{"slug": "saaras-v4", "title": "Saaras-V4", "summary": "Sarvam AI released Saaras V4, an automatic speech recognition model pairing an audio encoder with a 3B hybrid state-space LLM decoder trained from scratch in-house, which the company says achieves state-of-the-art performance on all 22 Indian languages and the lowest average word error rate across seven English benchmarks. Saaras V4 supports five transcript formats — transcribe, translate, verbatim, translit, and codemix — and reports a 2.9% language-identification error rate across the top ten Indian languages and 5.22% across all 22. Evaluation used HuggingFace's Open ASR Leaderboard datasets and code plus AI4Bharat's Svarah and Vistaar benchmarks, with results reported in both WER and LLM-WER.", "body_md": "# Introducing Saaras V4\n\nThe Next Leap in ASR for a Multilingual World\n\nWe are releasing Saaras V4, the latest generation of our speech recognition model.\n\nThis post covers the work behind Saaras V4, including the data used to train it, our pre-training approach, the architectural and decoding changes that support multiple output modes, and the evaluation methodology behind our results. We also report benchmark performance across Indian languages, English, and challenging real-world speech conditions.\n\n## Highlights\n\n- Saaras V4 uses an audio encoder, and an LLM decoder. The LLM decoder is a 3B hybrid state space model completely trained from scratch in-house\n- Saaras V4 achieved SOTA performance on all 22 Indian languages and reaffirms our commitment to even the most low-resource Indian languages\n- Saaras V4 achieves lowest WER on seven global English datasets, six being foreign English dialects and one being Indian English\n- You can choose the format that works best for you: Saaras V4 supports five transcript formats: transcribe, translate, verbatim, translit, and codemix\n- Saaras V4 is robust to noisy audios and code mixing. It also handles dialectical variation seamlessly\n- Its language identification is also best-in-class: a 2.9% error rate across the top ten Indian languages, and 5.22% across all 22\n\n## Inside Saaras V4\n\nSaaras V4 pairs an audio encoder with a 3B hybrid state-space language model that we trained from scratch in-house.\n\nWe designed the system to handle the range of speech we see in practice, including code-mixing, dialectal variation, and noisy audio, while supporting five different ways of representing the same speech: verbatim transcription, normalized text, code-mixed text, transliteration, and translation.\n\n### **Performance across English benchmarks**\n\nWe evaluated Saaras V4 across seven English speech recognition benchmarks covering Indian English, international accents, meetings, media, finance, and other real-world speech settings. Across these datasets, Saaras V4 achieves the lowest average word error rate.\n\nMeeting rooms, far-field microphones\n\nPodcast, audiobook, web video\n\nRead speech, studio conditions\n\nRead speech, harder acoustics\n\nEarning calls, financial vocabulary\n\nEuropean Parliament speeches\n\nIndian-accented English\n\n**Evaluation methodology**\n\n- Six international: taken as published at HuggingFace's [Open ASR Leaderboard](https://huggingface.co/datasets/hf-audio/open-asr-leaderboard)\n- One Indian accented English: [Svarah](https://huggingface.co/datasets/ai4bharat/Svarah) published by AI4Bharat\n- Normalization and scoring follow the Open ASR Leaderboard's [code](https://github.com/huggingface/open_asr_leaderboard)\n\n## Indic Benchmarks for Saaras V4:\n\n### Vistaar\n\nWe evaluate Saaras V4 on [Vistaar](https://github.com/AI4Bharat/vistaar) across ten Indian languages and report both standard Word Error Rate (WER) and LLM-WER.\n\nWER remains the standard deterministic measure for speech recognition, counting substitutions, deletions, and insertions between the reference and predicted transcript. In Indic-language settings, however, some of these differences can be orthographic or formatting variations that do not materially change the spoken content.\n\nTo account for this, we also report [LLM-WER](https://www.sarvam.ai/blogs/evaluating-indian-language-asr), which adds a semantic adjudication step to determine whether a mismatch changes the meaning of the transcript or simply represents the same content differently.\n\nThe two metrics are therefore complementary. WER provides a reproducible baseline, while LLM-WER gives a more faithful view of errors that affect the underlying speech content.\n\n### Detailed breakdown of various benchmarks:\n\n### Benchmark WER by dataset\n\nLower is better\nContains crowd-sourced read speech from the Mozilla Foundation's Commonvoice.\n\n### Five Modes, One Model\n\nDifferent applications need different representations of the same speech. A compliance archive may need a verbatim transcript, while a CRM workflow may require normalized text and an analytics system may need an English translation. These transformations are often handled through separate post-processing steps, which add complexity and can introduce additional errors.\n\nSaaras V4 handles these requirements within the model itself. From the same audio input, it can produce five transcript modes: verbatim, normalized, code-mixed, transliterated, and translated.\n\npunctuation restored\n\nin English\n\nEnglish script\n\ntranslation\n\nas spoken\n\n- **Verbatim:** The exact transcription in the native script of the spoken language\n- **Transcribe:** The exact transcription in the native script of the spoken language with numbers and dates normalised\n- **Codemix:** Transcribe mode plus keeps native english words in english to improve legibility\n- **Translit:** Does transcription but in english script. Colloquially referred to as “WhatsApp language”\n- **Translate:** The English translation of the audio with numbers normalized\n\nBecause all five come from one model, there are no extra pre-processing steps that could lead to cascading of errors.