perf: eliminate redundant Translate API calls — detection double-billing, segment re-translation, no persistent cache

[beastoin]

Problem

The listen WebSocket endpoint (transcribe.py:1508) calls Google Cloud Translate API with three compounding inefficiencies that together inflate translation costs ~6–8x above necessary levels:

1. Detection double-billing (~43% of spend is redundant): Every segment triggers an explicit detect_language() API call (translation.py:191), then translate_text() auto-detects the source language again for free (no source_language_code is set at line 298). Both are billed at the same per-character rate. The detection call is pure waste.

2. Growing segments re-translated 5–10x: The 0.6s processing loop (transcribe.py:1414) re-sends the full segment text on every tick as Deepgram adds words. Early words in a segment are billed 5–10 times as the segment grows.

3. No persistent cache: Translation cache is an in-memory per-session OrderedDict (translation.py:250), max 1000 items, lost on disconnect. Detection cache is global in-memory, lost on pod restart. Zero cross-user or cross-session reuse. Redis exists in the codebase for other features but is not used for translation.

4. Per-sentence API calls with comma splitting: split_into_sentences() (line 244) splits on .?!, — commas create 3–5 tiny fragments per segment, each sent as a separate translate_text() call. The API supports contents=[] lists (line 299) but code only sends 1 item per call.

Code Flow

stream_transcript_process() [transcribe.py:1414]
  └─ every 0.6s, for each updated segment:
      └─ translate(updated_segments) [line 1508]
          ├─ detect_language(text) [translation_cache.py:26]
          │   └─ _client.detect_language() ← PAID, redundant
          └─ translate_text_by_sentence(text) [translation.py:269]
              ├─ split_into_sentences() ← splits on commas
              └─ for each sentence:
                  └─ _client.translate_text(contents=[text]) ← PAID, 1 item at a time

Solution (4 changes, Codex-reviewed)

Change 1: Eliminate explicit detect_language API

• Remove _client.detect_language() calls entirely
• Use response.translations[0].detected_language_code from translate response (free)
• Replace translated_text == segment_text check (line 1155) with detected_language_code == target_language — Translate normalizes punctuation/spacing even for same-language text
• Impact: ~43% reduction (eliminates entire detection cost line)

Change 2: Per-segment debounce queue

• Translate immediately on first segment appearance (zero UX delay)
• Debounce updates with 0.8–1.2s trailing window before re-translating
• If STT marks segment as "final," translate immediately (skip debounce)
• Track segment_version / last_text_hash to prevent stale out-of-order responses
• Pattern: per-segment tracking map, not asyncio queue:

  pending[segment_id] = {last_text, last_hash, last_update_at, task}

• Impact: 60–80% reduction in translation characters

Change 3: Sentence-level Redis cache

• Key: translate:v1:{md5(sentence)}:{dest_lang} → translated text
• TTL: 7–30 days + Redis LRU eviction
• Sentence granularity provides best cache hits (intra-session growing segments + cross-user common phrases)
• Store detected_language_code alongside cached translation for ambiguous short text
• Impact: additional 10–15% reduction on remaining characters

Change 4: Fix sentence splitting + batch API calls

• Remove comma-based splitting — creates tiny ambiguous fragments with worse translation quality
• Split on .?! and newlines only, length-based fallback (max 200–300 chars)
• Batch all cache-miss sentences into one contents=[miss1, miss2, ...] call
• Impact: overhead/latency reduction, better translation quality

Correctness Guardrails

1. Use detected_language_code == target_language instead of text equality — Translate normalizes punctuation/spacing/quotes
2. Track segment version to prevent out-of-order responses overwriting newer translations
3. Version cache keys (translate:v1:...) for future compatibility
4. Mixed-language segments: auto-detect picks dominant language (pre-existing limitation, unchanged)

Files to Modify

File	Changes
backend/utils/translation.py	Remove detect API, add Redis cache, fix splitting, add batching
backend/utils/translation_cache.py	Simplify or remove (detection no longer needed)
backend/routers/transcribe.py	Add debounce queue in translate(), use detected_language_code
backend/database/redis_db.py	Add translation cache helpers (follows existing cache_signed_url pattern)

Estimated Combined Impact

Change	Reduction
Eliminate detection	~43%
Dedup queue	~35–40% of remainder
Redis cache	~10–15% of remainder
Total	~84–89%

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Analysis by @Chen (RepoOps), Codex-reviewed. All code references verified against current main.

[beastoin]

Deep Code Analysis — 4-Change Optimization (Codex-reviewed)

Traced the full call chain from the listen WebSocket endpoint through translation. Found 5 root causes, proposing 4 changes with estimated 84–89% cost reduction.

