# Sarvam AI Releases Saaras V4: A Speech-to-Text Model for All 22 Indian Languages and Global English > Source: > Published: 2026-09-26 21:56:23+00:00 [Sarvam AI](https://www.sarvam.ai/) has released [Saaras V4](https://www.sarvam.ai/blogs/introducing-saaras-v4), the newest generation of its speech recognition model. It covers all 22 scheduled Indian languages plus English, now including global English accents. Sarvam reports state-of-the-art accuracy across all 22 languages. **Is it deployable?** Yes, through Sarvam’s API today, using `model="saaras:v4"`. Weights are not public, and Sarvam’s [SageMaker self-hosting docs](https://docs.sarvam.ai/api/self-hosted/sagemaker/deploy-saaras) currently cover Saaras v3 only. ## **What is Inside Saaras V4** Saaras V4 is an encoder-decoder system. An audio encoder converts the waveform into embeddings that carry phonetic and acoustic detail. A temporal-downsampling adapter then shortens that sequence and projects it into the language model’s embedding space. This keeps long recordings inside the decoder’s context budget. The decoder is Sarvam-3B, a 3B-parameter hybrid state-space language model trained from scratch in-house. It reads the audio features alongside a text prompt. It then emits the transcript autoregressively, feeding each token back as input for the next. ## **Benchmark Results** - **English** : Sarvam evaluated 7 English datasets. Six come from Hugging Face’s[Open ASR Leaderboard](https://huggingface.co/datasets/hf-audio/open-asr-leaderboard) : AMI, GigaSpeech, LibriSpeech clean, LibriSpeech other, SPGISpeech and VoxPopuli. The seventh is AI4Bharat’s Indian-accented[Svarah](https://huggingface.co/datasets/ai4bharat/Svarah) . Scoring follows the leaderboard’s[normalization code](https://github.com/huggingface/open_asr_leaderboard) . Saaras V4 posts the lowest average WER among the models Sarvam benchmarked. - **Indic** : On[Vistaar](https://github.com/AI4Bharat/vistaar) , Sarvam reports results across 10 Indian languages using both WER and[LLM-WER](https://www.sarvam.ai/blogs/evaluating-indian-language-asr) . LLM-WER adds a semantic check. It separates real meaning errors from harmless spelling or formatting variants common in Indic scripts. - **Noisy audio** : On Kathbath Noisy, measured with LLM-WER, Sarvam says Saaras V4’s error rate is under half that of Deepgram Nova-3 and GPT-4o Transcribe. The set includes compressed, clipped and background-heavy recordings. - **Language ID** : On verified IndicVoices utterances, language identification error is 2.9% across the top 10 Indian languages. It is 5.22% across all 22. *It is important to note that **all numbers above are vendor-reported. Independent reproduction has not been published yet.*** ## **5 Output Modes From 1 Model** **The same audio can return 5 representations, selected through the `mode` parameter:** - **transcribe** (default): native script with numbers and dates normalized. - **verbatim** : every word as spoken, fillers and spoken numbers kept. - **codemix** : native script, with English words left in English. - **translit** : the full utterance in Latin script. - **translate** : an English translation with numbers normalized. Sarvam’s argument is simple. Handling these inside the model removes post-processing steps that can compound errors. ## **Keyterm Prompting** [Keyterm prompting](https://docs.sarvam.ai/api/api-guides-tutorials/speech-to-text/how-to/keyterms) is new in V4 and works only with `saaras:v4`. You pass a JSON list under `keyterms`, with up to 50 terms of 64 characters each. Keyterms bias recognition; they do not guarantee output. Use `codemix` mode when a brand such as PhonePe must stay in Latin script. On [IndicContextEval](https://huggingface.co/datasets/ai4bharat/IndicContextEval) ([paper](https://arxiv.org/pdf/2606.19157), Interspeech 2026), Saaras V4 reports 16.03% WER in the L5 keyword-prompting setting. Sarvam says that is the lowest score on the benchmark. ## **Streaming, Long Audio and