Game-Translator
Metaphor Refantazio Subtitle
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LOCALIZATION MOD
WATERMARKED vExperimental-2 Austronesian Lang

Metaphor Refantazio Subtitle Metaphor Refantazio Subtitle

Bahasa Indonesia, Melayu, Filipino

Memuat data interpretasi naratif secara real-time...

Product Narrative

The Full Story

Metaphor: ReFantazio adalah RPG epik bertema steampunk dari kreator Persona di mana kamu harus keliling kerajaan Euchronia demi menangin pemilu sihir dan nyelamatin Pangeran yang dikutuk. Dunianya gila banget, ada robot kapal darat Gauntlet Runner sampe monster ngeri bernama Human!


Kenapa wajib install mod ini? Bro, ada 841.860 kata yang kami terjemahin pake neural pipeline 8 tahap biar nggak kaku kayak robot! Kami nggak bohong, ini hasil kerja keras buat mastiin slang lokalnya dapet, dari celetukan Gallica yang asik sampe wibawa para ksatria yang kerasa banget Indonesianya. Kami juga udah fix 322 error tag bawaan gamenya biar mainmu lancar jaya. JRPG se-keren ini masa dimainin pake bahasa Inggris yang kering? Sikat modnya sekarang, mumpung gratis dan full power!

Current Milestone

Experimental Build

Author's Notes

I think Bahasa Indonesia is good enough. BUT some lines of Melayu and Filipino are blank. I just know it from clustering. So maybe report it in the Nexus Mods if you find more blank lines, and one more. THIS MOD WILL GIVE YOU RANDOM CRASH.


=== Audit Teknis & Semantik Lokalisasi METAPHOR REFANTAZIO ===

1. SKALA LINGUISTIK & CAKUPAN

- Skala Proyek: Sekitar 841,860 kata diproses melalui alur neural 8-tahap.

- Cakupan Bahasa: Dukungan trilingual penuh untuk pasar Indonesia, Malaysia, dan Filipina.

- Status Kelengkapan: Indonesia: 98.2%, Malay: 98.3%, Filipino: 98.0%

- Analisis Variasi Leksikal: Source -> Density: 62.0% | Diversity: 2.2%, Indonesia -> Density: 68.5% | Diversity: 2.8%, Malay -> Density: 69.2% | Diversity: 2.1%, Filipino -> Density: 58.5% | Diversity: 2.8%


2. VALIDASI NEURAL & AKURASI

- Skor Keselarasan Semantik (Platt Score): Indonesia: 89%, Malay: 87%, Filipino: 87%

(Skor ini mengukur seberapa akurat terjemahan mempertahankan makna asli dari teks sumber.)

- Gaya Bahasa Karakter: Penyesuaian gaya (gaul, formal, santai) telah diterapkan pada 1329 karakter unik.

- Pemulihan Struktur Otomatis (Tag Repair): 322 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 2.5% watermarks. Support this project on Trakteer or Ko-fi to download NON-WATERMARKED version.

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
37.5%
Standard
20.6%
Formal
41.9%
Emotional Spectrum

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

Positive/Warm
27.0%
Neutral/Functional
25.8%
Stoic/Restrained
20.6%
Complex/Ambivalent
13.4%
Negative/Intense
13.2%
Archetypes
30 detected
Gallica
14.5%
Strohl
6.7%
Hulkenberg
5.9%
Protagonist
4.7%
Junah
4.4%
Eupha
4.4%
Local Chatter
4.1%
Heismay
4.0%
Protagonist (normal)
3.7%
Basilio
3.4%
More
3.1%
(overhead Display)
3.1%
Neuras
2.6%
Grius
2.3%
Brigitta
2.3%
Ui
2.3%
Alonzo
2.1%
Louis
2.1%
Bardon
1.8%
Npc
1.6%
Fabienne
1.3%
Maria
1.2%
Catherina
1.0%
Fidelio
0.7%
Recruiter Dispatcher
0.6%
Batlin
0.6%
Mysterious Voice
0.4%
Overarching Summary
0.3%
System
0.3%
Seasoned Man
0.3%

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
58,768 / 59,866 lines
98%
Semantic Sim.
89 %
Lex. Density
68.5 %
src
62.0%
Lex. Diversity
2.8 %
src
2.2%
MS
Malay
58,840 / 59,866 lines
98%
Semantic Sim.
87 %
Lex. Density
69.2 %
src
62.0%
Lex. Diversity
2.1 %
src
2.2%
TL
Tagalog
58,653 / 59,866 lines
98%
Semantic Sim.
87 %
Lex. Density
58.5 %
src
62.0%
Lex. Diversity
2.8 %
src
2.2%

* 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
175,869 Token Lines
Src Density
62.0%
Src Diversity
2.2%
Syntactic Error Report

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

322
Mismatch
322
Fixed
0
Partial

Name

Label
Retrieving Portrait...
Narrative Profile

Associated Entities
Semantic Archetypes

NLP Pipeline Intelligence

Video Logs

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) 2026-04-04 08:40
Tag Repair (S6) 2026-03-19 17:59
Validator (S5) 2026-03-19 17:30
Corrector (S3) 2026-03-19 15:49
Re-Import (S4) 2026-03-19 15:49
Translator (S2) 2026-03-19 14:13
Splitter (S0) 2026-03-19 08:08
Tagger (S1) 2026-03-18 18:53

Released Archive

Austronesian Showcase

Location
Image
Video