Quick Example

Walk through processing a 14-minute TED talk from start to finish. See how VideoCaptioner handles transcription, optimization, translation, and synthesis.

Processing Pipeline

Step 1: Speech Transcription

Using Faster Whisper Large-v2 model with Silero V4 VAD (Voice Activity Detection), the system generates initial subtitles from the audio track.

videocaptioner transcribe ted-talk.mp4 --asr faster-whisper --model large-v2

The raw output has high recognition accuracy, but the sentence breaking is mechanical with basic punctuation — this is normal for raw ASR output.

Step 2: Intelligent Enhancement

Enable smart segmentation, optimization, and translation. The LLM performs semantic-based segmentation, producing fluid natural-language subtitles.

videocaptioner subtitle ted-talk.srt \
  --optimizer llm \
  --translator llm \
  --target-language zh \
  --enable-reflection

Reflection Translation

The system uses a two-pass "translate-reflect-translate" approach for each subtitle:

• Removes redundant words for conciseness
• Naturalizes expressions to sound native
• Compacts phrasing while preserving meaning
• Optimizes verb selection and word order

Step 3: Video Synthesis

Burn the optimized subtitles into the video with a professional dual-language layout:

videocaptioner synthesize ted-talk.mp4 -s ted-talk_optimized.srt

Step 4: All-in-One Command

Or simply run the entire pipeline with a single command:

videocaptioner process ted-talk.mp4 --target-language zh

Performance

Cost Analysis

Ideal Use Cases

• Educational content — Create bilingual learning materials
• Content creators — Multi-language versions for global reach
• Business — Video localization for international markets
• Conferences — Documentation with accurate subtitles