from onnx_modules.V230_OnnxInference import OnnxInferenceSession
import numpy as np
import torch
from scipy.io.wavfile import write
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
import utils
import commons
import uuid
from flask import Flask, request, jsonify, render_template_string
from flask_cors import CORS
import gradio as gr
import os
from threading import Thread
hps = utils.get_hparams_from_file('onnx/BangDreamApi.json')
device = 'cpu'
BandList = {
"PoppinParty":["香澄","有咲","たえ","りみ","沙綾"],
"Afterglow":["蘭","モカ","ひまり","巴","つぐみ"],
"HelloHappyWorld":["こころ","美咲","薫","花音","はぐみ"],
"PastelPalettes":["彩","日菜","千聖","イヴ","麻弥"],
"Roselia":["友希那","紗夜","リサ","燐子","あこ"],
"RaiseASuilen":["レイヤ","ロック","ますき","チュチュ","パレオ"],
"Morfonica":["ましろ","瑠唯","つくし","七深","透子"],
"MyGo":["燈","愛音","そよ","立希","楽奈"],
"AveMujica":["祥子","睦","海鈴","にゃむ","初華"],
"圣翔音乐学园":["華戀","光","香子","雙葉","真晝","純那","克洛迪娜","真矢","奈奈"],
"凛明馆女子学校":["珠緒","壘","文","悠悠子","一愛"],
"弗隆提亚艺术学校":["艾露","艾露露","菈樂菲","司","靜羽"],
"西克菲尔特音乐学院":["晶","未知留","八千代","栞","美帆"]
}
Session = OnnxInferenceSession(
{
"enc" : "onnx/BangDreamApi/BangDreamApi_enc_p.onnx",
"emb_g" : "onnx/BangDreamApi/BangDreamApi_emb.onnx",
"dp" : "onnx/BangDreamApi/BangDreamApi_dp.onnx",
"sdp" : "onnx/BangDreamApi/BangDreamApi_sdp.onnx",
"flow" : "onnx/BangDreamApi/BangDreamApi_flow.onnx",
"dec" : "onnx/BangDreamApi/BangDreamApi_dec.onnx"
},
Providers = ["CPUExecutionProvider"]
)
def get_text(text, language_str, hps, device, style_text=None, style_weight=0.7):
style_text = None if style_text == "" else style_text
norm_text, phone, tone, word2ph = clean_text(text, language_str)
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
if True:
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_ori = get_bert(
norm_text, word2ph, language_str, device, style_text, style_weight
)
del word2ph
assert bert_ori.shape[-1] == len(phone), phone
if language_str == "ZH":
bert = bert_ori
ja_bert = torch.randn(1024, len(phone))
en_bert = torch.randn(1024, len(phone))
elif language_str == "JP":
bert = torch.randn(1024, len(phone))
ja_bert = bert_ori
en_bert = torch.randn(1024, len(phone))
elif language_str == "EN":
bert = torch.randn(1024, len(phone))
ja_bert = torch.randn(1024, len(phone))
en_bert = bert_ori
else:
raise ValueError("language_str should be ZH, JP or EN")
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, en_bert, phone, tone, language
def infer(
text,
sid,
style_text=None,
style_weight=0.7,
sdp_ratio=0.5,
noise_scale=0.6,
noise_scale_w=0.667,
length_scale=1,
unique_filename = 'temp.wav'
):
language= 'JP' if is_japanese(text) else 'ZH'
bert, ja_bert, en_bert, phones, tone, language = get_text(
text,
language,
hps,
device,
style_text=style_text,
style_weight=style_weight,
)
with torch.no_grad():
x_tst = phones.unsqueeze(0).to(device).numpy()
language = np.zeros_like(x_tst)
tone = np.zeros_like(x_tst)
bert = bert.to(device).transpose(0, 1).numpy()
ja_bert = ja_bert.to(device).transpose(0, 1).numpy()
en_bert = en_bert.to(device).transpose(0, 1).numpy()
del phones
sid = np.array([hps.spk2id[sid]])
audio = Session(
x_tst,
tone,
language,
bert,
ja_bert,
en_bert,
sid,
seed=114514,
seq_noise_scale=noise_scale_w,
sdp_noise_scale=noise_scale,
length_scale=length_scale,
sdp_ratio=sdp_ratio,
)
del x_tst, tone, language, bert, ja_bert, en_bert, sid
write(unique_filename, 44100, audio)
return (44100,gr.processing_utils.convert_to_16_bit_wav(audio))
def is_japanese(string):
for ch in string:
if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
return True
return False
Flaskapp = Flask(__name__)
CORS(Flaskapp)
@Flaskapp.route('/')
def tts():
global last_text, last_model
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))
style_weight = float(request.args.get('style_weight', 0.7))
style_text = request.args.get('style_text', 'happy')
text = request.args.get('text')
is_chat = request.args.get('is_chat', 'false').lower() == 'true'
#model = request.args.get('model',modelPaths[-1])
if not speaker or not text:
return render_template_string("""
TTS API Documentation
""")
'''
if model != last_model:
unique_filename = loadmodel(model)
last_model = model
'''
if is_chat and text == last_text:
# Generate 1 second of silence and return
unique_filename = 'blank.wav'
silence = np.zeros(44100, dtype=np.int16)
write(unique_filename , 44100, silence)
else:
last_text = text
unique_filename = "temp.wav"
infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale,sid = speaker, style_text=style_text, style_weight=style_weight,unique_filename=unique_filename)
with open(unique_filename ,'rb') as bit:
wav_bytes = bit.read()
os.remove(unique_filename)
headers = {
'Content-Type': 'audio/wav',
'Text': unique_filename .encode('utf-8')}
return wav_bytes, 200, headers
if __name__ == "__main__":
speaker_ids = hps.spk2id
speakers = list(speaker_ids.keys())
last_text = ""
Flaskapp.run(host="0.0.0.0", port=5000,debug=True)