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)
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claude
135
vendor/melo/api.py
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135
vendor/melo/api.py
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import os
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import re
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import json
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import torch
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import librosa
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import soundfile
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import torchaudio
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import numpy as np
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import torch.nn as nn
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from tqdm import tqdm
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import torch
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from . import utils
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from . import commons
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from .models import SynthesizerTrn
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from .split_utils import split_sentence
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from .mel_processing import spectrogram_torch, spectrogram_torch_conv
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from .download_utils import load_or_download_config, load_or_download_model
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class TTS(nn.Module):
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def __init__(self,
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language,
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device='auto',
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use_hf=True,
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config_path=None,
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ckpt_path=None):
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super().__init__()
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if device == 'auto':
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device = 'cpu'
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if torch.cuda.is_available(): device = 'cuda'
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if torch.backends.mps.is_available(): device = 'mps'
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if 'cuda' in device:
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assert torch.cuda.is_available()
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# config_path =
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hps = load_or_download_config(language, use_hf=use_hf, config_path=config_path)
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num_languages = hps.num_languages
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num_tones = hps.num_tones
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symbols = hps.symbols
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model = SynthesizerTrn(
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len(symbols),
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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num_tones=num_tones,
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num_languages=num_languages,
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**hps.model,
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).to(device)
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model.eval()
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self.model = model
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self.symbol_to_id = {s: i for i, s in enumerate(symbols)}
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self.hps = hps
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self.device = device
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# load state_dict
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checkpoint_dict = load_or_download_model(language, device, use_hf=use_hf, ckpt_path=ckpt_path)
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self.model.load_state_dict(checkpoint_dict['model'], strict=True)
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language = language.split('_')[0]
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self.language = 'ZH_MIX_EN' if language == 'ZH' else language # we support a ZH_MIX_EN model
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@staticmethod
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def audio_numpy_concat(segment_data_list, sr, speed=1.):
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audio_segments = []
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for segment_data in segment_data_list:
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audio_segments += segment_data.reshape(-1).tolist()
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audio_segments += [0] * int((sr * 0.05) / speed)
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audio_segments = np.array(audio_segments).astype(np.float32)
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return audio_segments
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@staticmethod
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def split_sentences_into_pieces(text, language, quiet=False):
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texts = split_sentence(text, language_str=language)
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if not quiet:
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print(" > Text split to sentences.")
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print('\n'.join(texts))
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print(" > ===========================")
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return texts
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def tts_to_file(self, text, speaker_id, output_path=None, sdp_ratio=0.2, noise_scale=0.6, noise_scale_w=0.8, speed=1.0, pbar=None, format=None, position=None, quiet=False,):
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language = self.language
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texts = self.split_sentences_into_pieces(text, language, quiet)
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audio_list = []
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if pbar:
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tx = pbar(texts)
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else:
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if position:
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tx = tqdm(texts, position=position)
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elif quiet:
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tx = texts
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else:
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tx = tqdm(texts)
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for t in tx:
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if language in ['EN', 'ZH_MIX_EN']:
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t = re.sub(r'([a-z])([A-Z])', r'\1 \2', t)
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device = self.device
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bert, ja_bert, phones, tones, lang_ids = utils.get_text_for_tts_infer(t, language, self.hps, device, self.symbol_to_id)
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with torch.no_grad():
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x_tst = phones.to(device).unsqueeze(0)
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tones = tones.to(device).unsqueeze(0)
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lang_ids = lang_ids.to(device).unsqueeze(0)
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bert = bert.to(device).unsqueeze(0)
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ja_bert = ja_bert.to(device).unsqueeze(0)
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
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del phones
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speakers = torch.LongTensor([speaker_id]).to(device)
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audio = self.model.infer(
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x_tst,
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x_tst_lengths,
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speakers,
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tones,
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lang_ids,
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bert,
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ja_bert,
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sdp_ratio=sdp_ratio,
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noise_scale=noise_scale,
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noise_scale_w=noise_scale_w,
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length_scale=1. / speed,
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)[0][0, 0].data.cpu().float().numpy()
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del x_tst, tones, lang_ids, bert, ja_bert, x_tst_lengths, speakers
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#
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audio_list.append(audio)
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torch.cuda.empty_cache()
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audio = self.audio_numpy_concat(audio_list, sr=self.hps.data.sampling_rate, speed=speed)
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if output_path is None:
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return audio
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else:
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if format:
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soundfile.write(output_path, audio, self.hps.data.sampling_rate, format=format)
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else:
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soundfile.write(output_path, audio, self.hps.data.sampling_rate)
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