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| logger = logging.getLogger(__name__) import datetime import numpy as np import torch from ebooklib import epub import PyPDF2 from PyPDF2 import PdfReader import zipfile import shutil import sys, os import json from bs4 import BeautifulSoup import argparse import commons import utils from models import SynthesizerTrn from text.symbols import symbols from text import cleaned_text_to_sequence, get_bert from text.cleaner import clean_text import gradio as gr import webbrowser import re from scipy.io.wavfile import write
net_g = None
if sys.platform == "darwin" and torch.backends.mps.is_available(): device = "mps" os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" else: device = "cuda"
def is_japanese(string): for ch in string: if ord(ch) > 0x3040 and ord(ch) < 0x30FF: return True return False
def get_text(text, language_str, hps): norm_text, phone, tone, word2ph = clean_text(text, language_str) phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
if hps.data.add_blank: phone = commons.intersperse(phone, 0) tone = commons.intersperse(tone, 0) language = commons.intersperse(language, 0) for i in range(len(word2ph)): word2ph[i] = word2ph[i] * 2 word2ph[0] += 1 bert = get_bert(norm_text, word2ph, language_str, device) del word2ph assert bert.shape[-1] == len(phone), phone
if language_str == "ZH": bert = bert ja_bert = torch.zeros(768, len(phone)) elif language_str == "JA": ja_bert = bert bert = torch.zeros(1024, len(phone)) else: bert = torch.zeros(1024, len(phone)) ja_bert = torch.zeros(768, len(phone))
assert bert.shape[-1] == len( phone ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
phone = torch.LongTensor(phone) tone = torch.LongTensor(tone) language = torch.LongTensor(language) return bert, ja_bert, phone, tone, language
def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language): global net_g bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps) with torch.no_grad(): x_tst = phones.to(device).unsqueeze(0) tones = tones.to(device).unsqueeze(0) lang_ids = lang_ids.to(device).unsqueeze(0) bert = bert.to(device).unsqueeze(0) ja_bert = ja_bert.to(device).unsqueeze(0) x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) del phones speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) audio = ( net_g.infer( x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, ja_bert, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, )[0][0, 0] .data.cpu() .float() .numpy() ) del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers return audio
def tts_fn( text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language ): with torch.no_grad(): audio = infer( text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language= "JP" if is_japanese(text) else "ZH", ) torch.cuda.empty_cache() write("books/temp.wav", 44100, audio) return "Success"
app = Flask(__name__)
@app.route('/tts') def tts_api(): # 从请求中获取参数 text = request.args.get('text') speaker = request.args.get('speaker') sdp_ratio = float(request.args.get('sdp_ratio', 0.2)) noise_scale = float(request.args.get('noise_scale', 0.6)) noise_scale_w = float(request.args.get('noise_scale_w', 0.8)) length_scale = float(request.args.get('length_scale', 1)) status = tts_fn(text, speaker='つくし', sdp_ratio=0.2, noise_scale=0.6, noise_scale_w=0.8, length_scale=1) with open('books/temp.wav','rb') as bit: wav_bytes = bit.read() headers = { 'Content-Type': 'audio/wav', 'Text': status.encode('utf-8')} return wav_bytes, 200, headers
if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "-m", "--model", default="./logs/BangDream/G_47000.pth", help="path of your model" ) parser.add_argument( "-c", "--config", default="./logs/BangDream/config.json", help="path of your config file", ) parser.add_argument( "--share", default=True, help="make link public", action="store_true" ) parser.add_argument( "-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log" )
args = parser.parse_args() if args.debug: logger.info("Enable DEBUG-LEVEL log") logging.basicConfig(level=logging.DEBUG) hps = utils.get_hparams_from_file(args.config)
device = ( "cuda:0" if torch.cuda.is_available() else ( "mps" if sys.platform == "darwin" and torch.backends.mps.is_available() else "cpu" ) ) net_g = SynthesizerTrn( len(symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model, ).to(device) _ = net_g.eval()
_ = utils.load_checkpoint(args.model, net_g, None, skip_optimizer=True)
app.run(host="0.0.0.0", port=8080)
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