Multilingual transcription software

One workflow for every language you record in

Most teams that need multilingual transcription do not work in one language, so Sonix runs every language through the same account, the same pipeline and the same editor.

A market research firm runs focus groups in Spanish on Monday and Mandarin on Thursday. A newsroom fields interviews in Arabic, Hebrew and English in the same week. A university research group collects oral histories in Urdu and Bengali. None of them wants a different vendor for each language.

Every language runs through the same steps: upload from your computer, Dropbox, Google Drive or a URL, let the AI transcribe at 10x real-time speed, then polish the transcript in the browser-based editor, where clicking any word jumps to that moment in the audio.

The output works the same way in every language, too. Export to Word, PDF, plain text, SRT, VTT and 18+ formats in all, with or without timestamps and speaker labels. Whether the source audio is German or Tagalog, your downstream workflow never changes.

Each supported language has its own dedicated page with dialect notes, format details and language-specific answers. Jump to the language directory above, or start with our most requested guides: transcribe Spanish audio, transcribe Arabic audio and transcribe Cantonese audio.

Which languages transcribe best?

Sonix delivers 85% to 99% accuracy across supported languages depending on audio quality; the language sets the ceiling, and the recording decides where under that ceiling your transcript lands.

Not all languages transcribe equally well, on Sonix or on any AI transcription platform. Where a given file lands in that range depends on three things: the language itself, the speakers and the recording.

High-resource languages sit at the top of the range

Speech recognition models learn from transcribed audio. Languages with enormous amounts of training data available, such as English, Spanish, French, German, Portuguese, Mandarin and Japanese, tend to produce the strongest results. There is simply more recorded, transcribed speech for the models to learn from, across more accents, more recording conditions and more topics.

For these languages, clean audio with distinct speakers routinely lands at the high end of the accuracy range, which is why professional transcription leans on them.

Lower-resource languages deserve a closer edit

Languages with smaller bodies of training data, such as Bashkir, Uyghur, Mongolian or Welsh, still transcribe well on clear audio, but their transcripts benefit from a closer editing pass. Budget a few extra minutes in the Sonix editor for these languages, especially for proper nouns and technical vocabulary.

This is not a reason to avoid AI transcription for lower-resource languages. It is a reason to use a tool with a fast correction workflow. The Sonix editor plays audio in sync with the text, so verifying a doubtful word takes one click instead of a scrub through the file.

The broad middle: strong results with normal review

Most of the 54+ supported languages sit between those poles. Languages like Hindi, Bengali, Urdu, Hebrew, Cantonese, Vietnamese, Turkish, Polish and Indonesian have substantial training data and mature recognition models. On clean audio they perform very well; on noisy or heavily accented audio they need the same review pass you would give an English file recorded in the same conditions.

Two patterns are worth knowing for this group. First, formal registers transcribe better than casual ones: a Hindi news broadcast comes back cleaner than a Hindi family phone call, because broadcast speech is closer to the training data. Second, names and borrowed words are the most common corrections. A transcript of a Bengali interview about local politics will nail the Bengali but may stumble on an English company name dropped mid-sentence.

What drives accuracy in each language

Four factors matter more than the language ranking.

• Training data depth. More transcribed speech in a language means better recognition of that language's real-world variety, accents and all.
• Dialect distance. A model trained mostly on one standard variety makes more errors on distant dialects. This affects Arabic more than almost any other language, as the next section explains.
• Tonal and contextual complexity. Tonal languages like Mandarin, Cantonese, Vietnamese and Thai encode meaning in pitch. Modern models handle tone well in context, but homophones in noisy audio remain a common correction point.
• Audio quality. This is the biggest lever you control. A lapel mic in a quiet room in Urdu beats a phone recording in a reverberant hall in English. Minimal background noise, distinct speakers and minimal crosstalk push any language toward the top of the accuracy range.

How to push any language to the top of the range

You cannot change how much training data exists for your language, but you control almost everything else.

• Record close to the speaker. An external or lapel microphone within a few feet of the source beats a laptop mic across the room, in every language.
• Separate your speakers. Crosstalk hurts accuracy and speaker labeling. In interviews and focus groups, encourage one voice at a time.
• Cut background noise at the source. Close windows, pause the air conditioner and avoid cafes. Noise your ear ignores, the model does not.
• Match the language to the audio. Pick Cantonese for Cantonese, not Mandarin. Choose the specific language page from the directory above and check its dialect notes before uploading.
• Budget edit time by tier. For high-resource languages, plan a light skim. For lower-resource languages and dialect-heavy audio, plan a focused pass on names, numbers and technical terms.

The practical takeaway: language choice sets the ceiling, but recording quality decides where under that ceiling your transcript lands. A well-recorded Urdu interview will beat a badly recorded English one.

