Multilingual Transcription vs Translation — Quick Disambiguation
Different jobs. Multilingual transcription keeps the audio's language in the transcript — Spanish audio produces Spanish text. Translation converts text or audio from one language into another — Spanish audio to English text. Different products, different vendors: for text-to-text translation, DeepL and Google Translate lead. VexaScribe handles multilingual transcription (99 languages) and also offers Whisper's translate-to-English mode for single-step Spanish/French/German audio → English transcripts — see transcribe and translate audio.
What Is Multilingual Transcription?
Multilingual transcription is the process of converting spoken audio or video into written text across different languages. Rather than being limited to English or a handful of major languages, modern AI transcription tools can process speech in dozens or even hundreds of languages with high accuracy.
VexaScribe offers two modes for multilingual transcription: single-language mode, where you specify the language being spoken, and auto-detect mode, where the AI identifies the language from the audio itself. Single-language mode tends to be slightly more accurate since the AI knows exactly which language model to use. Auto-detect is convenient when you are unsure of the language or processing files in bulk.
Multilingual transcription is essential for global teams collaborating across borders, content creators reaching international audiences, and researchers working with foreign-language source material. Explore our audio transcription and speech to text tools to get started.
Supported Languages
+60 more languages supported
How Multilingual Transcription Works
Upload in Any Language
Drag and drop your audio or video file. VexaScribe accepts MP3, WAV, M4A, MP4, MOV, WEBM, and more. Upload recordings in any of the 99+ supported languages.
Select or Auto-Detect Language
Choose the spoken language from the list, or let our AI automatically detect it. The language detection analyzes speech patterns in the first portion of the audio.
Get Timestamped Transcript with Speaker Labels
Receive your transcript with accurate timestamps and speaker identification. Review, edit, and export as TXT, DOCX, or SRT in any language.
Who Needs Multilingual Transcription?
Global Teams
Transcribe international meetings where team members speak different native languages. Keep records of every conversation across time zones.
Content Creators
Transcribe YouTube videos and podcasts in any language. Reach wider audiences by creating subtitles and show notes from foreign-language content.
Academic Researchers
Transcribe interviews, field recordings, and lectures in the language they were conducted. Essential for ethnographic studies and cross-cultural research.
Legal & Immigration
Transcribe depositions, hearings, and client interviews conducted in non-English languages. Critical for immigration cases and international legal proceedings.
Healthcare
Transcribe patient consultations and medical dictations in the patient's preferred language. Supports multilingual healthcare environments and telemedicine.
Journalism
Transcribe foreign-language interviews and press conferences. Get accurate transcripts from sources speaking any of the 99+ supported languages.
Why Language Support Matters
Not all transcription tools support the same number of languages. Here is how the leading services compare:
| Service | Languages Supported |
|---|---|
| VexaScribeYou are here | 99+ |
| Otter.ai | 3(English, French, Spanish only) |
| Sonix | 53 |
| Rev | Limited AI |
| Descript | ~20 |
| HappyScribe | 120+ |
| TurboScribe | 98 |
Language counts are approximate and based on publicly available information. Otter.ai notably supports only 3 languages, making it unsuitable for multilingual workflows.
STT Model Language Coverage Compared (2026)
The hosted transcription tools above wrap one of five underlying STT models. If you're integrating multilingual STT into a product or self-hosting for scale, here's how the models themselves compare on language coverage — not marketing counts, actual documented language support at verification date.
| Model | Languages supported | Auto-detection | RTL support | Best-supported | Weakest tier |
|---|---|---|---|---|---|
| OpenAI Whisper Large-v3 | 99 | Yes | Yes | EN, ES, DE, FR, IT, PT, NL, RU, JA, ZH, KO | Low-resource African, endangered |
| Whisper Large V3 Turbo (VexaScribe default) | 99 | Yes | Yes | Same as Large-v3, ~1-3% WER hit on some | Same as Large-v3 |
| Deepgram Nova-3 | ~40 | Yes | Partial | EN, ES, FR, DE, IT, PT, HI, JA, ZH, RU | Regional dialects, low-resource |
| Microsoft Azure Speech | 140+ locales (nl-NL, nl-BE separate) | Yes | Yes | EN, ES, FR, DE, IT, PT, ZH, JA, KO, AR, HE | Endangered / low-resource |
| AWS Transcribe | ~30 | Yes | Partial | EN, ES, FR, DE, IT, PT, ZH, JA, KO, AR | Long-tail languages |
| Google Speech-to-Text v2 / Chirp | 125+ | Yes | Yes | EN, ES, FR, DE, IT, PT, ZH, JA, KO, HI, AR | Endangered / low-resource |
Methodology note: Language count ≠ language quality. Each vendor tests on different reference sets, so head-to-head WER on identical audio isn't published. Numbers verified 2026-07-23 against vendor language-support documentation: OpenAI Whisper paper (Radford et al. 2022), Deepgram docs, Azure Speech language-support, AWS Transcribe supported-languages, Google Speech-to-Text v2 languages.
