# -*- coding: utf-8 -*- """ Turkish Text Normalization (TN/ITN) dataset builder. Deterministic, rule-based generation of written<->spoken Turkish pairs. Reproducible: fixed seed. No external data sources. """ import csv, json, os, random SEED = 42 random.seed(SEED) ONES = ["", "bir","iki","üç","dört","beş","altı","yedi","sekiz","dokuz"] TENS = ["", "on","yirmi","otuz","kırk","elli","altmış","yetmiş","seksen","doksan"] SCALES = ["", "bin", "milyon", "milyar"] def three_digit(n): # 0..999 -> words out = [] h = n // 100 if h == 1: out.append("yüz") elif h > 1: out.append(ONES[h] + " yüz") t = (n % 100) // 10 if t: out.append(TENS[t]) o = n % 10 if o: out.append(ONES[o]) return " ".join(out) def num_to_tr(n): # 0..10^12-1 -> Turkish words (cardinal) if n == 0: return "sıfır" chunks = [] x = n while x > 0: chunks.append(x % 1000); x //= 1000 words = [] for i in range(len(chunks)-1, -1, -1): c = chunks[i] if c == 0: continue if i == 1 and c == 1: words.append("bin") # "bin", not "bir bin" else: words.append(three_digit(c)) if i > 0: words.append(SCALES[i]) return " ".join(words) ORDINAL_LAST = { "bir":"birinci","iki":"ikinci","üç":"üçüncü","dört":"dördüncü","beş":"beşinci", "altı":"altıncı","yedi":"yedinci","sekiz":"sekizinci","dokuz":"dokuzuncu", "on":"onuncu","yirmi":"yirminci","otuz":"otuzuncu","kırk":"kırkıncı","elli":"ellinci", "altmış":"altmışıncı","yetmiş":"yetmişinci","seksen":"sekseninci","doksan":"doksanıncı", "yüz":"yüzüncü","bin":"bininci","milyon":"milyonuncu","milyar":"milyarıncı", } def ordinal_tr(n): w = num_to_tr(n).split() w[-1] = ORDINAL_LAST[w[-1]] return " ".join(w) MONTHS = ["","ocak","şubat","mart","nisan","mayıs","haziran","temmuz","ağustos","eylül","ekim","kasım","aralık"] def dec_to_tr(int_part, frac_str): return num_to_tr(int_part) + " virgül " + " ".join(num_to_tr(int(d)) if d!="0" else "sıfır" for d in frac_str) \ if False else num_to_tr(int_part) + " virgül " + num_to_tr(int(frac_str)) if not frac_str.startswith("0") else \ num_to_tr(int_part) + " virgül " + " ".join("sıfır" if d=="0" else num_to_tr(int(d)) for d in frac_str) rows = [] def add(cat, written, spoken): spoken = " ".join(spoken.split()) rows.append((cat, written, spoken)) # 1) Cardinals seen=set() for _ in range(9000): r = random.random() if r < 0.5: n = random.randint(0, 100) elif r < 0.8: n = random.randint(101, 9999) elif r < 0.95: n = random.randint(10000, 999999) else: n = random.randint(10**6, 10**9) if n in seen: continue seen.add(n) add("cardinal", str(n), num_to_tr(n)) # 2) Ordinals seen=set() for _ in range(3500): n = random.choice([random.randint(1,100), random.randint(1,9999)]) if n in seen: continue seen.add(n) add("ordinal", f"{n}.", ordinal_tr(n)) # 3) Decimals seen=set() for _ in range(3500): ip = random.randint(0, 9999) fl = random.choice([1,1,2,2,2,3]) fr = "".join(random.choice("0123456789") for _ in range(fl)) key=(ip,fr) if key in seen: continue seen.add(key) frac_words = " ".join("sıfır" if d=="0" else num_to_tr(int(d)) for d in fr) if fr[0]=="0" else num_to_tr(int(fr)) add("decimal", f"{ip},{fr}", f"{num_to_tr(ip)} virgül {frac_words}") # 4) Percentages seen=set() for _ in range(2500): whole = random.random() < 0.7 if whole: v = random.randint(0,100); w=f"%{v}"; sp=f"yüzde {num_to_tr(v)}" else: ip=random.randint(0,100); fr=random.choice(["5","25","5","75","50"]); w=f"%{ip},{fr}" frac_words = num_to_tr(int(fr)) sp=f"yüzde {num_to_tr(ip)} virgül {frac_words}" if w in seen: continue seen.add(w); add("percentage", w, sp) # 5) Currency CUR = [("TL","lira"),("TL","türk lirası"),("$","dolar"),("€","avro"),("₺","lira")] seen=set() for _ in range(3200): n = random.choice([random.randint(1,999), random.randint(1,99999), random.randint(1,9999)]) sym,word = random.choice(CUR) if sym in ("$","€","₺"): w=f"{sym}{n}" else: w=f"{n} {sym}" key=(n,sym,word) if key in seen: continue seen.add(key); add("currency", w, f"{num_to_tr(n)} {word}") # 6) Dates DD.MM.YYYY seen=set() for _ in range(3200): d=random.randint(1,28); m=random.randint(1,12); y=random.randint(1900,2035) w=f"{d:02d}.{m:02d}.{y}" if w in seen: continue seen.add(w) add("date", w, f"{num_to_tr(d)} {MONTHS[m]} {num_to_tr(y)}") # 7) Times HH:MM seen=set() for _ in range(2200): h=random.randint(0,23); mn=random.randint(0,59) w=f"{h:02d}:{mn:02d}" if w in seen: continue seen.add(w) if mn==0: sp=f"saat {num_to_tr(h)}" elif mn==30 and random.random()<0.5: sp=f"saat {num_to_tr(h)} buçuk" else: sp=f"saat {num_to_tr(h)} {num_to_tr(mn)}" add("time", w, sp) # dedup globally on (written, spoken) uniq = {} for cat,w,s in rows: uniq[(w,s)] = (cat,w,s) data = list(uniq.values()) random.shuffle(data) # split 90/10 cut = int(len(data)*0.9) train, test = data[:cut], data[cut:] os.makedirs("data", exist_ok=True) def dump(path, part): with open(path,"w",encoding="utf-8",newline="") as f: wr=csv.writer(f); wr.writerow(["id","category","written","spoken"]) for i,(cat,w,s) in enumerate(part): wr.writerow([i,cat,w,s]) dump("data/train.csv", train) dump("data/test.csv", test) from collections import Counter cats = Counter(c for c,_,_ in data) stats = {"total": len(data), "train": len(train), "test": len(test), "by_category": dict(sorted(cats.items())), "seed": SEED} json.dump(stats, open("stats.json","w",encoding="utf-8"), ensure_ascii=False, indent=2) print(json.dumps(stats, ensure_ascii=False, indent=2)) print("\n--- ornekler ---") for cat,w,s in random.sample(data, 14): print(f"[{cat:11}] {w:16} -> {s}")