

Flat monotone delivery. No pauses, no emphasis, uniform speed — is the most common IVR failure mode. Listeners hang up in under 8 seconds. Timbre models pause structure and emphasis at the sentence level.

Gnani Timbre V2.5

BETA

"Rahul ji, [natural pause] aapki EMI kal due hai. [pause] Aaj complete kar lein, penalty nahi lagegi."

‍

Seamless code-switch handling - single acoustic model

Generic TTS

"Your EMI payment is due. Please pay immediately. Failure to pay will result in consequences."

‍

Monotone delivery - 8s average hang-up on flat IVR

Currency amounts with Indian cadence.

"₹75,440" is not "seventy-five thousand four hundred and forty." It is pachattar hazaar chaar sau chaalis with lakh and crore stress patterns that no Western training corpus has ever contained.

Gnani Timbre V2.5

BETA

"Aapka outstanding amount hai pachattar hazaar, chaar sau chaalis rupaye aaj settle karne par interest waived hoga."

Indian numeral system - lakh, crore, correct stress every time

Generic TTS

"Your outstanding balance is seventy five thousand four hundred and forty rupees. Please pay now."

‍

Western numeral cadence - sounds robotic to Indian ears

Telephony audio at 8kHz. Not studio conditions.

Call centers run at 8kHz. Most TTS products are benchmarked on 44kHz studio audio and never tested at actual deployment fidelity. A score achieved in silence is not a production guarantee.

Gnani Timbre V2.5

BETA

"Timbre v2.5 MOS: 4.23 on 8kHz telephony audio. Same model. Real conditions. No asterisk."

‍

Telephony-native benchmarked where the product actually runs

Generic TTS

"A 4.4 MOS on 44kHz audio that degrades to 2.8 on telephony is not an enterprise product."

‍

Degrades significantly on real IVR channels

10 languages. One model. No degradation.

Most multilingual TTS products have a flagship language and nine increasingly thin fallbacks. Timbre v2.5 is natively trained across all 10. No fine-tuned shortcuts, no MOS floor drop, no latency penalty.

Gnani Timbre V2.5

BETA

"MOS floor across all 10 languages: 4.1minimum. p95 latency delta across languages: under 18ms."

‍

Native 10 language model: same quality floor, every language

Generic TTS

"One flagship language. Tamil MOS: 2.8. Telugu MOS: 3.1. The fine print is the product."

‍

Language-gap degradation: hidden in most benchmark reports

Challenge 1

01 / 06

Text to Speech that doesn't make you wait.

P95 latency - telephony audio (ms)

Timbre v2.5

BETA

242

Provider A

510

Provider B

650

Provider C

790

Streaming audio chunks — first byte

init

auth

header

chunk_05

fin

chunk_02

chunk_03

chunk_04

—

First byte

Chunks / sec

99.9%

Uptime SLA

WebSocket streaming. First byte in <250ms.

A single API call. Streaming PCM audio in chunks. Optimized for real-time conversational pipelines.

1import requests 
2url = "https://api.vachana.ai/api/v1/tts/sse" 
3payload = { 
4	"audio_config": { 
5    	"bitrate": "192k", 
6    	"container": "mp3", 
7    	"encoding": "linear_pcm", 
8    	"num_channels": 1, 
9    	"sample_rate": 44100, 
10    	"sample_width": 2 
11	}, 
12	"model": "vachana-voice-v3", 
13	"text": "नमस्ते, आप कैसे हैं?", 
14	"voice": "Karan" 
15} 
16headers = { 
17	"X-API-Key-ID": "<x-api-key-id>", 
18	"Content-Type": "application/json" 
19} 
20response = requests.post(url, json=payload, headers=headers) 
21print(response.text)

Copy Code

1curl --request POST \ 
2  --url https://api.vachana.ai/api/v1/tts/sse \ 
3  --header 'Content-Type: application/json' \ 
4  --header 'X-API-Key-ID: <x-api-key-id>' \ 
5  --data ' 
6{ 
7  "audio_config": { 
8	"bitrate": "192k", 
9	"container": "mp3", 
10	"encoding": "linear_pcm", 
11	"num_channels": 1, 
12	"sample_rate": 44100, 
13	"sample_width": 2 
14  }, 
15  "model": "vachana-voice-v3", 
16  "text": "नमस्ते, आप कैसे हैं?", 
17  "voice": "Karan" 
18}

Copy Code

Response

"event: audio_chunk\ndata: UklGRiQAAABXQVZFZm10IBAAAAABAAEAQB8AAEAfAAABAAgAZGF0YQAAAAA=\n\nevent: completed\ndata: {\"status\": \"success\"}\n"

Explore APIs

Read Documentation

FAQs

Everything you need to know about Gnani's Voice AI platform, models, and deployment.

What makes this text-to-speech model different from generic TTS systems?

Most text-to-speech systems are trained on limited datasets and struggle with the linguistic complexity of Indian languages. This model is built for multilingual Indian speech, handling regional accents, code-switching, pronunciation, and conversational delivery with greater naturalness and accuracy.

Can it handle Hindi-English and multilingual conversations naturally?

Yes. The model is designed to handle code-switched speech, allowing it to seamlessly generate speech across multiple languages within the same conversation without unnatural transitions or pronunciation errors.

Is it optimized for contact centres, IVRs, and voice agents?

Yes. The model is built for production voice applications including AI agents, collections, reminders, customer support, IVRs, and outbound engagement. It is optimized for telephony environments and real-world customer interactions.

How fast is speech generation for real-time applications?

The model supports streaming speech generation with low latency, making it suitable for conversational AI systems where responsiveness is critical. Audio generation begins in milliseconds, enabling natural back-and-forth interactions.

How can developers integrate text-to-speech into their applications?

Developers can access speech synthesis capabilities through APIs and SDKs for real-time and batch use cases. Common implementations include voice agents, conversational AI platforms, contact centres, customer engagement systems, and enterprise workflow automation.

Can responses be streamed in real time?

Yes. The WebSocket API begins delivering PCM audio chunks before the full sentence is synthesized. Average first-byte latency is among the least among other providers - enabling genuine real-time conversational AI pipelines.