Which STT Model for Which Language Family
Western European (EN, ES, FR, DE, IT, PT, NL)
All major models are excellent. Whisper self-hosted wins on price; VexaScribe wins on hosted convenience (Whisper Large V3 Turbo backend, no infra). Deepgram best for real-time streaming APIs. Azure/Google if already on that cloud.
CJK (Chinese, Japanese, Korean)
Whisper Large-v3 leads on accuracy per the Whisper paper. Azure Speech (Chirp) is a strong enterprise alternative. Deepgram support is functional but weaker than Whisper on CJK. Google Chirp added strong CJK coverage in 2025.
RTL (Arabic, Hebrew, Persian, Urdu)
Whisper and Azure both fully supported with correct text-direction handling. VexaScribe preserves RTL in TXT, DOCX, and SRT exports. AWS Transcribe partial. Google Chirp supported.
South Asian (Hindi, Bengali, Tamil, Telugu, Urdu)
Whisper and Azure best-supported. Google Chirp expanded Indic coverage in 2025. Deepgram supports Hindi well but weaker on other Indic languages.
Slavic (Russian, Polish, Ukrainian, Czech, Croatian, Serbian)
Whisper Large-v3 excellent across major Slavic. Azure strong. Google Chirp good coverage. Deepgram limited beyond Russian.
Low-resource / endangered
Whisper Large-v3 has surprisingly broad coverage (99 total) but WER climbs on endangered languages. No commercial model handles Frisian, Basque, or many African languages well. Expect 15-25%+ WER for endangered/low-resource content.
Language-Specific Tool Comparisons
Deep-dive comparisons for each major language — WER benchmarks, dialect handling (Netherlands Dutch vs Flemish, pt-BR vs pt-PT, MSA vs Egyptian Arabic), and tool ranking calibrated to that language:
Automatic Language Detection
VexaScribe analyzes the speech patterns in the first seconds of your audio to automatically identify the language being spoken. This works reliably for all Tier 1 and Tier 2 languages. Once detected, the appropriate language model is loaded and the full audio is processed.
When to use auto-detect: Use auto-detect when you are processing files in bulk and do not know the language of each file, or when you receive recordings from international sources. It is also useful for quick uploads where selecting a language manually feels like an extra step.
When to select manually: If you know the language, selecting it manually can produce slightly better results. This is especially true for closely related languages (e.g., Norwegian vs. Danish, or Malaysian vs. Indonesian) where the AI might need a hint.
Code-switching limitation: If speakers switch between languages mid-sentence (code-switching), auto-detect will pick the dominant language. The transcript may be less accurate for the secondary language segments. For such recordings, we recommend selecting the dominant language manually.
Affordable Pricing
Same price for all languages. No premium rates for non-English transcription.
View pricing plansMultilingual Transcription Features
Everything you need to transcribe audio in any language.
99+ Languages
Transcribe audio in over 99 languages, from widely spoken ones like English and Spanish to regional languages like Tagalog and Swahili.
Automatic Language Detection
Let the AI identify the spoken language automatically. No need to know the language in advance — the system analyzes speech patterns and selects the correct model.
Speaker Diarization (Language-Independent)
Identify and label different speakers in any language. Speaker detection works by voice characteristics, not language, so it functions equally well across all 99+ languages.
Multiple Export Formats
Export your multilingual transcripts as TXT, DOCX, or SRT. All formats preserve the original language text, including right-to-left scripts like Arabic and Hebrew.
Batch Processing for Mixed-Language Files
Upload multiple files in different languages and process them all at once. Each file is detected and transcribed in its own language independently.
Timestamps in All Languages
Every transcript includes word-level timestamps regardless of language. Navigate to any point in your recording, whether it is in Japanese, Arabic, or Portuguese.
Multilingual Transcription FAQ
How many languages does VexaScribe support?
VexaScribe supports transcription in 99+ languages, including all major world languages and many regional languages. From widely spoken languages like English, Spanish, and Mandarin to less common languages like Basque, Swahili, and Tagalog.
How accurate is multilingual transcription?
