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import torch
import commons
import utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import text_to_sequence
import io
from scipy.io.wavfile import write
from flask import Flask, request
import threading
app = Flask(__name__)
mutex = threading.Lock()
def get_text(text, hps):
text_norm = text_to_sequence(text, hps.data.text_cleaners)
if hps.data.add_blank:
text_norm = commons.intersperse(text_norm, 0)
text_norm = torch.LongTensor(text_norm)
return text_norm
hps = utils.get_hparams_from_file("./configs/ljs_mb_istft_vits.json")
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model)
_ = net_g.eval()
# _ = utils.load_checkpoint("../tempbest.pth", net_g, None)
import time
def tts(txt):
audio = None
if mutex.acquire(blocking=False):
try:
stn_tst = get_text(txt, hps)
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
t1 = time.time()
audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8,
length_scale=1)[0][0, 0].data.float().numpy()
t2 = time.time()
print("推理时间：", (t2 - t1), "s")
finally:
mutex.release()
return audio
@app.route('/tts')
def text_api():
text = request.args.get('text','')
bytes_wav = bytes()
byte_io = io.BytesIO(bytes_wav)
audio = tts(text)
if audio is None:
return "服务器忙"
write(byte_io, 22050, audio)
wav_bytes = byte_io.read()
# audio_data = base64.b64encode(wav_bytes).decode('UTF-8')
return wav_bytes, 200, {'Content-Type': 'audio/wav'}
if __name__ == '__main__':
app.run("0.0.0.0", 8080)
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