Voice recognition in your language. Supports Marathi, Hindi, Gujarati, Tamil, Malayalam, Arabic, Japanese, and 90+ more languages. No language packs needed.
Comprehensive language support with no additional downloads
All languages built into the AI model. No separate language packs to download. Works offline with full multilingual capability.
Whisper identifies spoken language automatically. No need to manually select language before transcription. Switch between languages seamlessly.
Outputs text in native scripts: Devanagari, Arabic, Chinese characters, Japanese kana, Korean Hangul. Proper character rendering and Unicode support.
Handles speakers mixing multiple languages within single conversation. Common in bilingual communities and international business contexts.
Trained on diverse speakers with various accents. Works with regional variations and non-native speakers. Less accent bias than older systems.
Process all languages locally without internet. Privacy-focused multilingual transcription for sensitive communications. See offline speech to text details.
Modern AI speech recognition models like OpenAI Whisper support dozens of languages without requiring separate training data or language-specific models. Single unified model handles 99+ languages, trained on 680,000 hours of multilingual audio from diverse sources.
This approach contrasts with older systems requiring separate models per language. Unified multilingual models benefit from cross-lingual transfer learning, where patterns learned from one language improve performance in others. This technology powers OpenAI Whisper speech to text.
Full support for Spanish, French, German, Italian, Portuguese, Russian, Polish, Dutch, Swedish, Norwegian, Danish, Finnish, Greek, Romanian, Czech, Slovak, Hungarian, Ukrainian, and dozens more.
Transcribe meetings and calls conducted in multiple languages. Useful for multinational companies with diverse teams. Convert client communications in their native language for documentation and analysis.
Students practice pronunciation by comparing spoken input to transcribed output. Language teachers analyze student speech patterns. Create transcripts of foreign language lectures and presentations. Also useful for audio to text transcription of recorded content.
Transcribe source language audio before translation. Provides text foundation for human or machine translation. Common workflow for content localization and subtitling.
Users communicate in heritage languages while living abroad. Transcribe messages to family members, document cultural stories, or maintain language connections across generations.
Anthropologists and ethnographers transcribe interviews in indigenous and minority languages. Oral history projects preserve stories in original languages. Linguistic research analyzes speech patterns across languages.
Different languages use various writing systems: Latin alphabet, Devanagari (Hindi, Marathi, Sanskrit), Arabic script, Chinese characters, Japanese kana/kanji, Korean Hangul, Cyrillic, Greek, Thai, and many others.
Ensure your software correctly renders and saves text in required script. Unicode support essential for non-Latin scripts. Font availability affects display quality.
Arabic, Hebrew, Persian, and Urdu write right-to-left. Proper text editor support needed for natural editing experience. Many Western applications lack adequate RTL support.
Mandarin Chinese, Cantonese, Vietnamese, and Thai use tones to distinguish word meanings. Speech recognition must handle tonal variations accurately. Whisper performs well on tonal languages due to extensive multilingual training.
Major languages have significant dialectal variation: Indian English vs British vs American, Modern Standard Arabic vs regional dialects, Mandarin vs Cantonese Chinese. AI models trained on diverse data handle variations better than region-specific older systems.
High-resource languages (English, Spanish, Chinese) have abundant training data. Low-resource languages have limited publicly available speech data. Whisper's web-scale training provides reasonable coverage even for less common languages.
Languages with large character sets (Chinese: 20,000+ characters, Japanese: 2,000+ kanji) present unique challenges. Token-based models must efficiently represent diverse scripts within fixed vocabulary size.
Agglutinative languages (Turkish, Finnish, Hungarian) create words by combining morphemes. Single word in these languages may correspond to entire phrase in English. AI models must handle variable word lengths and complex morphology.
Modern multilingual speech recognition works without language-specific configuration. Install software, select or auto-detect language, and start speaking. System handles script rendering and text formatting automatically.
For best results with non-Latin scripts, use text editors with proper Unicode support. Microsoft Word, Google Docs, and modern browsers handle all scripts correctly. Some older applications may display rendering issues.
Microphone quality and ambient noise affect all languages equally. Same audio best practices apply: quiet environment, quality microphone, consistent positioning.
AI advances continue expanding language coverage and improving accuracy for low-resource languages. Models increasingly handle code-switching (mixing languages mid-sentence) and regional accents.
Emerging research focuses on zero-shot language adaptation: models transcribing languages not seen during training by leveraging cross-lingual patterns. This may eventually provide universal speech recognition covering all human languages.