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import os
from pathlib import Path
from loguru import logger
# from app import CONFIG_URL, MODEL_URL
from app.util import get_hparams_from_file, get_paths, time_it
import requests
from tqdm.auto import tqdm
import re
from re import Pattern
import onnxruntime as ort
import threading
MODEL_URL = r"https://api.onedrive.com/v1.0/shares/u!aHR0cHM6Ly8xZHJ2Lm1zL3UvcyFBdG53cTVRejJnLTJmckZWcGdCR0xxLWJmU28/root/content"
CONFIG_URL = r"https://api.onedrive.com/v1.0/shares/u!aHR0cHM6Ly8xZHJ2Lm1zL3UvcyFBdG53cTVRejJnLTJhNEJ3enhhUHpqNE5EZWc/root/content"
class Config:
hps: dict = None
pattern: Pattern = None
# symbol_to_id:dict = None
speaker_choices: list = None
ort_sess: ort.InferenceSession = None
model_is_ok: bool = False
@classmethod
def init(cls):
# logger.add(
# "vits_infer.log", rotation="10 MB", encoding="utf-8", enqueue=True, retention="30 days"
# )
brackets = ['（', '[', '『', '「', '【', ")", "】", "]", "』", "」", "）"]
cls.pattern = re.compile('|'.join(map(re.escape, brackets)))
dir_path = Path(__file__).parent.absolute() / ".model"
dir_path.mkdir(
parents=True, exist_ok=True
)
model_path, config_path = get_paths(dir_path)
if not model_path or not config_path:
model_path = dir_path / "model.onnx"
config_path = dir_path / "config.json"
logger.warning(
"unable to find model or config, try to download default model and config"
)
cfg = requests.get(CONFIG_URL, timeout=5).content
with open(str(config_path), 'wb') as f:
f.write(cfg)
cls.setup_config(str(config_path))
t = threading.Thread(target=cls.pdownload,
args=(MODEL_URL, str(model_path)))
t.start()
# cls.pdownload(MODEL_URL, str(model_path))
else:
cls.setup_config(str(config_path))
cls.setup_model(str(model_path))
@classmethod
@logger.catch
@time_it
def setup_model(cls, model_path: str):
import numpy as np
cls.ort_sess = ort.InferenceSession(model_path)
# init the model
seq = np.random.randint(low=0, high=len(
cls.hps.symbols), size=(1, 10), dtype=np.int64)
# seq_len = torch.IntTensor([seq.size(1)]).long()
seq_len = np.array([seq.shape[1]], dtype=np.int64)
# noise(可用于控制感情等变化程度) lenth(可用于控制整体语速) noisew(控制音素发音长度变化程度)
# 参考 https://github.com/gbxh/genshinTTS
# scales = torch.FloatTensor([0.667, 1.0, 0.8])
scales = np.array([0.667, 1.0, 0.8], dtype=np.float32)
# make triton dynamic shape happy
# scales = scales.unsqueeze(0)
scales.resize(1, 3)
# sid = torch.IntTensor([0]).long()
sid = np.array([0], dtype=np.int64)
# sid = torch.LongTensor([0])
ort_inputs = {
'input': seq,
'input_lengths': seq_len,
'scales': scales,
'sid': sid
}
cls.ort_sess.run(None, ort_inputs)
cls.model_is_ok = True
logger.info(
f"model init done with model path {model_path}"
)
@classmethod
def setup_config(cls, config_path: str):
cls.hps = get_hparams_from_file(config_path)
cls.speaker_choices = list(
map(lambda x: str(x[0])+":"+x[1], enumerate(cls.hps.speakers)))
logger.info(
f"config init done with config path {config_path}"
)
@classmethod
def pdownload(cls, url: str, save_path: str, chunk_size: int = 8192):
# copy from https://github.com/tqdm/tqdm/blob/master/examples/tqdm_requests.py
file_size = int(requests.head(url).headers["Content-Length"])
response = requests.get(url, stream=True)
with tqdm(total=file_size, unit='B', unit_scale=True, unit_divisor=1024, miniters=1,
desc="model download") as pbar:
with open(save_path, 'wb') as f:
for chunk in response.iter_content(chunk_size=chunk_size):
if chunk:
f.write(chunk)
pbar.update(chunk_size)
cls.setup_model(save_path)
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