def main():
MODE = 'TIMIT'
# 加载参数
args = get_option(MODE)
speaker_cfg = args.speaker_cfg # speaker recognition识别的网络模型的配置参数
args_speaker = read_conf(speaker_cfg, deepcopy(args))
args_speaker.model_path = args.speaker_model
print(args_speaker.model_path)
pretrained_models = get_pretrained_models(args_speaker)
# 读取数据
speak_file = r'DR1/FCJF0/SA2.WAV.wav'
real_data, fs = sf.read(speak_file)
# 输出说话人的真实标签
# 主要输入要小写
print("当前人的人名:", speak_file.split('/')[1].lower())
print("当前人的label:", get_label_by_name('FCJF0'.lower()))
# 归一化数据
real_data_norm = real_data / np.abs(real_data).max()
# 计算预测值，预测值是个标签
pred_real, pred_pro = sentence_test(
pretrained_models['speaker'].eval(),
torch.from_numpy(real_data_norm).float().cuda().unsqueeze(0))
print("预测的label:", pred_real)
print("根据预测的label获得的人名:", get_name_by_label(pred_real))
print(pred_pro.shape)
def to_excerpt_dict(self):
return {
'name': self.name,
'category': self.category.name,
'tags': [x.name for x in self.tags],
'updated': rfc3339(self.updated),
'excerpt': self.text[:option.get_option(Config.excerptLength)]
}
def get_speaker_model(MODE):
args = get_option(MODE)
speaker_cfg = args.speaker_cfg # speaker recognition识别的网络模型的配置参数
args_speaker = read_conf(speaker_cfg, deepcopy(args))
args_speaker.model_path = args.speaker_model
print(args_speaker.model_path)
pretrained_models = get_pretrained_models(args_speaker)
return pretrained_models['speaker']
def post():
try:
option.get_option(Site.title)
abort(410)
except NotInitializedError as e: # database not set up properly, ok to do OOBE
# set up database
from models.AboutPage import AboutPage
# add options
option.set_option(Config.username, request.json['username'])
option.set_option(Config.passwordHash, generate_password_hash(request.json['password']))
option.set_option(Config.captcha, request.json['captcha'])
BlogMeta.from_dict(request.json['blogMeta']).write_to_options()
# noinspection PyArgumentList
db.session.add(AboutPage(text=request.json['aboutPage']['text']))
db.session.commit()
return api_jsonify(payload=None)
def get():
if not option.get_option(Config.captcha):
abort(404)
answer = ''.join([choice(alphabet) for i in range(4)])
captcha = ImageCaptcha().generate_image(answer)
buf = BytesIO()
captcha.save(buf, format='GIF')
b64_payload = b64encode(buf.getvalue())
payload = {
'image': 'data:image/gif;base64,' + b64_payload.decode('ascii'),
'answer_enc': jwt_encode(interval=timedelta(minutes=5),
data={'answer': answer})
}
return api_jsonify(payload)
def load_from_options():
return BlogMeta(site_title_=option.get_option(Site.title),
author_=option.get_option(Site.author),
contact_=option.get_option(Site.contact),
friends_=option.get_option(Site.friends),
description_=option.get_option(Site.description))
def get():
try:
option.get_option(Site.title)
abort(410)
except NotInitializedError as e: # database not set up properly, ok to do OOBE
return '', 204
def jwt_auth(token):
try:
return jwt_decode(token, audience=get_option(Config.username))
except InvalidTokenError as e:
raise Unauthorized from e
def basic_auth(username, password, required_scopes):
if option.get_option(Config.username) != username \
or not check_password_hash(option.get_option(Config.passwordHash), password):
return None
else:
return {'sub': username}
def wrapped_func(*args, **kwargs):
if get_option(Config.captcha) and\
not check_captcha(request.json.get('captcha')):
abort(403, 'bad captcha')
return func(*args, **kwargs)