This repository has been archived by the owner on Jun 10, 2022. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 3
/
params.py
51 lines (44 loc) · 1.8 KB
/
params.py
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
# Copyright 2019 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Hyperparameters for YAMNet."""
from dataclasses import dataclass
# The following hyperparameters (except patch_hop_seconds) were used to train YAMNet,
# so expect some variability in performance if you change these. The patch hop can
# be changed arbitrarily: a smaller hop should give you more patches from the same
# clip and possibly better performance at a larger computational cost.
@dataclass(frozen=True) # Instances of this class are immutable.
class Params:
sample_rate: float = 16000.0
stft_window_seconds: float = 0.025
stft_hop_seconds: float = 0.010
mel_bands: int = 64
mel_min_hz: float = 125.0
mel_max_hz: float = 7500.0
log_offset: float = 0.001
patch_window_seconds: float = 0.96
patch_hop_seconds: float = 0.48
@property
def patch_frames(self):
return int(round(self.patch_window_seconds / self.stft_hop_seconds))
@property
def patch_bands(self):
return self.mel_bands
num_classes: int = 521
conv_padding: str = 'same'
batchnorm_center: bool = True
batchnorm_scale: bool = False
batchnorm_epsilon: float = 1e-4
classifier_activation: str = 'sigmoid'
tflite_compatible: bool = False