Game-Translator
Silent Hill Townfall Mod
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LOCALIZATION MOD
WATERMARKED valpha-1 Austronesian Lang

Silent Hill Townfall Mod Silent Hill Townfall Mod

Bahasa Indonesia, Melayu, Filipino

Bahasa Indonesia, Melayu, Filipino

Product Narrative

The Full Story

Silent Hill: Townfall membawa kamu ke dalam teror psikologis yang kelam di kota pesisir St. Amelia yang dipenuhi kabut tebal. Kamu akan berperan sebagai Simon Ordell, seorang penyintas amnesia yang tersiksa oleh rasa bersalah dan berusaha menguak rahasia mengerikan di balik kecelakaan anjungan minyak SWDP milik Chapman Energy Group. Di tengah kabut tebal dan gangguan sinyal radio misterius, setiap langkahmu dipenuhi bayang-bayang masa lalu yang menanti untuk dipecahkan. Daripada kamu pusing dengerin istilah teknis pas nyelarasin sinyal CRTV, mending cobain mod lokalisasi super niat ini! Garapan gila ini pakai teknologi neural pipeline 8 tahap demi menghasilkan terjemahan super luwes dengan slang lokal yang asyik dan pembagian gaya bahasa unik buat tiap karakter, menerjemahkan total 24.927 kata! Karena ini masih versi alpha eksperimental yang punya watermark uji coba, yuk download sekarang, nikmati atmosfer horornya, dan kabari kalau ada kalimat yang terasa janggal biar langsung saya benerin di patch selanjutnya!

Current Milestone

Experimental Build

Author's Notes

=== Audit Teknis & Semantik Lokalisasi SILENT HILL TOWNFALL === 1. SKALA LINGUISTIK & CAKUPAN - Skala Proyek: Sekitar 24,927 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: 90.6%, Malay: 90.8%, Filipino: 89.1% - Analisis Variasi Leksikal: Source -> Density: 67.8% | Diversity: 14.9%, Indonesia -> Density: 72.7% | Diversity: 18.9%, Malay -> Density: 75.7% | Diversity: 16.6%, Filipino -> Density: 64.9% | Diversity: 16.7% 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 48 karakter unik. - Pemulihan Struktur Otomatis (Tag Repair): 9 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 2.9% 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
45.5%
Standard
52.0%
Formal
2.5%
Emotional Spectrum

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

Neutral/Functional
45.6%
Stoic/Restrained
26.6%
Negative/Intense
9.8%
Complex/Ambivalent
9.5%
Positive/Warm
8.6%
Archetypes
30 detected
Ui/system
46.9%
Ambient Character
14.6%
Simon
9.3%
Zoe
8.0%
Richard
6.6%
Dr. Glenn
4.2%
Dr Glenn (automated Line)
1.2%
Unknown Voice Richard
0.8%
Dog
0.7%
Joe Mckenzie
0.6%
Exhibit Voice
0.6%
Douglas
0.6%
Protest Leader
0.6%
Automated Voice
0.5%
Scott
0.4%
Receptionist
0.4%
Swdp Boss
0.3%
Answer Machine
0.3%
Brian Morris
0.3%
Unknown Voice Zoe
0.3%
Interviewer 1
0.3%
Mrs. Mckenzie
0.2%
Worker 2
0.2%
Manager
0.2%
Automated Phone Service Voice
0.2%
Unknown Female
0.2%
Mary
0.2%
Hotel Manager
0.2%
Jessica White
0.1%
Radio Host
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
1,728 / 1,907 lines
91%
Lex. Density
72.7 %
src
67.8%
Lex. Diversity
18.9 %
src
14.9%
MS
Malay
1,732 / 1,907 lines
91%
Lex. Density
75.7 %
src
67.8%
Lex. Diversity
16.6 %
src
14.9%
TL
Tagalog
1,699 / 1,907 lines
89%
Lex. Density
64.9 %
src
67.8%
Lex. Diversity
16.7 %
src
14.9%

* 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
5,601 Token Lines
Src Density
67.8%
Src Diversity
14.9%
Syntactic Error Report

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

9
Mismatch
9
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) 2026-09-25 13:40
Tag Repair (S6) 2026-09-25 09:47
Re-Import (S4) 2026-09-25 09:41
Corrector (S3) 2026-09-25 09:38
Translator (S2) 2026-09-25 09:24
Tagger (S1) 2026-09-25 08:28
Splitter (S0) 2026-09-25 01:43

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