Best Speech-to-Text APIs for Indian Languages in 2026 (Compared)
We tested 6 major STT providers on Hindi, Tamil, Telugu, Bengali, and Hinglish audio. Here's which one actually works for Indian businesses.
Varun Sharma
Founder
The STT Problem for Indian Businesses
You've decided to add voice to your product. Great. Now comes the hard part: which speech-to-text API actually works for Indian users?
Most STT benchmarks test on clean American English audio. But your customers speak Hindi with English words mixed in. They call from noisy shops. They have regional accents that vary by state, by city, even by neighborhood.
We tested 6 major STT providers on real-world Indian audio to find out which ones deliver—and which ones don't.
Our Testing Methodology
We collected 100 audio samples across 5 categories:
| Category | Description | Samples |
|---|---|---|
| Clean Hindi | Studio-quality Hindi speech | 20 |
| Hinglish | Hindi-English code-switching | 20 |
| Regional accents | Tamil-accented English, Punjabi Hindi, etc. | 20 |
| Noisy environments | Street noise, call center, WhatsApp audio | 20 |
| South Indian languages | Tamil, Telugu, Kannada, Malayalam | 20 |
Each sample was run through all 6 providers, and we measured:
The Providers We Tested
Results: Clean Hindi
| Provider | WER | Latency | Notes |
|---|---|---|---|
| **Sarvam Saaras v2** | **5.2%** | 1.1s | Best overall for Hindi |
| Google Cloud v2 | 7.8% | 1.4s | Solid but slower |
| OpenAI Whisper | 8.1% | 2.3s | Good accuracy, high latency |
| Azure Speech | 9.4% | 1.2s | Decent |
| Deepgram Nova-2 | 12.3% | 0.6s | Fast but less accurate for Hindi |
| AssemblyAI | 14.7% | 1.8s | Struggles with Hindi |
Winner: Sarvam AI — Their model is trained specifically on Indian speech patterns, and it shows.
Results: Hinglish (Code-Switching)
This is where it gets interesting. "Hinglish" is how most urban Indians actually speak: "Yaar, can you check my order status? Maine yesterday order kiya tha."
| Provider | WER | Code-Switch Accuracy | Notes |
|---|---|---|---|
| **Sarvam Saaras v2** | **8.4%** | **94%** | Handles switching seamlessly |
| OpenAI Whisper | 11.2% | 87% | Sometimes picks wrong language |
| Google Cloud v2 | 13.5% | 82% | Language hints help but not enough |
| Azure Speech | 15.1% | 79% | Misses many English words in Hindi context |
| Deepgram Nova-2 | 18.7% | 71% | Designed for English-first |
| AssemblyAI | 22.3% | 65% | Essentially English-only |
Winner: Sarvam AI — Their training data includes massive amounts of code-switched audio, which is critical for real Indian conversations.
Results: Noisy Environments
Real customer support audio isn't recorded in studios. It comes from bustling markets, auto-rickshaws, and crowded offices.
| Provider | WER (Clean) | WER (Noisy) | Degradation |
|---|---|---|---|
| **Sarvam Saaras v2** | 5.2% | **11.3%** | +6.1% |
| Google Cloud v2 | 7.8% | 14.2% | +6.4% |
| OpenAI Whisper | 8.1% | 13.8% | +5.7% |
| Azure Speech | 9.4% | 16.5% | +7.1% |
| Deepgram Nova-2 | 12.3% | 19.1% | +6.8% |
| AssemblyAI | 14.7% | 24.2% | +9.5% |
Winner: OpenAI Whisper — Slightly better noise robustness, but Sarvam's absolute WER is still lower.
Results: South Indian Languages
For Tamil, Telugu, Kannada, and Malayalam:
| Provider | Tamil WER | Telugu WER | Kannada WER | Malayalam WER |
|---|---|---|---|---|
| **Sarvam Saaras v2** | **7.1%** | **8.3%** | **9.5%** | **10.2%** |
| Google Cloud v2 | 10.4% | 11.7% | 14.2% | 15.8% |
| OpenAI Whisper | 11.8% | 13.2% | 15.7% | 16.4% |
| Azure Speech | 13.5% | 15.1% | 18.3% | 20.1% |
| Deepgram Nova-2 | 22.4% | 25.7% | 28.1% | 30.5% |
| AssemblyAI | 28.1% | 31.4% | 35.2% | 37.8% |
Winner: Sarvam AI — Dominant across all South Indian languages.
Latency Comparison
For customer support, speed matters. Here's end-to-end latency for a 10-second audio clip:
| Provider | p50 Latency | p95 Latency | Streaming Support |
|---|---|---|---|
| Deepgram Nova-2 | **0.6s** | 1.1s | Yes |
| Sarvam Saaras v2 | 1.1s | 1.8s | No (batch only) |
| Azure Speech | 1.2s | 2.0s | Yes |
| Google Cloud v2 | 1.4s | 2.3s | Yes |
| AssemblyAI | 1.8s | 3.1s | Yes |
| OpenAI Whisper | 2.3s | 4.2s | No |
Winner: Deepgram — But the accuracy trade-off for Indian languages makes it impractical for most use cases.
Pricing Comparison
| Provider | Price per Minute | Free Tier | Min Commitment |
|---|---|---|---|
| Deepgram Nova-2 | $0.0043 | 12,000 min | None |
| AssemblyAI | $0.0050 | 100 hours | None |
| Sarvam Saaras v2 | $0.0060 | 1,000 min | None |
| OpenAI Whisper | $0.0060 | None | None |
| Google Cloud v2 | $0.0060-0.0090 | 60 min/month | None |
| Azure Speech | $0.0080-0.0160 | 5 hours/month | None |
Our Recommendation
For Indian customer support: **Sarvam AI Saaras v2**
It wins on the metrics that matter most:
For global/English-primary: **OpenAI Whisper**
If your users primarily speak English with occasional Hindi, Whisper offers solid multilingual support with the broadest language coverage.
For real-time streaming: **Google Cloud v2**
If you need word-by-word streaming transcription (like live captions), Google's streaming API is the most reliable.
How Agent Rush Uses Voice AI
At Agent Rush, we integrated Sarvam's Saaras v2 as our default STT engine. Here's why:
The result? Voice-enabled agents on Agent Rush achieve 94% transcription accuracy across Hindi, English, and code-switched conversations—right out of the box.
Getting Started
If you're building voice features for Indian users, here's our advice:
Varun Sharma
Founder
Building the future of customer support at Agent Rush. Passionate about AI, product design, and creating delightful user experiences.
