AI Models

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

Cara milih model sing bener

Model transkripsi kang béda-béda bisa digunakaké ing wewengkon kang béda. Gunakaké panduan iki kanggo milih model kang paling apik kanggo kabutuhanmu.

Model WER Speed Basa Paling apik kanggo
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 …

Apa iku WER (Word Error Rate)?

Tingkat Kesalahan Kata (WER) iku metrik standar kanggo ngukur akurasi pengenalan basa. Iki ngitung persentase tembung ing transkripsi kang beda karo referensi. Tingkat WER 5% tegesé kira-kira5saka saben 100 tembung duwé kesalahan. Manawa luwih endhek luwih apik.

Ing basa Inggris, tembung "average" iku tegesé rata-rata, lan "average" iku tegesé rata-rata, lan "average" iku tegesé rata-rata.

Ora yakin manawi model ingkang kedah dipunginakaken?

Coba default kita - Whisper Large V3 Turbo nyedhiyani keseimbangan paling apik saka kecepatan lan akurasi. Free kanggo miwiti, ora perlu ndhaptar.

Miwiti transkripsi gratis