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df4eaf9 1547671 df4eaf9 10a9dd9 df4eaf9 6bac4ab 072773f 6bac4ab df4eaf9 db68367 df4eaf9 b7edbd8 df4eaf9 f6ecd7d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | from flask import Flask, jsonify, request, send_file
import sys
import re
import os
import torch
import utils
import commons
import soundfile as sf
from models import SynthesizerTrn
from text import text_to_sequence, _clean_text
from pyngrok import ngrok
parser = sys.argv
argsAuth = parser[1]
argsDevice = parser[2]
ngrok.set_auth_token(argsAuth)
public_url = ngrok.connect(5000).public_url
print("Monggo: " + public_url)
# limit text and audio length in huggingface spaces
limitation = os.getenv("SYSTEM") == "spaces"
hps_ms = utils.get_hparams_from_file(r'config/config.json')
def get_text(text, hps, is_symbol):
text_norm, clean_text = text_to_sequence(
text, hps.symbols, [] if is_symbol else 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, clean_text
def generate(textnya):
tipeDevicenya = argsDevice
net_g_ms = SynthesizerTrn(
len(hps_ms.symbols),
hps_ms.data.filter_length // 2 + 1,
hps_ms.train.segment_size // hps_ms.data.hop_length,
n_speakers=hps_ms.data.n_speakers,
**hps_ms.model)
utils.load_checkpoint('models/ayaka-jp.pth', net_g_ms, None)
_ = net_g_ms.eval().to(tipeDevicenya)
is_symbol = True
input_text = textnya
language = "Mix（wrap the Chinese text with [ZH][ZH], wrap the Japanese text with [JA][JA]）"
noise_scale = 0.6
noise_scale_w = 0.668
length_scale = 1.2
sid = 303
speaker_id = 303
text = input_text.replace('\n', ' ').replace('\r', '').replace(" ", "")
if limitation:
text_len = len(re.sub("\[([A-Z]{2})\]", "", input_text))
max_len = 100
if is_symbol:
max_len *= 3
if text_len > max_len:
print("Error: Text is too long")
if not is_symbol:
if language == 0:
text = f"[ZH]{text}[ZH]"
elif language == 1:
text = f"[JA]{text}[JA]"
else:
text = f"{text}"
stn_tst, clean_text = get_text(text, hps_ms, is_symbol)
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0).to(tipeDevicenya)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(tipeDevicenya)
sid = torch.LongTensor([speaker_id]).to(tipeDevicenya)
audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
sf.write('speech.wav', audio, 22050)
def create_to_symbol_fn(hps, is_symbol_input, input_text, temp_lang):
if temp_lang == 0:
clean_text = f'[ZH]{input_text}[ZH]'
elif temp_lang == 1:
clean_text = f'[JA]{input_text}[JA]'
else:
clean_text = input_text
return _clean_text(clean_text, hps.data.text_cleaners) if is_symbol_input else ''
app = Flask(__name__)
@app.route('/')
def home():
return "WOI"
@app.route('/ayaka/')
def ayakaTTS():
query = request.args.get('text')
generate(query)
return jsonify({"work": query, "url": public_url + "/result"})
@app.route('/result')
def resultAud():
return send_file("speech.wav")
if __name__ == "__main__":
app.run()
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