23-11-2025, 11:30 AM
First of all: This is NOT a serious solution for the VMS! Since I have seen many “solutions” with pseudo-Latin lately, I just want to demonstrate how easy it is to replicate this with any word list. Anyone can reproduce the procedure described below. You may only need to install Metaphone for Python:
pip install Metaphone
First, you can map the most common letters in VMS to the most common letters in the Latin alphabet. This step isn't really necessary, but it's “nicer”.
cat RF1a-n-x7.txt | tr oehyacdiklrstnqpmfgxzv ieutasrnmocdlpbqghfxyz > voyn2latin.txt
Now you create a word list from any Latin text. I took an excerpt from Regimen Sanitatis. 5388 Latin words were extracted. That is definitely sufficient.
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Finally, run the following Python script. This will take a few minutes. The script checks the Levenshtein distance and the phonetic similarity of the words in voyn2latin.txt against the word list. The words found are inserted accordingly and written to the output.
python phonetic_levenshtein.py voyn2latin.txt mapped.txt
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If you want to do this to yourself, you can run mapped.txt through a translator. The result is just as meaningless as most of the Latin “solutions” I've seen. If you actually find a meaningful sentence, you can keep it
pip install Metaphone
First, you can map the most common letters in VMS to the most common letters in the Latin alphabet. This step isn't really necessary, but it's “nicer”.
cat RF1a-n-x7.txt | tr oehyacdiklrstnqpmfgxzv ieutasrnmocdlpbqghfxyz > voyn2latin.txt
Now you create a word list from any Latin text. I took an excerpt from Regimen Sanitatis. 5388 Latin words were extracted. That is definitely sufficient.
You are not allowed to view links. Register or Login to view.
Finally, run the following Python script. This will take a few minutes. The script checks the Levenshtein distance and the phonetic similarity of the words in voyn2latin.txt against the word list. The words found are inserted accordingly and written to the output.
python phonetic_levenshtein.py voyn2latin.txt mapped.txt
Code:
#!/usr/bin/env python3
# coding: utf-8
"""
DoubleMetaphone + Levenshtein Hybrid Decoder
--------------------------------------------------------
1. Load Latin words from wordlist.txt
2. Build a DoubleMetaphone index
3. For each Voynich word:
- Compute its DoubleMetaphone codes
- Find matching or phonetically similar Latin words
- Rank candidates using Levenshtein distance
- Output the best candidate (or <no match>)
"""
import sys
from metaphone import doublemetaphone
from collections import defaultdict
from tqdm import tqdm
# ---------------------------------------------------------
# Levenshtein distance
# ---------------------------------------------------------
def levenshtein(a, b):
if not a:
return len(b)
if not b:
return len(a)
dp = range(len(b) + 1)
for i, ca in enumerate(a, 1):
new_dp = [i]
for j, cb in enumerate(b, 1):
if ca == cb:
new_dp.append(dp[j-1])
else:
new_dp.append(1 + min(dp[j-1], dp[j], new_dp[-1]))
dp = new_dp
return dp[-1]
# ---------------------------------------------------------
# Load Latin words
# ---------------------------------------------------------
def load_wordlist(path="wordlist.txt"):
words = []
with open(path, "r", encoding="utf8") as f:
for line in f:
word = line.strip().lower()
if word:
words.append(word)
return words
# ---------------------------------------------------------
# Normalize a Voynich word (remove non-letters)
# ---------------------------------------------------------
def normalize_voynich_word(w):
return "".join(c.lower() for c in w if c.isalpha())
# ---------------------------------------------------------
# Build Metaphone index
# ---------------------------------------------------------
def build_metaphone_index(words):
index = defaultdict(list)
for w in words:
m1, m2 = doublemetaphone(w)
if m1:
index[m1].append(w)
if m2 and m2 != m1:
index[m2].append(w)
return index
# ---------------------------------------------------------
# Combined DoubleMetaphone + Levenshtein matching
# ---------------------------------------------------------
def hybrid_match(v_word, index, top_n=1):
if not v_word:
return ["<no match>"]
m1, m2 = doublemetaphone(v_word)
candidates = []
# 1) Exact metaphone matches
if m1 in index:
candidates.extend(index[m1])
if m2 in index:
candidates.extend(index[m2])
# 2) If too few candidates, try similar metaphone keys
if len(candidates) < 5 and m1:
prefix = m1[:2]
for key in index:
if key.startswith(prefix):
candidates.extend(index[key])
# 3) Still nothing? → no match
if not candidates:
return ["<no match>"]
# 4) Compute Levenshtein distances
scored = []
for cand in candidates:
score = levenshtein(v_word, cand[:len(v_word)])
scored.append((score, cand))
scored.sort(key=lambda x: x[0])
best_score = scored[0][0]
# All equally good candidates
best = [w for s, w in scored if s == best_score]
return best[:top_n]
# ---------------------------------------------------------
# MAIN
# ---------------------------------------------------------
def main():
if len(sys.argv) < 3:
print("Usage: python3 phonetic_levenshtein.py voyn2latin.txt output.txt")
sys.exit(1)
infile = sys.argv[1]
outfile = sys.argv[2]
print("Loading Latin wordlist …")
latin_words = load_wordlist("wordlist.txt")
print(f"{len(latin_words)} Latin words loaded.")
print("Building DoubleMetaphone index …")
index = build_metaphone_index(latin_words)
print(f"Index contains {len(index)} metaphone keys.")
print("Decoding …")
with open(infile, "r", encoding="utf8") as f:
lines = f.readlines()
output_lines = []
for line in tqdm(lines, desc="Lines"):
if not line.strip():
output_lines.append("")
continue
words = line.split()
decoded_line = []
for w in words:
normalized = normalize_voynich_word(w)
matches = hybrid_match(normalized, index, top_n=1)
decoded_line.append(matches[0])
output_lines.append(" ".join(decoded_line))
with open(outfile, "w", encoding="utf8") as f:
for row in output_lines:
f.write(row + "\n")
print("Done →", outfile)
if __name__ == "__main__":
main()You are not allowed to view links. Register or Login to view.
If you want to do this to yourself, you can run mapped.txt through a translator. The result is just as meaningless as most of the Latin “solutions” I've seen. If you actually find a meaningful sentence, you can keep it
