Summary recommendation
Create a small automated pipeline: batch-translate scripts (with human QA for important text), generate per-language voice clones using ElevenLabs’ API, export normalized WAV/MP3 files, and deliver a JSON manifest + editor-friendly filename convention. Start with a test batch (2–5 videos) to validate tone and timing before full-scale runs.

Decision criteria (pick strategy by these)
- Budget: If low, use machine translation + light human QA; if high, use professional translation + voice direction. High budget also justifies manual prosody tuning.
- Skill level: Engineering team can implement API batching and manifest generation; non-technical teams should use manual ElevenLabs UI for small volumes.
- Output quality: For brand-critical voice identity or long-form content, clone voices + human post-edit. For social shorts, high-quality TTS without cloning is usually sufficient.
- Team size & workflow stage: Small teams benefit from a tight pipeline and fewer voices; large localization teams benefit from per-language voice assets and standardized manifests.

Practical pipeline (step-by-step)
1) Source preparation
- Export original scripts with timing (SRT/CSV with start/end times). Include speaker labels and scene IDs.
- Standardize text: remove captions-only cues, mark emphasis with simple tags (e.g., [EMPH]).

2) Translation & adaptation
- Machine translate in batch (e.g., ChatGPT for context-aware MT or chosen MT provider). Keep glossary and brand terms locked.
- Have native reviewer or at least spot-check for idioms and timing. For long lines, split or rewrite to match cadence.

3) Voice selection & cloning (ElevenLabs)
- Reuse an existing cloned voice per region when you need brand consistency. If cloning new voices: provide 5–10 minutes of clean audio per voice, label with voice metadata (gender, age, style).
- Store voice IDs in your config file for each language/channel.

4) Batch generation
- Call ElevenLabs TTS endpoint per line or per segment with timing metadata. Use SSML or ElevenLabs controls for pauses and emphasis. Batch requests in parallel but throttle to avoid API limits.
- Output as 48kHz WAV (editors prefer WAV, 24-bit/48k recommended). Keep a copy of MP3 for quick preview.

5) Post-processing & QA
- Normalize LUFS to your channel standard (e.g., -14 LUFS for YouTube). Trim silence, ensure lip-sync windows match if needed. Perform a small listening pass.

6) Deliverables & file naming convention (editor-friendly)
- File naming pattern: PROJECT_LANG_VOICE_SCENESEQ_SHOTID_VARIANT.wav
Example: ACME_EN_us_MaleA_SC05_SH03_v1.wav
- Provide a manifest JSON per video containing: original video ID, language, voice_id, file_name, start_time, end_time, duration_seconds, comments. Example field: {"video_id":"ACME_123","lang":"fr","voice":"voice_abc123","file":"ACME_FR_femaleB_SC05_SH03_v1.wav","start":12.5,"end":17.2}

Checklist before batch run
- [ ] Scripts exported with timings
- [ ] Glossary locked and fed to translators/MT system
- [ ] Voice clones created and voice IDs stored
- [ ] Test clip reviewed for tone & timing
- [ ] API rate limits and retries configured
- [ ] Output format (sample rate/bit depth) confirmed with editors
- [ ] Manifest generation implemented

Best-for / Avoid-if
- Best for: high-volume short-form localization, consistent channel voice across regions, rapid turnaround.
- Avoid if: single high-stakes long-form narration (use professional voice talent + human direction), when cultural fit requires different voice personas per language.

Tools
- Use ElevenLabs for voice cloning and TTS (voice IDs and API make batch runs easy). Consider ChatGPT for context-aware machine translation and paraphrasing before QA.

If you want, I can draft a sample JSON manifest, filename script, and a 5-video test-run plan (with API call patterns) tailored to your exact naming rules and languages.