Urdu 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 Urdu speech recognition for real-world audio
Get real-time Urdu speech-to-text in under 300 ms while maintaining high accuracy in noisy, accented, or overlapping conversations.
Urdu language overview
Speakers: 230+ million total speakers
Regions: Pakistan (national language), India, UAE, Saudi Arabia, United States, United Kingdom, Canada, Afghanistan, Bangladesh
Dialects: Deccani, Karachi, Punjabi Urdu, Capital Area (Islamabad), Northwestern, Baluchistan
Writing system: Perso-Arabic script (Nastaliq/Naskh)
Language family: Indo-European
Urdu is widely used across Pakistan, India, and large diaspora communities in the Middle East and Western nations, making it a key language for customer support centers, healthcare services, government operations, media broadcasting, and multilingual voice agents.
Urdu speech-to-text capabilities
Diarization
Automatically detect and label who is speaking in multi-speaker Urdu conversations.
Smart formatting
Apply automatic capitalization, paragraphing, and clean transcript structure for Urdu text.
Search
Instantly find words or phrases inside long Urdu recordings without reprocessing audio.
Utterances
Segment streaming Urdu audio into real-time sentence-level units for voice agents.
Punctuation
Add accurate punctuation and capitalization to Urdu transcripts for easy reading.
Redaction
Automatically remove sensitive data like credit cards, phone numbers, and PII from Urdu transcripts.
Keyterm prompting for Urdu
Boost recognition of brand names, product terms, and domain-specific vocabulary in Urdu 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.
Scale beyond Urdu with one API
Start with Urdu 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 Urdu 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 Urdu audio. You can transcribe real-time or prerecorded audio by setting `language=ur` in your API request.
Deepgram supports Urdu in the Nova model family. 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 Urdu, 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 Urdu transcription instantly in the Playground, or call the API directly by sending audio to `/v1/listen` with `language=ur`. Starter apps and code examples are available on GitHub.
Ready to build with Urdu speech-to-text?
Start transcribing Urdu audio with Deepgram's speech-to-text API. It is fast, accurate, and built for real-time applications.
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