Accuracy varies by language and audio quality. Tier 1 languages (English, Spanish, French, German, etc.) achieve the highest accuracy. Less common languages may have slightly lower accuracy but are continuously improving. Clear audio with minimal background noise produces the best results.
Can it transcribe audio with multiple languages?
VexaScribe works best with single-language audio. If your recording has speakers using different languages in separate segments, upload and specify the primary language. For recordings where speakers switch languages mid-sentence, results may vary — we recommend selecting the dominant language.
Does language detection happen automatically?
Yes, VexaScribe can automatically detect the spoken language from the audio. The AI analyzes the speech patterns in the first portion of the recording. You can also manually select the language before upload if you already know what it is.
Is multilingual transcription more expensive?
No. VexaScribe charges the same rate regardless of language. Whether you transcribe English, Japanese, or Arabic, the pricing is identical. Some competitors charge premium rates for non-English languages — we don’t.
Can I get speaker labels in non-English transcripts?
Yes, speaker identification works across all supported languages. The AI detects different speakers by voice characteristics, which is language-independent.
What about right-to-left languages like Arabic and Hebrew?
VexaScribe fully supports RTL languages including Arabic, Hebrew, Persian (Farsi), and Urdu. The transcript text displays in the correct reading direction. Export formats preserve RTL text direction.
How do I transcribe a YouTube video in a foreign language?
Copy the YouTube URL or download and upload the video file, select the language or use auto-detect, and the AI will transcribe it. Works for any of the 99+ supported languages.
What's the difference between multilingual transcription and translation?
Multilingual transcription keeps the audio's language in the transcript — Spanish audio produces Spanish text. Translation converts text or audio from one language into another — Spanish audio to English text. Different products, different vendors: for text-to-text translation, DeepL and Google Translate lead. VexaScribe handles multilingual transcription (99 languages) and offers Whisper's translate-to-English mode for single-step Spanish/French/German audio → English transcripts.
How many languages does Whisper support?
OpenAI Whisper Large-v3 (and Whisper Large V3 Turbo, the default in VexaScribe) supports 99 languages according to the Whisper paper and repository. Coverage quality varies by language: Western European, CJK (Chinese, Japanese, Korean), and major world languages have the highest accuracy. Low-resource and endangered languages are supported but with higher WER.
How many languages does Deepgram support?
Deepgram Nova-3 currently supports approximately 40 languages via its API. Coverage focuses on major world languages: English (with regional variants), Spanish, French, German, Italian, Portuguese, Dutch, Hindi, Japanese, Chinese Mandarin, Korean, Russian, and others. Fewer than Whisper's 99, but strong quality on the supported set. Check deepgram.com/docs/language-support for the current list.
How many languages does AWS Transcribe support?
AWS Transcribe supports approximately 30 languages with automatic language identification. Language coverage is smaller than Whisper or Azure but AWS Transcribe integrates natively with other AWS services (S3, Lambda, Comprehend) which matters for teams already on AWS. Check the AWS Transcribe supported-languages documentation for the current list.
Does Google Speech-to-Text support my language?
Google Speech-to-Text v2 (Chirp / Chirp 2) supports approximately 125 language locales including regional variants like nl-NL, nl-BE, pt-BR, pt-PT, en-US, en-GB, es-ES, es-MX, and many more. Best fit if your infrastructure runs on Google Cloud. Check the official Google Speech-to-Text supported-languages docs for current coverage.
Which STT is best for Arabic, Chinese, or Japanese?
For CJK (Chinese/Japanese/Korean) and Arabic: OpenAI Whisper Large-v3 leads on accuracy per the Whisper paper benchmarks, and Microsoft Azure Speech (Chirp) is a strong alternative especially for enterprise integration. Deepgram's CJK support is functional but weaker than Whisper. For RTL languages (Arabic, Hebrew, Persian, Urdu), Whisper and Azure both preserve text-direction correctly in all export formats.
How well does automatic language detection work?
Whisper's auto-detection analyzes speech patterns in the first ~30 seconds of audio and achieves >95% accuracy on major supported languages. It struggles when: (1) audio starts with music or long silence, (2) recording is very short (under 10 seconds), (3) the language is a low-resource variant that Whisper confuses with a related language (Norwegian ↔ Swedish, Portuguese ↔ Spanish). For those cases, select the language manually. Deepgram, Azure, and Google use similar approaches with similar accuracy on well-supported languages.
Note: Transcription accuracy varies by language, audio quality, and speaker clarity. Tier 1 languages generally achieve the highest accuracy. Results for less common languages are continuously improving as AI models are updated.
Need to transcribe audio in a specific format or for a specific use case? Explore our other transcription tools below.
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