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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) | |
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" | |
) | |