한국어 우선 MeloTTS 웹 서비스 추가 (GPU, 속도/피치, 로그인 없음)

- FastAPI 백엔드: /api/voices, /api/tts, /api/health
- 한국어 메인 + 영어(5종 악센트), 중국어/일본어 UI 제거
- 언어별 목소리 모델 선택, 속도(0.5~2.0x)/피치(±12반음) 조절
- 로그인/사용제한/과금 유도 없음 (무제한 테스트)
- NVIDIA GPU 자동 사용 (torch cu128, Blackwell sm_120)
- 유저 친화 한국어 UI (frontend)
- Docker/compose (GPU 예약), 8788 포트
- MeloTTS(순수 파이썬) 벤더링 + 검증된 버전 고정(constraints)

vendor/melo/text/spanish.py

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+import pickle
+import os
+import re
+
+from . import symbols
+from .es_phonemizer import cleaner as es_cleaner
+from .es_phonemizer import es_to_ipa
+from transformers import AutoTokenizer
+
+
+def distribute_phone(n_phone, n_word):
+    phones_per_word = [0] * n_word
+    for task in range(n_phone):
+        min_tasks = min(phones_per_word)
+        min_index = phones_per_word.index(min_tasks)
+        phones_per_word[min_index] += 1
+    return phones_per_word
+
+def text_normalize(text):
+    text = es_cleaner.spanish_cleaners(text)
+    return text
+
+def post_replace_ph(ph):
+    rep_map = {
+        "：": ",",
+        "；": ",",
+        "，": ",",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+        "\n": ".",
+        "·": ",",
+        "、": ",",
+        "...": "…"
+    }
+    if ph in rep_map.keys():
+        ph = rep_map[ph]
+    if ph in symbols:
+        return ph
+    if ph not in symbols:
+        ph = "UNK"
+    return ph
+
+def refine_ph(phn):
+    tone = 0
+    if re.search(r"\d$", phn):
+        tone = int(phn[-1]) + 1
+        phn = phn[:-1]
+    return phn.lower(), tone
+
+
+def refine_syllables(syllables):
+    tones = []
+    phonemes = []
+    for phn_list in syllables:
+        for i in range(len(phn_list)):
+            phn = phn_list[i]
+            phn, tone = refine_ph(phn)
+            phonemes.append(phn)
+            tones.append(tone)
+    return phonemes, tones
+
+
+# model_id = 'bert-base-uncased'
+model_id = 'dccuchile/bert-base-spanish-wwm-uncased'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+def g2p(text, pad_start_end=True, tokenized=None):
+    if tokenized is None:
+        tokenized = tokenizer.tokenize(text)
+    # import pdb; pdb.set_trace()
+    phs = []
+    ph_groups = []
+    for t in tokenized:
+        if not t.startswith("#"):
+            ph_groups.append([t])
+        else:
+            ph_groups[-1].append(t.replace("#", ""))
+    
+    phones = []
+    tones = []
+    word2ph = []
+    # print(ph_groups)
+    for group in ph_groups:
+        w = "".join(group)
+        phone_len = 0
+        word_len = len(group)
+        if w == '[UNK]':
+            phone_list = ['UNK']
+        else:
+            phone_list = list(filter(lambda p: p != " ", es_to_ipa.es2ipa(w)))
+        
+        for ph in phone_list:
+            phones.append(ph)
+            tones.append(0)
+            phone_len += 1
+        aaa = distribute_phone(phone_len, word_len)
+        word2ph += aaa
+        # print(phone_list, aaa)
+        # print('=' * 10)
+
+    if pad_start_end:
+        phones = ["_"] + phones + ["_"]
+        tones = [0] + tones + [0]
+        word2ph = [1] + word2ph + [1]
+    return phones, tones, word2ph
+
+def get_bert_feature(text, word2ph, device=None):
+    from text import spanish_bert
+    return spanish_bert.get_bert_feature(text, word2ph, device=device)
+
+if __name__ == "__main__":
+    text = "en nuestros tiempos estos dos pueblos ilustres empiezan a curarse, gracias sólo a la sana y vigorosa higiene de 1789."
+    # print(text)
+    text = text_normalize(text)
+    print(text)
+    phones, tones, word2ph = g2p(text)
+    bert = get_bert_feature(text, word2ph)
+    print(phones)
+    print(len(phones), tones, sum(word2ph), bert.shape)
+
+