Pricing** - **Streaming:**[WebSocket](https://docs.sarvam.ai/api/api-guides-tutorials/speech-to-text/realtime-streaming) with partial results and time to first token below 150 ms. - **REST:**[synchronous](https://docs.sarvam.ai/api/api-guides-tutorials/speech-to-text/rest-api) transcription for clips up to 30 seconds. - **Batch:**[asynchronous](https://docs.sarvam.ai/api/api-guides-tutorials/speech-to-text/batch-api) jobs up to 2 hours per file, with optional speaker diarization. - **SDKs:** Python 3.9+ and Node.js 18+, plus[LiveKit Agents](https://docs.livekit.io/agents/models/stt/sarvam/) ,[Pipecat](https://docs.pipecat.ai/api-reference/server/services/stt/sarvam) and[Vercel AI SDK](https://docs.sarvam.ai/api/integration/vercel-ai-sdk) integrations. - **Price:** Sarvam lists speech-to-text at[₹30 per hour](https://www.sarvam.ai/api-pricing) for real-time, streaming and batch, and ₹45 per hour with diarization. Saaras v3 stays the [default model](https://docs.sarvam.ai/api/getting-started/models/saaras). V4 uses the same request shape, so switching is a 1-line change. ## **Saaras V4 vs Closest Competitors** These are the 3 systems Sarvam benchmarked against. Figures come from each vendor’s public docs and pricing pages, checked on September 26, 2026. | Feature | Sarvam Saaras V4 | Deepgram Nova-3 | ElevenLabs Scribe v2 | OpenAI GPT-4o Transcribe | |---|---|---|---|---| | Indian scheduled languages (of 22) | [22](https://docs.sarvam.ai/api/getting-started/models/saaras) | [11](https://developers.deepgram.com/docs/models-languages-overview) | [14](https://elevenlabs.io/docs/overview/capabilities/speech-to-text) | [Not listed per language](https://developers.openai.com/api/docs/guides/speech-to-text) | | Total languages | 23 (22 Indian + English) | 45+ | 90+ | Multilingual | | Keyterm biasing | Up to 50 terms | [Yes, paid add-on](https://deepgram.com/pricing) | Up to 1,000 (batch), 50 (realtime), paid add-on | Free-text `prompt` | | Built-in output modes | 5 (transcribe, verbatim, codemix, translit, translate) | Transcript plus Smart Formatting | Verbatim or no_verbatim | Transcript | | Real-time streaming | WebSocket, under 150 ms TTFT (vendor claim) | Yes (WebSocket) | Scribe v2 Realtime, about 150 ms | File streaming; live via Realtime API | | Speaker diarization | Batch API | Yes | Up to 32 speakers | Separate `gpt-4o-transcribe-diarize` model | | List price | [₹30/hour](https://www.sarvam.ai/api-pricing) | [$0.0052/min](https://deepgram.com/pricing) (multilingual, pre-recorded) | [$0.22/hour](https://elevenlabs.io/pricing/api) (batch) | [~$0.006/min](https://developers.openai.com/api/docs/pricing) | | Self-hosting | Not for V4 yet (v3 on SageMaker) | [Yes](https://developers.deepgram.com/docs/self-hosted-introduction) | Cloud API | Cloud API | ## **Key Takeaways** - Saaras V4 covers all 22 Indian languages plus global English in 1 model. - A 3B hybrid state-space decoder, trained from scratch, sits behind an audio encoder. - Keyterm prompting accepts up to 50 terms and scored 16.03% WER on IndicContextEval L5. - 5 output modes and sub-150 ms streaming TTFT come from the same model. - API-only today at ₹30 per hour; self-hosting docs still cover v3. Check out the [**Technical Details**](https://www.sarvam.ai/blogs/introducing-saaras-v4). All credit goes to the researcher of this project. Also, feel free to follow us on **[Twitter](https://x.com/intent/follow?screen_name=marktechpost)** and don’t forget to join our **[150k+ML SubReddit](https://www.reddit.com/r/machinelearningnews/)** and Subscribe to **[our Newsletter](https://magic.beehiiv.com/v1/f5e63dd4-5653-4f09-83e2-321a8b1ba526?email={{email}})**. Wait! are you on telegram? [now you can join us on telegram as well.](https://t.me/machinelearningresearchnews) Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? [Connect with us](https://forms.gle/MJjjVDPS7whH8Ngs6) Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. 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