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Sarvam AI releases Saaras V4: A speech-to-text model for all 22 Indian languages and global English. September 26, 2026 Sarvam AI has released 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 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: AMI, GigaSpeech, LibriSpeech clean, LibriSpeech other, SPGISpeech and VoxPopuli. The seventh is AI4Bharat's Indian-accented Svarah. Scoring follows the leaderboard's normalization code. Saaras V4 posts the lowest average WER among the models Sarvam benchmarked. * Indic: On Vistaar, Sarvam reports results across 10 Indian languages using both WER and LLM-WER. 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 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 (paper, 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 with partial results and time to first token below 150 ms. * REST: synchronous transcription for clips up to 30 seconds. * Batch: asynchronous jobs up to 2 hours per file, with optional speaker diarization. * SDKs: Python 3.9+ and Node.js 18+, plus LiveKit Agents, Pipecat and Vercel AI SDK integrations. * Price: Sarvam lists speech-to-text at ₹30 per hour for real-time, streaming and batch, and ₹45 per hour with diarization. Saaras v3 stays the default model. 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. 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. Need to partner with Marktechpost LLC. for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with Marktechpost LLC. 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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Sarvam launches Vision 2.1 AI model for documents and Indian languages. Sarvam has launched Vision 2.1, a new AI model that can read forms, tables and documents in English and 22 Indian languages. Sarvam has launched the latest version of its Vision AI model, with a focus on reading and understanding documents. The model can extract information from forms and tables and turn it into structured digital data. Vision 2.1 can also read handwritten text in Indian languages. Sarvam said the model scored 87.3 on the olmOCR-Bench and 87.39 on its own Indic benchmark. Better at forms, tables and handwriting. The new model is designed to handle more complicated documents, including tables spread across multiple pages and forms where information has to be picked out from specific fields. It can also recognise handwritten text in Indian languages. This could be useful for converting older or handwritten documents into digital formats. Sarvam said it worked on reducing incorrect responses and inconsistent results seen in the earlier version of the model. Model trained on real and synthetic data. Sarvam said Vision 2.1 was trained using a mix of real-world and synthetic data. The training material included handwritten and printed forms in multiple languages, along with artificially filled-in forms. The company then used supervised fine-tuning and reinforcement learning with verifiable rewards to improve the model's results. Sarvam first launched its Vision model in February 2026. The earlier version focused on reading text from images and documents, understanding tables and producing structured information. Lower cost of running the model. Sarvam has also made changes to the technology used to run Vision 2.1, which it said allows the model to be offered at a lower cost than originally planned. Its API has also been optimised for companies that want to use the model in their own software. Vision 2.1 uses a vision-language model along with additional systems that help it understand the layout of a document and the order in which its contents need to be read. Conclusion. Sarvam Vision 2.1 is aimed at businesses and other users that need to process large amounts of documents, forms and tables. The model adds support for handwritten text in Indian languages and is designed to handle more complex documents than the earlier version. Disclaimer: This blog has been written exclusively for educational purposes. The securities mentioned are only examples and not recommendations. This does not constitute a personal recommendation/investment advice. It does not aim to influence any individual or entity to make investment decisions. Recipients should conduct their own research and assessments to form an independent opinion about investment decisions. Investments in the securities market are subject to market risks, read all the related documents carefully before investing. Take action on today's market news - Complete your Demat Account Opening now. Published on: Sep 25, 2026, 3:10 PM IST Team Angel One Team Angel One is a group of experienced financial writers that deliver insightful articles on the stock market, IPO, economy, personal finance, commodities and related categories. Open free Demat Account! Join its 3.8 Cr+ happy customers 10 Cr+ DOWNLOADS Enjoy ₹0 Account Opening charges. Get the link to download the App

Mahindra Finance and Sarvam expand Voice AI collaboration. September 10, 2026 15:00 IST Mahindra & Mahindra Financial Services Limited ("Mahindra Finance"), one of India's leading financial services providers, has announced an expanded voice AI partnership with Sarvam, the Bengaluru-based sovereign AI company, at Global Fintech Fest (GFF) 2026, where Sarvam is this year's AI Ecosystem Partner. Building on an earlier collaboration, Mahindra Finance has scaled the engagement into one of the largest voice AI deployments in Indian financial services. The Mahindra Group's AI Division (Mahindra AI) has developed voice agents on Sarvam's platform that operate across 12 Indian languages and have completed over one crore calls to date, reaching Mahindra Finance customers in their preferred language, at a time of their choosing. The partnership now spans three areas of Mahindra Finance's business: Sales: Relationship-management voice agents check in with customers and surface cross-sell opportunities across vehicle, tractor, SME, housing, insurance and investment products. Collections: Voice agents support the full lifecycle, from pre-due reminders through to write-off and repossession-stage conversations. Employee engagement: Voice agents gather field-level feedback, giving leadership a more direct line of sight into frontline realities. Strong collaboration between Mahindra AI and Sarvam have led to tangible results at scale for Mahindra Finance. Mahindra AI orchestrated campaigns layered on top of inhouse machine learning models that utilized Sarvam's voice agents to advance Mahindra Finance's sales and collections campaigns translating model-driven predictions into real conversations, at scale, in the customer's own language. Every call is recorded and analysed by custom AI pipelines, feeding a continuous-improvement loop and integrating with downstream systems and activities, including CRM, loan servicing and collections, to turn conversations into action. "Our customers have always trusted us to stay close to them, whether they are in a city or a village. Voice AI has allowed us to extend that closeness at scale reaching people proactively, in their own language." said Raul Rebello, Managing Director & CEO, Mahindra Finance. "As we diversify into new lines of business, this partnership with Sarvam is central to how we scale customer-first financial services across Bharat." "Mahindra Finance has set a strong benchmark for what AI at scale can look like in financial services," said Pratyush Kumar, Co-founder, Sarvam. "More than one crore calls across sales, collections and employee engagement show what becomes possible when AI is deeply embedded into the business and continuously learns from customer outcomes."