Turn Marathi audio
into text and translations.

A different approach to Marathi transcription.

Marathi is spoken by more than 83 million people as a first language, mostly across Maharashtra and Goa, and it shares the Devanagari script and a deep vein of vocabulary with Sanskrit, the classical language behind much of its religious and literary tradition. That overlap is exactly where generic transcription tools fall apart. A lecture, pravachan, or research interview will slip between conversational Marathi and a Sanskrit shloka mid-sentence, dip into Varhadi or Konkani-influenced speech, and a tool trained on one language treats the switch as noise: mangled words, dropped lines, or a transcript that reads confidently wrong.

Reading the language, not just the words

Speak AI reads the whole recording, not just the transcript. Each file is transcribed in your language, with 100+ supported, so Marathi and the Sanskrit terms inside it are captured together instead of flattened into English. Alongside the words, the recording itself is analyzed: the tone and pacing of the speaker, the emphasis on a particular shloka or phrase, and any on-screen text or slides captured through screen recording. Ask across your entire Marathi library with AI chat, using ChatGPT, Claude, and Gemini built directly into the workspace.

What teams ask their Marathi transcripts

• “Which Sanskrit terms and shlokas came up most often this month, and how were they translated?”
• “Show every recording where a speaker switched from Marathi into Sanskrit mid-sentence.”
• “Summarize this lecture in Marathi, then again in English.”
• “Which interviews mention Varhadi or Konkani-influenced Marathi, and what did participants actually say?”

From scattered files to a searchable archive

The result is a Marathi archive that behaves like a research database instead of a folder of MP3s. Sanskrit terms and shlokas are flagged instead of buried, translations sit next to the original line, and dashboards you can customize and white-label track how often specific terms, dialects, or topics come up across your library over time, this quarter measured against last. One education program built a similar multilingual pipeline around embedded recorders and automatic transcription and recovered 120 hours and $4,000+ in labor, without adding staff. And because Marathi recordings rarely live alone, the same engine connects to call scoring and coaching for any other language your team works in, plus a full MCP server so Claude, ChatGPT, and Cursor can query your Marathi transcripts directly.