AI Models

Choose Your Transcription Engine — Compare accuracy, speed, and language support across leading speech recognition models.

Como Escolher o Modelo Correto

Différents modèles de transcription excellent dans différents domaines.Utilisez ce guide pour choisir le meilleur modèle pour vos besoins.

Model WER Speed Lingue Best For
STT.ai Enhanced 3.2% 160.0x 100 STT.ai's flagship speech-to-text model with best-in-class accuracy and speed. Optimized …
Whisper Large V3 4.2% 8.0x 99 OpenAI's largest and most accurate Whisper model. Excellent multilingual support …
Whisper Turbo 5.1% 32.0x 99 OpenAI's speed-optimized Whisper variant. 4x faster than Large V3 with …
NVIDIA Canary 3.5% 45.0x 4 NVIDIA's multi-task ASR model with top-tier accuracy on English. Built …
Moonshine 7.8% 80.0x 1 Ultra-lightweight ASR model designed for edge devices. Runs on Raspberry …
NVIDIA Parakeet 3.0% 55.0x 1 NVIDIA's CTC-based English ASR model. One of the most accurate …
SenseVoice 5.5% 50.0x 50 Multilingual speech understanding model with emotion recognition and audio event …
Distil-Whisper 5.8% 48.0x 99 Distilled version of Whisper Large V3. 6x faster with 49% …
Vosk 12.0% 100.0x 20 Lightweight offline speech recognition. Works without internet, ideal for privacy-sensitive …

Ombi WER (Word Error Rate)?

Tasso d'errore di parola (WER) è la misura standard per misurare l'accuratezza del riconoscimento vocale. Calcola il percento di parole in una trascrizione che differiscono dalla riferimento. Un WER di 5% significa che circa 5 su ogni 100 parole contengono un errore.

Professional human transcriptionists typically achieve a WER of 4-5%. The best AI models now match or approach human-level accuracy on clean audio.

Non so quale modello usare?

Prova la nostra versione predefinita — Whisper Large V3 Turbo offre il miglior equilibrio tra velocità e precisione.Gratuito per iniziare, nessuna registrazione richiesta.

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