Transcribe Spanish audio to text

Transcribe Spanish audio to text in seconds — from Madrid to Buenos Aires to Mexico City. Our Whisper-powered engine handles every regional accent.

  • Spanish-language podcast episodes, interview shows, and radio segments
  • Customer support calls and sales recordings from Spain, Mexico, and LATAM
  • University lectures, academic research interviews, and oral-history projects
  • YouTube and TikTok creators who repurpose long-form audio into searchable text
  • Wide regional variation — Castilian (Spain), Mexican, Rioplatense (Argentina), and Andean accents can shift word choice and pronunciation dramatically
  • Rapid speech and frequent code-switching between Spanish and English, especially in US-based content
  • Colloquial slang and diminutives ("chiquito", "ahorita") that confuse generic transcription models
  • For best results, use audio recorded in a quiet environment with a single speaker when possible
  • If your recording is multi-speaker (interview, panel), keep speakers at a similar distance from the mic
  • When the model is unsure, it defaults to Mexican Spanish — the most common training data. For Castilian or Argentine content, expect regional spelling ("vosotros" vs "ustedes") to be normalized to Latin American forms

Upload any audio file in mp3, mp4, wav, m4a, ogg, flac, webm format, up to 25 MB. Files are processed securely and never stored on our servers.

How accurate is AiScribe for Spanish audio?
On clean, single-speaker Spanish audio, AiScribe achieves near-human accuracy for both Castilian and Latin American variants. Heavy background noise, multiple overlapping speakers, or strong regional accents can reduce accuracy.
Can it distinguish between Spanish and code-switched English/Spanish?
Yes. The model handles code-switching — the common practice of mixing English and Spanish in the same sentence — far better than older transcription services. It will transcribe each word in its original language.
Does it support Mexican vs Castilian vs Argentine Spanish differently?
The underlying model is trained on all major regional variants, so it understands them. Output is normalized to standard spelling — for example, "vosotros" used in Spain will appear as it was spoken, but punctuation and capitalization follow Latin American norms by default.

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