Dialects and scripts

A language menu with 54+ entries only tells part of the story; what matters day to day is how a platform handles the variation inside each language.

Cantonese is not Mandarin

Sonix treats Chinese as two distinct options: Cantonese and Mandarin. They are not mutually intelligible in speech, and running Cantonese audio through a Mandarin model produces unusable output. Select the variety your speakers actually use. For Hong Kong media, family archives and Cantonese interviews, use the dedicated Cantonese transcription option; for Mainland and Taiwanese audio, use Mandarin transcription.

Arabic across its dialect continuum

Arabic spans Modern Standard Arabic plus regional families including Egyptian, Levantine, Gulf and Maghrebi. Broadcast and formal speech in Modern Standard Arabic transcribes most reliably; conversational dialect audio is more variable and benefits from an editing pass. The Arabic transcription page has dialect-specific guidance.

Portuguese: Brazil and Portugal

Sonix offers a single Portuguese option covering both Brazilian and European speech. Pronunciation differs noticeably between the two and vocabulary diverges in places, so give proper nouns and regional terms a quick check whichever variety you upload. The Portuguese transcription page covers both.

Right-to-left scripts

Transcripts for Arabic, Hebrew, Persian and Urdu come back in their native right-to-left scripts, not romanized approximations, and the editor lays the text out right to left while you correct it. See the Hebrew transcription and Urdu video transcription guides for the specifics.

Native script, not romanization

Languages with non-Latin writing systems, including Hindi (Devanagari), Bengali, Tamil, Thai, Korean, Japanese and Greek, come back in their own scripts. If you need a Latin-script version for a downstream tool, transcribe in the native script first, then translate the transcript into English. Transcribing in the native script preserves accuracy; romanizing at the recognition stage does not.

Automatic language detection for multi-language audio

When you do not know which language a file is in, or a recording switches between languages, Sonix can identify the language for you or transcribe several languages in one file.

Auto-detect the language

Choose "Auto-detect language" at upload when you are handling files from many sources and cannot check each one. Sonix identifies the spoken language and routes the file to the right recognition model, so a batch of interviews in Spanish, French and German can be uploaded together without sorting them first.

Several languages in one recording

For a file that switches languages, choose "Transcribe in multiple languages" and select every language spoken. Sonix transcribes each stretch of the conversation in the language it hears, so a bilingual focus group that drifts between English and Spanish, a diaspora interview that mixes Urdu and English, or an international panel with an Arabic speaker beside a French one comes back as one transcript.

One honest limitation, straight from how the feature works: it performs best when language switches happen between speakers rather than mid-sentence. Rapid code-switching inside a single sentence is the hardest case in speech recognition. For heavily code-switched audio, transcribe in the dominant language and correct the exceptions in the editor.

When you know the language, say so

If you already know the language of your file, select it directly. Naming the language removes the identification step and guarantees the right model handles your audio from the first second.

Transcribe first, then translate

The most reliable path from foreign-language audio to English text is a two-step workflow: transcribe the audio in its original language, then translate the finished transcript.

Transcribing in the source language keeps the recognition model on familiar ground, and translation then works from clean text instead of guessing at audio. Sonix builds this handoff in: once your transcript is ready, translate it into any of 54+ languages with one click, powered by neural machine translation that preserves context and meaning. Translations are delivered in minutes, in the same account, with no re-uploading.

Typical uses: translate Spanish focus-group transcripts into English for the client report, turn Arabic interview transcripts into English subtitles, or localize an English podcast transcript for a German audience. Because translation starts from the corrected transcript, any fix you make in the editor carries into every translation.

For video teams, the same handoff powers multilingual captioning. Transcribe the original audio, correct it once, translate, and export SRT or VTT subtitle files for each target language. The timestamps from the original transcript carry through, so translated captions stay synced to the video without manual retiming.

Learn more about Sonix translation, or see how the full pipeline works on our automated transcription page.

Built for multilingual teams

The same upload serves a research group, a newsroom, a legal team, a market research firm and a podcaster; only the language changes.

Researchers and academics

Oral history projects, linguistics fieldwork and cross-border studies generate audio in whatever language participants speak. Sonix returns native-script transcripts with timestamps, so citations point to exact moments in the recording.

Newsrooms and media

Reporters file interviews in Arabic, Hebrew, Spanish and French from the field. Transcribing in the source language first preserves exact quotes; translation happens afterward, from verified text.

Legal and compliance teams

Multilingual testimony, client calls and evidence audio need accurate, timestamped records. Word-level timestamps make it fast to check any disputed passage against the original audio.

Market research firms

Focus groups in Spanish, Cantonese or Portuguese become searchable text in minutes, so analysts can code themes the same day instead of waiting on a translation vendor.

Podcasters and video producers

Transcripts in the original language power show notes and search visibility; one-click translation turns the same transcript into subtitles for new markets through SRT and VTT export.