Evaluating AI Video Translation Quality – Metrics that Matter
Translating videos across languages is no small feat — it involves transcription, translation, voice synthesis, timing, and more. At Curify, we’ve built a robust evaluation pipeline to ensure each piece meets industry standards.
1. Transcription Quality
Engine: WhisperX
- WER (Word Error Rate)
- Punctuation F1 (for expressiveness and readability)
2. Translation Quality
Engines: Helsinki, MarianMT
- BLEU (standard metric)
- COMET / chrF++ (semantic similarity)
- Human review: fluency + adequacy
3. Voice Synthesis Quality
Engines: XTTS / YourTTS
- MOS (Naturalness, similarity, expressiveness)
- Speaker verification accuracy
4. Alignment & Lip Sync
- Segment duration mismatch
- Wav2Lip sync confidence
- Temporal drift analysis
5. Semantic Preservation
We use LLMs (like GPT-4) to judge whether the translated speech preserves the original meaning, tone, and emotion. Example prompt:
Compare this Mandarin transcript to the English voiceover. Does the tone, intent, and content match? Rate 1–5 and explain.
6. User Feedback & GTM Validation
- Voice quality fit for product category
- Viewer retention improvement
- Adoption willingness from early users (e.g., 1688 sellers)