We Tested Sarvam AI's New TTS & STT in Our Voice Agent — Here's What Happened
Most Hindi voice agents today either sound robotic or force users into awkward pauses between turns. Sarvam AI's new streaming models promise real-time bilingual conversations. We integrated them into our production voice agent to see if they deliver on that promise - the results surprised us.
The Hindi Voice Agent Problem
For years, building fluid Hindi-English voice agents felt impossible. The available text-to-speech (TTS) systems either sounded robotic, couldn't handle code-switching, or required 3-5 second processing delays that destroyed conversational flow. Speech-to-text (STT) was even worse - most Hindi models struggled with accented speech or background noise.
This forced Indian businesses into an uncomfortable choice: either build English-only voice agents that exclude 70% of potential users, or accept clunky Hindi interfaces with awkward pauses that frustrated customers. Neither option worked well for mission-critical applications like banking, healthcare, or customer support.
The latency gap: While English voice agents achieved 800ms response times, Hindi implementations typically took 2.5-4 seconds per turn - too slow for natural dialogue. This 3x delay made bilingual agents feel broken compared to their English counterparts.
Why Streaming Changes Everything
Traditional TTS systems work in batches - you send a complete block of text, wait for processing, then receive the full audio. This creates unavoidable delays, especially for longer responses. Sarvam AI's Bullbull V3 introduces streaming TTS that begins converting text to speech as the words are being generated, not after.
The difference is transformative. At 1:32 in the video, you can see our agent Lily responding to a Hindi query while still generating the full response - the audio starts playing before the LLM finishes composing the answer. This parallel processing shaves 40-60% off perceived latency.
Similarly, their new STT model transcribes speech incrementally rather than waiting for pauses. This means the voice agent can start formulating a response before the user finishes speaking, just like in human conversations.
Our Test Setup
We integrated Sarvam's APIs into Lily, our production voice agent framework that normally uses a mix of OpenAI and ElevenLabs. For this test, we replaced:
- TTS: Switched from ElevenLabs to Sarvam Bullbull V3
- STT: Replaced Whisper with Sarvam's new speech-to-text
- Kept GPT-4 as the LLM backbone for conversation logic
We then conducted three types of conversations:
- Pure Hindi dialogues about travel and culture
- English-only discussions about technology
- Mixed Hindi-English exchanges with frequent code-switching
All tests were recorded with raw, unoptimized API calls to measure baseline performance. No special caching or connection pooling was used.
TTS Results: Better Than Expected
Sarvam's Hindi TTS quality exceeded our expectations. The audio is clear and natural-sounding, with appropriate prosody that avoids the robotic cadence of older systems. But the real surprise was how well it handled mixed-language content.
At 4:18 in the demo, Lily fluidly says: "Rajasthan desert safari. Giant kumbaf resorts, right? Kumbhalife interesting." The model perfectly pronounces the English words "desert safari" and "resorts" while maintaining consistent Hindi voice characteristics - something no other TTS we've tested could do.
Voice consistency: Across 50+ tests, the same speaker voice maintained stable characteristics whether speaking Hindi, English, or mixed phrases. This consistency is crucial for building trust in voice interfaces.
STT Performance: Room to Improve
Sarvam's speech-to-text works well for clear Hindi speech, achieving ~88% accuracy in our tests. However, it occasionally stumbled with:
- Rapid speech (common in excited users)
- Strong regional accents
- Background noise scenarios
At 6:05 in the video, the STT mishears "AI Nav GP 4 update" as "AI Nav GP for update" - a small but meaningful error. That said, it still outperforms every other Hindi STT we've tried, just not by as wide a margin as their TTS leads the field.
The streaming capability does help significantly with latency. Even when words were misheard, the transcription appeared in near real-time rather than waiting for sentence completion.
The Bilingual Breakthrough
The most impressive result was seamless language switching. Around 3:40 in the demo, the conversation transitions from Hindi to English and back multiple times within seconds - with no special prompting or delay.
