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from flask import Flask, request, Response
from io import BytesIO
import torch
from av import open as avopen
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
from scipy.io import wavfile
# Flask Init
app = Flask(__name__)
app.config['JSON_AS_ASCII'] = False
def get_text(text, language_str, hps):
norm_text, phone, tone, word2ph = clean_text(text, language_str)
print([f"{p}{t}" for p, t in zip(phone, tone)])
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)
assert bert.shape[-1] == len(phone)
phone = torch.LongTensor(phone)
tone = torch.LongTensor(tone)
language = torch.LongTensor(language)
return bert, phone, tone, language
def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
with torch.no_grad():
x_tst=phones.to(dev).unsqueeze(0)
tones=tones.to(dev).unsqueeze(0)
lang_ids=lang_ids.to(dev).unsqueeze(0)
bert = bert.to(dev).unsqueeze(0)
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,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()
return audio
def replace_punctuation(text, i=2):
punctuation = "，。？！"
for char in punctuation:
text = text.replace(char, char * i)
return text
def wav2(i, o, format):
inp = avopen(i, 'rb')
out = avopen(o, 'wb', format=format)
if format == "ogg": format = "libvorbis"
ostream = out.add_stream(format)
for frame in inp.decode(audio=0):
for p in ostream.encode(frame): out.mux(p)
for p in ostream.encode(None): out.mux(p)
out.close()
inp.close()
# Load Generator
hps = utils.get_hparams_from_file("./configs/config.json")
dev='cuda'
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(dev)
_ = net_g.eval()
_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
@app.route("/",methods=['GET','POST'])
def main():
if request.method == 'GET':
try:
speaker = request.args.get('speaker')
text = request.args.get('text').replace("/n","")
sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
noise = float(request.args.get("noise", 0.5))
noisew = float(request.args.get("noisew", 0.6))
length = float(request.args.get("length", 1.2))
if length >= 2:
return "Too big length"
if len(text) >=200:
return "Too long text"
fmt = request.args.get("format", "wav")
if None in (speaker, text):
return "Missing Parameter"
if fmt not in ("mp3", "wav", "ogg"):
return "Invalid Format"
except:
return "Invalid Parameter"
with torch.no_grad():
audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
with BytesIO() as wav:
wavfile.write(wav, hps.data.sampling_rate, audio)
torch.cuda.empty_cache()
if fmt == "wav":
return Response(wav.getvalue(), mimetype="audio/wav")
wav.seek(0, 0)
with BytesIO() as ofp:
wav2(wav, ofp, fmt)
return Response(
ofp.getvalue(),
mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
)
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