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

Nola aukeratu modelo egokia

Transkribapen-eredu desberdinak arlo desberdinetan dira bikainak. Erabili gida hau zure beharretara egokitzen den modeloa aukeratzeko.

Model WER Speed Hizkuntzak Honako hauentzako onena
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 …

Zer da WER (Hitzen Errore Tasa)?

Hitzen errore-tasa (WER) hizketa-ezagutza zehaztasuna neurtzeko metrika estandarra da. Erreferentziatik desberdina den transkripzioko hitzen ehunekoa kalkulatzen du. %5eko WER batek esan nahi du 100 hitzen artean gutxi gorabehera 5ek errorea dutela. Zenbat eta txikiagoa, orduan eta hobe.

Giza transkribatzaile profesionalek %4-5eko WER bat lortzen dute normalean. AI modelorik onenek gaur egun giza mailako zehaztasuna lortzen dute edo hurbiltzen dira audio garbian.

Ez dakizu zein modelo erabili?

Probatu gure lehenetsia — Whisper Large V3 Turbo-k abiadura eta zehaztasunaren arteko oreka onena eskaintzen du. Doakoa da hasteko, ez da erregistrorik behar.

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