Short recommendation
Use a sentence-level, table-driven pipeline: translate+edit a master CSV, create per-sentence TTS via ElevenLabs API (consistent voice ID and settings), stitch or upload per-sentence audio to Canva and lock slide timings. Keep segments short (2–6s), add small padding, and run a two-stage QA (automated then native-speaker spot-check). This gives repeatability, predictable lip-sync, and easy re-renders when copy changes.

Decision criteria (pick what matters most)
- Output quality: If you need broadcast-grade emotion/intonation, budget for professional post-edit and human adaptation of translations. If acceptable, high-quality ElevenLabs voices are usually very good for demos.
- Speed vs accuracy: Machine translation + human edit is fastest; full human translation takes longer but reduces pronunciation errors.
- Team size & skill: Small solo teams benefit from scripted CSV + batch TTS. Larger teams can use a lightweight web UI to review translations and listen to segments before finalizing.
- Budget: Automated steps (ElevenLabs + ChatGPT for draft translations) save cost. If budget allows, invest in 1 native-speaker review per language.

Practical checklist / repeatable workflow
1. Prepare master script (CSV/Google Sheet): columns = segment_id, source_text, language, translated_text, voice_id, target_slide, note_for_pronunciation.
2. Translate + adapt: Use ChatGPT or your translator to draft translations, then have a native speaker edit. Add pronunciations or SSML hints in the note column.
3. Segmenting rules: Keep segments 2–6s (short sentences, or split long ones at natural breaks). Add 200–400 ms tail silence to each segment to help Canva timing/lip-sync.
4. TTS batch generation (ElevenLabs API): Request WAV/48k/24-bit (or 16-bit if file-size constrained). Use same voice_id and voice_settings for consistency. Generate one file per segment named like en_001.wav, es_001.wav.
- API tips: batch requests to respect rate limits; reuse voice IDs; if ElevenLabs supports SSML/prosody tokens, use them to adjust pacing; include brief breaths if needed.
5. Normalize & stitch: Normalize per-segment loudness to ~-16 LUFS (web/video). Either stitch into a single track in a DAW/FFmpeg when you want a single audio file, or upload per-segment audio to Canva and attach to each slide.
6. Sync in Canva: Import audio files. If using per-slide audio, place the correct audio on each slide and set slide duration to the audio length + small buffer. If using a stitched audio track, use an SRT or timecode sheet to set slide transitions to match timestamps.
7. QA checks before export: automated checks (segment length, LUFS, file names match CSV), then human checks (native-speaker listen for mispronunciations, unnatural breaks, context errors).
8. Final export & iterate: Keep the CSV as source of truth; any script change only requires regenerating affected segments.

Best-for / Avoid-if
- Best for: teams producing repeated multi-language demos with frequent copy updates and limited localization budget. Works well if you need fast re-renders.
- Avoid if: you need heavy lip-sync for closeup talking heads (you’ll need specialized facial animation or human ADR) or if every syllable must be phonetically controlled.

Quick QA metrics to track
- % segments reviewed by native speaker
- Average per-segment length (target 2–6s)
- Post-normalization LUFS (-16 web) and peak headroom
- Number of pronunciation fixes per video

Tools that help: ElevenLabs for high-quality voice generation; use ChatGPT for draft translations/SSML scaffolding, but always include human review when accuracy matters.

If you want, I can produce a CSV template and example API call pattern for ElevenLabs to start batching your TTS files.