Game-Translator
House Party
LOCALIZATION MOD
WATERMARKED valpha-5 Austronesian Lang

House Party

House Party Subtitle

Get ready to experience the wild, comedic, and chaotic world of Madison's house party like never before! Dive into a sandbox adventure of endless choice, hilarious interactions, and deep character storylines as you navigate the social landscape of the house. From engaging in deep...

Product Narrative

The Full Story

Sudah siap buat ngerasain keseruan pesta liar Madison yang penuh drama, komedi, dan pilihan gokil? Game petualangan sosial ini menantang kamu untuk berbaur dengan para tamu, memecahkan teka-teki konyol, dan menentukan sendiri jalur romansa atau kejahilanmu di dalam rumah. Mulai dari meloloskan diri dari penjagaan Frank yang galak sampai seru-seruan bareng Derek, setiap interaksi bakal menguji seberapa adaptif kemampuan sosialmu. Kenapa harus puas dengan teks bahasa Inggris yang kaku kalau kamu bisa menikmatinya pake bahasa gaul tongkrongan? Kami begadang demi menggarap lokalisasi raksasa 476.035 kata ini lewat pipeline AI neural 8-tahap yang menghasilkan terjemahan super luwes untuk Bahasa Indonesia, Melayu, dan Filipino. Setiap karakter dapet register gaya bahasa unik mereka sendiri biar dialognya terasa hidup, alami, dan kocak tanpa menghilangkan esensi aslinya. Proyek ini masih berupa experimental alpha dengan watermark penguji di dalamnya. Yuk unduh sekarang, bantu kami koreksi kalimat yang masih kaku, dan dukung karya kami di Karyain!

Current Milestone

Experimental Build

Author's Notes

=== Audit Teknis & Semantik Lokalisasi HOUSE PARTY === 1. SKALA LINGUISTIK & CAKUPAN - Skala Proyek: Sekitar 476,035 kata diproses melalui alur neural 8-tahap. - Cakupan Bahasa: Dukungan trilingual penuh untuk pasar Indonesia, Malaysia, dan Filipina. - Status Build: Experimental Alpha — watermarked preview build. - Status Kelengkapan: Indonesia: 99.2%, Malay: 99.2%, Filipino: 98.2% - Analisis Variasi Leksikal: Source -> Density: 61.9% | Diversity: 3.0%, Indonesia -> Density: 72.1% | Diversity: 4.2%, Malay -> Density: 72.0% | Diversity: 3.1%, Filipino -> Density: 60.8% | Diversity: 4.5% 2. VALIDASI NEURAL & AKURASI - Skor Keselarasan Semantik (Platt Score): (Skor ini mengukur seberapa akurat terjemahan mempertahankan makna asli dari teks sumber.) - Gaya Bahasa Karakter: Penyesuaian gaya (gaul, formal, santai) telah diterapkan pada 27 karakter unik. - Pemulihan Struktur Otomatis (Tag Repair): 56 tag kode game telah dipulihkan secara presisi. 3. KAPABILITAS ENGINE - Pipeline: Austronesian Localization System (Neural LoRA-Adaptive Architecture). - Pengenalan Entitas: Ekstraksi penuh untuk terminologi spesifik game dan konstanta lore.

Attention: This version contains 1.3% watermarks. Support this project on Trakteer or Ko-fi to download NON-WATERMARKED version.

Comments

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Linguistic Analysis Report

Stylometric Register Analysis

Discourse analysis using Gemma embeddings. Classifies rhetorical register across the corpus to ensure tonal consistency with source narrative assets.

Casual
89.1%
Standard
7.6%
Formal
3.4%
Emotional Spectrum

Emotional tone mapped via dot-product similarity between extracted dialog embeddings and predefined sentiment anchors using zero-shot semantic alignment.

Complex/Ambivalent
24.8%
Negative/Intense
21.2%
Neutral/Functional
20.4%
Positive/Warm
17.9%
Stoic/Restrained
15.7%
Archetypes
27 detected
Player
39.3%
Derek
6.2%
Madison
5.1%
Frank
4.9%
Leah
4.4%
Patrick
4.4%
Ashley
3.8%
Ui/system
3.6%
Vickie
3.0%
Katherine
2.8%
Brittney
2.5%
Rachael
2.4%
Gisella
2.3%
Amy
2.2%
Lety
2.1%
Stephanie
2.0%
Liz Katz
2.0%
Arin
1.6%
Dan
1.5%
Compubrah
1.4%
Amala
1.0%
Babs
0.5%
Doja Cat
0.4%
Murray
0.2%
Podcast
0.2%
Tater
0.1%
Phone Call
0.1%

DISCLOSURE: Profiling data generated algorithmically via zero-shot inference and semantic vector alignment. Represents AI interpretation of the dataset corpus, not explicit ground-truth statistics from the underlying game engine or internal metrics. Use as a heuristic guide for context mapping.

Cross-Lingual Quality Matrix

Semantic alignment quantified via Multilingual E5 Large Instruct (RoBERTa based) bitext mining. NER entities preserved using GLiNER heuristic extraction protocols to maintain terminological invariance.

ID
Indonesian
22,025 / 22,208 lines
99%
Semantic Sim.
83 %
Lex. Density
72.1 %
src
61.9%
Lex. Diversity
4.2 %
src
3.0%
MS
Malay
22,022 / 22,208 lines
99%
Semantic Sim.
83 %
Lex. Density
72.0 %
src
61.9%
Lex. Diversity
3.1 %
src
3.0%
TL
Tagalog
21,805 / 22,208 lines
98%
Semantic Sim.
82 %
Lex. Density
60.8 %
src
61.9%
Lex. Diversity
4.5 %
src
3.0%

* Sim = Cosine Similarity (Vector Space) · Density = Content/Total Tokens · Diversity = TTR (Type-Token Ratio) · "src" = Source Baseline · Named Entities enforced via GLiNER mining.

Corpus Volume & Metrics
65,775 Token Lines
Src Density
61.9%
Src Diversity
3.0%
Syntactic Error Report

Heuristic markup verification utilizing multi-pass validation and correction to ensure syntactical integrity of control codes and visual tags.

56
Mismatch
56
Fixed
0
Partial

Name

Label
Retrieving Portrait...
Narrative Profile

Associated Entities
Semantic Archetypes

NLP Pipeline Intelligence

Featured Preview Auto-Detected

Line Identity 0
Source (English)
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Indonesian (ID)
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Malay (MS)
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Tagalog (TL)
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Pipeline Receipts

Merger (S7) 2026-09-24 16:50
Tag Repair (S6) 2026-09-24 15:43
Re-Import (S4) 2026-09-24 15:10
Corrector (S3) 2026-09-24 15:05
Translator (S2) 2026-09-24 14:06
Tagger (S1) 2026-09-23 21:03
Splitter (S0) 2026-09-23 18:55
Validator (S5) 2026-03-24 03:50
Validator (S5) 2026-02-21 10:01
Re-Import (S4) 2026-02-21 04:45
Tagger (S1) 2026-02-20 21:40

Released Archive

Austronesian Showcase

Location
Image
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