Root Causes Verified in Code

#	Root Cause	Location	Impact
1	Double-paying for detection — explicit detect_language() API + translate auto-detects for free	translation.py:191 + translation.py:298 (no source_language_code set)	~43% of total spend is redundant detection
2	Re-translating growing segments 5–10x — 0.6s loop re-sends full segment text as Deepgram adds words	transcribe.py:1414,1508	Same chars billed 5–10x per segment
3	No persistent cache — in-memory only, per-session, max 1000 items	translation.py:250 (per-instance OrderedDict)	Zero cross-user/cross-session reuse
4	Per-sentence API calls — comma-based splitting creates 3–5x call multiplication	translation.py:244,269	Tiny fragments, worse translation quality
5	≤5 word threshold sends short text to paid detection	translation.py:220	Short utterances always hit paid API

Key Finding: Detection API is Pure Waste

translate_text() at line 298 sends no source_language_code. Per Google's pricing:

"If you don't specify a source language, Cloud Translation detects the source language for you with no additional charge."

The response includes detected_language_code (currently ignored at line 305). Detection and translation are both $20/M chars — so every foreign-language segment is billed 2x (detect + translate) when translate alone suffices.

Proposed Solution (4 Changes)

Change 1: Eliminate explicit detect_language API

• Remove _client.detect_language() calls entirely
• Use response.translations[0].detected_language_code from translate response (free)
• Replace brittle translated_text == segment_text check (line 1155) with detected_language_code == target_language — Codex flagged that Translate normalizes punctuation/spacing even for same-language text
• Estimated savings: ~43% of total spend

Change 2: Per-segment debounce queue (dedup growing segments)

• Translate immediately on first segment appearance (zero UX delay)
• Debounce updates: 0.8–1.2s trailing window before re-translating
• If STT marks segment as "final," translate immediately (skip debounce)
• Pattern (Codex-recommended): per-segment tracking map, not asyncio queue:

  pending[segment_id] = {last_text, last_hash, last_update_at, task}
  # On update: cancel/reschedule debounce task
  # On fire: translate only if last_hash unchanged since task creation

• Track segment_version to prevent stale out-of-order responses from overwriting newer translations
• Estimated savings: 60–80% of translation chars

Change 3: Sentence-level Redis cache

• Key: translate:v1:{md5(sentence)}:{dest_lang} → translated text
• TTL: 7–30 days + Redis LRU eviction (volatile-ttl)
• Sentence granularity provides best cache hits:
  • Intra-session: early sentences of growing segments hit cache on subsequent ticks
  • Cross-user: common phrases cached globally
• Include detected_language_code in cache value to avoid ambiguity on short text (Codex: "pan", "no", "gift" can auto-detect differently)
• Prefix keys with version (v1:) for future compatibility
• Estimated savings: additional 10–15% on remaining chars

Change 4: Fix sentence splitting + batch API calls

• Remove comma-based splitting — commas create tiny ambiguous fragments with worse translation quality and cache churn
• Split on .?! and newlines only, with length-based fallback (max 200–300 chars per chunk)
• Batch all cache-miss sentences into one contents=[miss1, miss2, ...] call — API already supports lists at line 299 but code only sends 1
• Respect API per-request limits (chunk if needed)
• Impact: overhead/latency reduction, better translation quality

Correctness Guardrails (from Codex review)

1. Use detected_language_code == target_language instead of translated_text == segment_text — Translate normalizes punctuation/spacing/quotes even for same-language text
2. Track segment version to prevent out-of-order responses from overwriting newer translations
3. Version cache keys (translate:v1:...) for future compatibility if splitting strategy or model changes
4. Mixed-language segments: auto-detect picks dominant language — pre-existing limitation, unchanged by this optimization

Files to Modify

File	Changes
backend/utils/translation.py	Remove detect_language API calls, add Redis cache, fix sentence splitting, add batching
backend/utils/translation_cache.py	Simplify or remove (detection no longer needed)
backend/routers/transcribe.py	Add debounce queue in translate() function, use detected_language_code
backend/database/redis_db.py	Add translation cache helpers (follows existing cache_signed_url pattern)

Combined Impact

Change	Reduction
Eliminate detection	~43%
Dedup queue	~35–40% of remainder
Redis cache	~10–15% of remainder
Total	~84–89%

---

Analysis by @Chen (RepoOps) with Codex review. Traced from transcribe.py:1508 → translation_cache.py:26 → translation.py:191,298. All code references verified.

[beastoin]

Sub-Issues (implement in order)

#	Issue	Change	Priority	Impact
1	#4712	Eliminate redundant detect_language API	P1	~43% reduction
2	#4713	Debounce translation for growing segments	P1	60–80% of remaining chars
3	#4714	Add Redis persistent translation cache	P1	10–15% of remaining chars
4	#4715	Fix sentence splitting + batch API calls	P2	Overhead/quality improvement

Order matters: #4712 first (largest impact, changes translate response shape), then #4713 (uses new response), then #4714 (adds cache layer), then #4715 (uses cache in batching).