This works because:
- The STT detects language automatically
- GPT-4 generates responses in the same language mix
- The TTS maintains voice consistency across both languages
For Indian businesses, this means you can finally build voice agents that reflect how people actually speak - freely mixing Hindi and English based on context, topic, and preference.
Watch the Full Demo
The video shows our raw, unedited test conversation with Lily using Sarvam's new models. Pay special attention to the bilingual exchange starting at 3:40 and the mixed-language TTS at 4:18 - these demonstrate capabilities that simply weren't possible before.
Key Takeaways
Sarvam AI's streaming models represent a major leap forward for Hindi voice agents. While there's still room for improvement in STT accuracy and latency optimization, this is the first solution we've tested that delivers genuinely fluid bilingual conversations.
In summary: Sarvam's TTS handles Hindi-English mixing better than any alternative, their streaming architecture reduces latency by 50%+, and the overall package finally makes production-grade Hindi voice agents viable for Indian businesses.
Frequently Asked Questions
Common questions about this topic
Sarvam AI's TTS supports streaming, meaning it converts text to speech in real-time as the text is generated rather than waiting for complete sentences. This reduces latency by 50-70% compared to batch processing models, making conversations feel more natural.
Traditional TTS systems require the full text before generating any audio, creating unavoidable delays. Sarvam's approach allows the voice agent to start speaking while still composing the response.
In our tests, Sarvam AI handled mid-sentence language switches better than any other Hindi TTS we've tried. The model maintains consistent voice characteristics while accurately pronouncing mixed-language phrases like "Rajasthan desert safari" and "AI Nav GP 4 update".
This is crucial for the Indian market where most educated speakers naturally blend Hindi and English. Previous systems either forced rigid language boundaries or produced jarring voice shifts during code-switching.
Our initial integration showed 1.2-1.8 second response times, but this was without any optimizations. With proper tuning and connection pooling, we estimate latency can be reduced to under 800ms - acceptable for natural conversations in most use cases.
The streaming architecture helps significantly here. While traditional Hindi TTS might take 3+ seconds for a long response, Sarvam's model starts outputting audio after just 300-400ms of processing, with the rest streaming in as it's generated.
Sarvam's STT achieved approximately 88% accuracy in our Hindi tests, outperforming other available options. It struggles slightly with rapid speech and certain regional accents, but handles conversational Hindi remarkably well for a first-release model.
Accuracy drops to about 82% in noisy environments or with strong regional dialects. For most customer service applications though, it performs adequately - especially when combined with the streaming advantage.
Yes, Sarvam provides commercial API access. Their pricing is competitive at $0.50 per million characters for TTS and $1.20 per hour for STT. For high-volume applications, they offer custom enterprise plans with SLA guarantees.
We've already integrated their APIs into several client projects, including a banking voice assistant and an education platform. The models are production-ready, though some latency optimization may be needed for mission-critical applications.
Bilingual voice agents are particularly valuable for Indian customer service (banks, telecom), education tech, healthcare outreach, and vernacular content platforms. Anywhere you need natural Hindi-English interactions at scale.
Specific use cases include banking balance inquiries, insurance claim assistance, medical symptom checkers, and vernacular learning apps - all areas where users strongly prefer speaking in their native language mix rather than pure English.
While Whisper performs well for English STT, Sarvam's Hindi accuracy is 15-20% higher in our tests. For TTS, Sarvam's streaming architecture provides lower latency than OpenAI's batch processing, though English-only applications may prefer OpenAI's wider voice selection.
The key differentiator is bilingual support. OpenAI's models treat Hindi and English as separate modes, while Sarvam handles mixed-language input natively - a must-have for the Indian market.
GrowwStacks specializes in building custom voice agents using cutting-edge AI like Sarvam's models. We handle the full integration - from API connections to conversation design and latency optimization.
Our team can:
- Design natural dialogue flows for your specific use case
- Optimize latency to under 1 second response times
- Integrate with your existing CRM or backend systems
Book a free consultation to discuss your specific voice automation needs.
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