AI Models
Choose Your Transcription Engine — Compare accuracy, speed, and language support across leading speech recognition models.
如何选择正确的模型
不同的转录模型在不同领域都很出色。 使用此指南来选择适合您需要的最佳模型 。
| Model | WER | Speed | 语言语言语言语言语言 | 最佳 |
|---|---|---|---|---|
| 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 … |
WER(错误率)是什么?
WER 值是测量语音识别准确度的标准度量。 它计算出与引用值不同的笔录中的单词百分比。 5%的 WER 表示每100个单词中大约5个包含错误。 更低则更好 。
专业的人类笔记家通常达到4-5%的 WER。 最好的人工智能模型现在与清洁音频匹配或接近人类水平的精确度。