f197177ae1
- 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)
40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
import torch
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from transformers import AutoTokenizer, AutoModelForMaskedLM
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import sys
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model_id = 'bert-base-uncased'
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = None
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def get_bert_feature(text, word2ph, device=None):
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global model
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if (
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sys.platform == "darwin"
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and torch.backends.mps.is_available()
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and device == "cpu"
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):
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device = "mps"
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if not device:
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device = "cuda"
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if model is None:
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model = AutoModelForMaskedLM.from_pretrained(model_id).to(
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device
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)
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device)
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res = model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
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assert inputs["input_ids"].shape[-1] == len(word2ph)
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word2phone = word2ph
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phone_level_feature = []
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for i in range(len(word2phone)):
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repeat_feature = res[i].repeat(word2phone[i], 1)
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phone_level_feature.append(repeat_feature)
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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return phone_level_feature.T
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