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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)
|