我运行Tensorflow audio recognition tutorial进行唤醒字检测。对于给定的输入波形文件，模型在大约5秒内返回结果。
有没有办法通过在训练期间更改模型参数将此时间缩短到100毫秒？我遇到了this link that relates to optimizing the graph obtained，但我想知道我可以冒险操作的训练参数是什么，这样可以进一步减少响应时间，而不会影响模型的质量。
训练参数是：
parser.add_argument(
'--background_volume',
type=float,
default=0.1,
help="""\
How loud the background noise should be, between 0 and 1.
""")
parser.add_argument(
'--background_frequency',
type=float,
default=0.8,
help="""\
How many of the training samples have background noise mixed in.
""")
parser.add_argument(
'--silence_percentage',
type=float,
default=10.0,
help="""\
How much of the training data should be silence.
""")
parser.add_argument(
'--unknown_percentage',
type=float,
default=10.0,
help="""\
How much of the training data should be unknown words.
""")
parser.add_argument(
'--time_shift_ms',
type=float,
default=100.0,
help="""\
Range to randomly shift the training audio by in time.
""")
parser.add_argument(
'--testing_percentage',
type=int,
default=10,
help='What percentage of wavs to use as a test set.')
parser.add_argument(
'--validation_percentage',
type=int,
default=10,
help='What percentage of wavs to use as a validation set.')
parser.add_argument(
'--sample_rate',
type=int,
default=16000,
help='Expected sample rate of the wavs',)
parser.add_argument(
'--clip_duration_ms',
type=int,
default=1000,
help='Expected duration in milliseconds of the wavs',)
parser.add_argument(
'--window_size_ms',
type=float,
default=30.0,
help='How long each spectrogram timeslice is',)
parser.add_argument(
'--window_stride_ms',
type=float,
default=10.0,
help='How long each spectrogram timeslice is',)
parser.add_argument(
'--dct_coefficient_count',
type=int,
default=40,
help='How many bins to use for the MFCC fingerprint',)
parser.add_argument(
'--how_many_training_steps',
type=str,
default='15000,3000',
help='How many training loops to run',)
parser.add_argument(
'--eval_step_interval',
type=int,
default=400,
help='How often to evaluate the training results.')
parser.add_argument(
'--learning_rate',
type=str,
default='0.001,0.0001',
help='How large a learning rate to use when training.')
parser.add_argument(
'--batch_size',
type=int,
default=100,
help='How many items to train with at once',)
parser.add_argument(
'--summaries_dir',
type=str,
default='/tmp/retrain_logs',
help='Where to save summary logs for TensorBoard.')
parser.add_argument(
'--wanted_words',
type=str,
default='yes,no,up,down,left,right,on,off,stop,go',
help='Words to use (others will be added to an unknown label)',)
parser.add_argument(
'--train_dir',
type=str,
default='/tmp/speech_commands_train',
help='Directory to write event logs and checkpoint.')
parser.add_argument(
'--save_step_interval',
type=int,
default=100,
help='Save model checkpoint every save_steps.')
parser.add_argument(
'--start_checkpoint',
type=str,
default='',
help='If specified, restore this pretrained model before any training.')
parser.add_argument(
'--model_architecture',
type=str,
default='conv',
help='What model architecture to use')
parser.add_argument(
'--check_nans',
type=bool,
default=False,
help='Whether to check for invalid numbers during processing')