Hindi speech-to-text
Transcribe and translate speech across 60+ languages with the speed and accuracy real-time experiences demand.
Leading voice teams build with Deepgram
Fast and accurate Hindi speech recognition for real-world audio
Get real-time Hindi speech-to-text in under 300 ms while maintaining high accuracy in noisy, accented, or overlapping conversations.
Hindi language overview
Speakers: 609 million total speakers
Regions: India (northern and central states including Uttar Pradesh, Bihar, Madhya Pradesh, Rajasthan, Haryana, Delhi), Nepal, diaspora communities in the United States, Mauritius, Fiji, and South Africa
Dialects: Standard Hindi (Khari Boli), Braj Bhasha, Awadhi, Bhojpuri, Magahi
Writing system: Devanagari script (abugida with 47 primary characters)
Language family: Indo-Aryan branch of the Indo-European language family
Hindi is widely used across India's massive multilingual market, particularly in call centers, healthcare, media, education, and legal services, making it a key language for customer support AI, call analytics, Bollywood media transcription, e-learning platforms, voice-enabled telemedicine, court documentation, and multilingual voice agents serving over 500 million Hindi speakers.
Hindi speech-to-text capabilities
Diarization
Automatically detect and label who is speaking in multi-speaker Hindi conversations.
Smart formatting
Apply automatic capitalization, paragraphing, and clean transcript structure for Hindi text.
Search
Instantly find words or phrases inside long Hindi recordings without reprocessing audio.
Utterances
Segment streaming Hindi audio into real-time sentence-level units for voice agents.
Punctuation
Add accurate punctuation and capitalization to Hindi transcripts for easy reading.
Redaction
Automatically remove sensitive data like credit cards, phone numbers, and PII from Hindi transcripts.
Keyterm prompting for Hindi
Boost recognition of brand names, product terms, and domain-specific vocabulary in Hindi audio to improve keyword recall and transcript accuracy.
Voice AI that performs in the real world
Low latency. Effortless scale. Production-grade reliability. Everything you need to build and run real-time voice experiences.
Domain-aware speech
Understand specialized terminology, names, accents, and industry-specific language with production-grade accuracy.
Expressive voices
Give every interaction a natural, consistent voice that adapts its tone when the conversation changes.
Context-aware delivery
Carry meaning, tone, and conversational state across turns so agents listen and respond more naturally.
Build global voice agents with Flux Multilingual
Build and scale global voice agents with one model
Supports 10 languages in a single conversational model, enabling teams to build and deploy voice agents globally with one integration. No per-language infrastructure or model orchestration required.
Ultra-low latency conversational speech recognition
Model-based turn detection delivers accurate end-of-turn decisions in under 400 ms, keeping conversations fluid and responsive across languages.
Monolingual-grade accuracy with real-time control
Flexible real-time control through language hints or automatic detection, with native code-switching and dynamic adaptation as conversations evolve.
Scale beyond Hindi with one API
Start with Hindi speech-to-text, then expand to 45+ languages using the same API, models, and tooling.
FAQ
Speech-to-text (also called automatic speech recognition, or ASR) converts spoken Hindi audio into written text. Deepgram processes the audio signal, detects speech in real time, and returns structured transcript data through an API. Developers use it for applications like voice agents, call analytics, captioning, and searchable media.
Yes. Deepgram supports automatic speech recognition (ASR) for Hindi audio. You can transcribe real-time or prerecorded audio by setting `language=hi` in your API request.
Deepgram supports Hindi in the Nova and Flux model families. Model availability may vary by language, so check the Models & Languages page for the latest support details.
Yes. Deepgram provides low-latency streaming transcription for Hindi, suitable for live voice agents, call analytics, captioning, and other real-time applications.
Deepgram offers automatic language detection for many languages. You can enable it with `detect_language=true`. See the Language Detection documentation for the full list of supported languages.
Yes. Deepgram supports multilingual transcription and code-switching when using Nova's multilingual models. Set `language=multi` to enable it. See the Models & Languages page for supported languages.
Deepgram supports timestamps, punctuation, diarization, numeral formatting, filler-word detection, redaction, and more — all through the API.
Accuracy depends on model choice, audio quality, and domain. Nova models deliver state-of-the-art accuracy, and you can improve results further using Keyterm Prompting or Custom Models.
Sign up for a free Deepgram account to access your API key. You can test Hindi transcription instantly in the Playground, or call the API directly by sending audio to `/v1/listen` with `language=hi`. Starter apps and code examples are available on GitHub.
Ready to build with Hindi speech-to-text?
Start transcribing Hindi audio with Deepgram's speech-to-text API. It is fast, accurate, and built for real-time applications.
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