Voices that Sound like India
Real-time speech synthesis for multilingual
Indian conversations.
<250ms
10
4.23
Explore our Audio Library
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.
Text to Speech
that doesn't make you wait.
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) 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} "event: audio_chunk\ndata: UklGRiQAAABXQVZFZm10IBAAAAABAAEAQB8AAEAfAAABAAgAZGF0YQAAAAA=\n\nevent: completed\ndata: {\"status\": \"success\"}\n" 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.