\n\n### Beyond the transcription\n\n#### a. Best in class Language Identification\n\nSaaras V4 can identify the spoken language directly from the audio and transcribe it in the corresponding native script, without requiring the language to be specified in advance. On verified IndicVoices utterances, language identification error is 5.22% across all 22 Indian languages, falling to 2.9% across the ten most widely spoken languages.\n\n#### b. Keyterm prompting\n\nKeyterm prompting lets you bias transcription toward words or phrases that are easy to miss, such as product names, people’s names, acronyms, or domain-specific terminology.\n\nValidated as best on [ai4bharat/IndicContextEval](https://huggingface.co/datasets/ai4bharat/IndicContextEval) (Interspeech 2026): Saaras V4 reaches the lowest WER of **16.03%** in the L5 (keyword prompting) setting, where the domain-entity list is provided in native script along with the language. For dataset details and competitor scores, see the [paper](https://arxiv.org/pdf/2606.19157).\n\nઢૂન માટે આંખનું કામ કરે છે અને દરેક વખતે\n\nડ્રોન માટે આંખનું કામ કરે છે અને દરેક વખતે\n\nReference keywords provided (24)\n\n#### c. Built For Real-World Audio\n\nField audio is compressed, clipped, and full of interference. These are conditions where systems trained on clean benchmarks often fall apart. Saaras V4 is designed to remain reliable under these conditions, including noisy audio, code-mixing, and dialectal variation.\n\n| Language | Condition | Format | Transcript | \n|---|---|---|---|\n| Hindi | Constant wind noise | Verbatim | ए एक बात सुनो ना मैडम दो दो दो दो दो दोनों मिलाकर | \n| Hindi | Distortion and clipping | Transcribe | सर आपको अपनी पॉलिसी के बारे में कुछ पूछना है? मैं आपसे ये पूछ रही थी कि। | \n| Hindi | Constant background | Codemix | होते हो। Nice to meet you brothers. Okay. Thank you. Bye. कुछ कुछ price तो ऐसा है कि जो | \n| Bengali | Heavy traffic | Translit | Maane ami ekta TV kinechilam ami ekta TV installmente niyechilam apnader bank hoyto erokom taka kate okhan theke ki koreche | \n| Kannada | Static noise | Translate | First, in our art, in our culture, first we built a temple. We used to live in huts. We built big, big temples. If you see it, it's a surprise even now. How they must have built it, for a thousand years, for two thousand years, this kind of thing, it's a surprise. | \n| English | Channel distortion | Transcribe | Oh, what a record to read! What a picture to gaze upon! How awful the fact! | \n\n#### d. Low-latency streaming\n\nFor real-time voice applications, latency is as important as transcription quality. Saaras V4 supports streaming with time to first token below 150 ms, allowing downstream systems to begin responding with minimal delay and enabling more natural turn-taking.\n\nIt also handles long-form audio natively, with multi-minute recordings processed within a second.\n\n## Integrating Saaras V4 into your platform\n\nIntegrate Saaras v4 into your application with a simple API key from the [Sarvam AI dashboard](https://indus.sarvam.ai/)\n\n**Choose Your Integration Path:**\n\n- [**REST API**](https://docs.sarvam.ai/api/api-guides-tutorials/speech-to-text/rest-api) : Synchronous transcription for audio under 30 seconds\n- [**Batch API**](https://docs.sarvam.ai/api/api-guides-tutorials/speech-to-text/batch-api) : Asynchronous processing for files up to 2 hours. Speaker Diarization can be enabled in batch mode\n- [**WebSocket (Real-time)**](https://docs.sarvam.ai/api/api-guides-tutorials/speech-to-text/realtime-streaming) : Streaming transcription with partial results for live applications\n\n**SDKs & Frameworks:**\n\nReady-to-use SDKs for Python 3.9+ and Node.js 18+.\n\nFramework integrations available for [Vercel AI SDK](https://docs.sarvam.ai/api/integration/vercel-ai-sdk), [LiveKit Agents](https://docs.livekit.io/agents/models/stt/sarvam/) and [Pipecat Agents](https://docs.pipecat.ai/api-reference/server/services/stt/sarvam)\n\n**Output Modes:**\n\nConfigure transcription output: `transcribe` (default), `translate` (to English), `translit` (romanized), or `verbatim` or `codemix`\n\n**Quick Start:**\n\nSee the [Developer Quickstart](https://docs.sarvam.ai/api/api-guides-tutorials/speech-to-text/rest-api) for code examples and authentication details\n\n## One model across languages\n\nWith Saaras V4, you no longer have to trade off English performance against Indic language coverage. It delivers strong performance across both, while supporting low-latency streaming and reliable production workloads. This makes it possible to build multilingual voice applications with one ASR model.\n\nCurious what else we're building? Explore our APIs and start creating.\n\nCurious what else we're building?\n\nExplore our APIs and start creating.", "url": "https://wpnews.pro/news/saaras-v4", "canonical_source": "https://www.sarvam.ai/blogs/introducing-saaras-v4", "published_at": "2026-10-07 10:37:35+00:00", "updated_at": "2026-10-07 10:49:25.665052+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "natural-language-processing", "ai-research", "ai-products"], "entities": ["Sarvam AI", "Saaras V4", "HuggingFace", "Open ASR Leaderboard", "AI4Bharat", "Svarah", "Vistaar", "Mozilla Common Voice"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/saaras-v4", "markdown": "https://wpnews.pro/news/saaras-v4.md", "text": "https://wpnews.pro/news/saaras-v4.txt", "jsonld": "https://wpnews.pro/news/saaras-v4.jsonld"}}