keyword_spotting_mobilenetv2¶
Source code: keyword_spotting_mobilenetv2.py
Pre-trained model: keyword_spotting_mobilenetv2.mltk.zip
This model specification script is designed to work with the Model Optimization tutorial.
This model is based on the industry-standard classification model MobileNetV2 developed by Google to detect the keywords:
left
right
up
down
stop
go
MobileNetV2 is a common and useful model because it is generic enough that it can be applied to most classification tasks but still runs efficiently on embedded devices.
Dataset¶
This uses the mltk.datasets.audio.speech_commands.speech_commands_v2 dataset provided by Google.
Preprocessing¶
This uses the mltk.core.preprocess.audio.parallel_generator.ParallelAudioDataGenerator with the
mltk.core.preprocess.audio.audio_feature_generator.AudioFeatureGenerator settings:
sample_rate: 16kHz
sample_length: 1.2s
window size: 30ms
window step: 20ms
n_channels: 49
Commands¶
# Do a "dry run" test training of the model
mltk train keyword_spotting_mobilenetv2-test
# Train the model
mltk train keyword_spotting_mobilenetv2
# Evaluate the trained model .tflite model
mltk evaluate keyword_spotting_mobilenetv2 --tflite
# Profile the model in the MVP hardware accelerator simulator
mltk profile keyword_spotting_mobilenetv2 --accelerator MVP
# Profile the model on a physical development board
mltk profile keyword_spotting_mobilenetv2 --accelerator MVP --device
# Run the model in the audio classifier on the local PC
mltk classify_audio keyword_spotting_mobilenetv2 --verbose
# Run the model in the audio classifier on the physical device
mltk classify_audio keyword_spotting_mobilenetv2 --device --verbose
Model Summary¶
mltk summarize keyword_spotting_mobilenetv2 --tflite
+-------+-------------------+-----------------+-----------------+-------------------------------------------------------+
| Index | OpCode | Input(s) | Output(s) | Config |
+-------+-------------------+-----------------+-----------------+-------------------------------------------------------+
| 0 | conv_2d | 59x49x1 (int8) | 30x25x8 (int8) | Padding:same stride:2x2 activation:relu6 |
| | | 3x3x1 (int8) | | |
| | | 8 (int32) | | |
| 1 | depthwise_conv_2d | 30x25x8 (int8) | 30x25x8 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x8 (int8) | | |
| | | 8 (int32) | | |
| 2 | conv_2d | 30x25x8 (int8) | 30x25x8 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x8 (int8) | | |
| | | 8 (int32) | | |
| 3 | add | 30x25x8 (int8) | 30x25x8 (int8) | Activation:none |
| | | 30x25x8 (int8) | | |
| 4 | conv_2d | 30x25x8 (int8) | 30x25x48 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x8 (int8) | | |
| | | 48 (int32) | | |
| 5 | pad | 30x25x48 (int8) | 31x27x48 (int8) | BuiltinOptionsType=22 |
| | | 2 (int32) | | |
| 6 | depthwise_conv_2d | 31x27x48 (int8) | 15x13x48 (int8) | Multipler:1 padding:valid stride:2x2 activation:relu6 |
| | | 3x3x48 (int8) | | |
| | | 48 (int32) | | |
| 7 | conv_2d | 15x13x48 (int8) | 15x13x8 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x48 (int8) | | |
| | | 8 (int32) | | |
| 8 | conv_2d | 15x13x8 (int8) | 15x13x48 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x8 (int8) | | |
| | | 48 (int32) | | |
| 9 | depthwise_conv_2d | 15x13x48 (int8) | 15x13x48 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x48 (int8) | | |
| | | 48 (int32) | | |
| 10 | conv_2d | 15x13x48 (int8) | 15x13x8 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x48 (int8) | | |
| | | 8 (int32) | | |
| 11 | add | 15x13x8 (int8) | 15x13x8 (int8) | Activation:none |
| | | 15x13x8 (int8) | | |
| 12 | conv_2d | 15x13x8 (int8) | 15x13x48 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x8 (int8) | | |
| | | 48 (int32) | | |
| 13 | pad | 15x13x48 (int8) | 17x15x48 (int8) | BuiltinOptionsType=22 |
| | | 2 (int32) | | |
| 14 | depthwise_conv_2d | 17x15x48 (int8) | 8x7x48 (int8) | Multipler:1 padding:valid stride:2x2 activation:relu6 |
| | | 3x3x48 (int8) | | |
| | | 48 (int32) | | |
| 15 | conv_2d | 8x7x48 (int8) | 8x7x8 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x48 (int8) | | |
| | | 8 (int32) | | |
| 16 | conv_2d | 8x7x8 (int8) | 8x7x48 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x8 (int8) | | |
| | | 48 (int32) | | |
| 17 | depthwise_conv_2d | 8x7x48 (int8) | 8x7x48 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x48 (int8) | | |
| | | 48 (int32) | | |
| 18 | conv_2d | 8x7x48 (int8) | 8x7x8 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x48 (int8) | | |
| | | 8 (int32) | | |
| 19 | add | 8x7x8 (int8) | 8x7x8 (int8) | Activation:none |
| | | 8x7x8 (int8) | | |
| 20 | conv_2d | 8x7x8 (int8) | 8x7x48 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x8 (int8) | | |
| | | 48 (int32) | | |
| 21 | depthwise_conv_2d | 8x7x48 (int8) | 8x7x48 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x48 (int8) | | |
| | | 48 (int32) | | |
| 22 | conv_2d | 8x7x48 (int8) | 8x7x8 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x48 (int8) | | |
| | | 8 (int32) | | |
| 23 | add | 8x7x8 (int8) | 8x7x8 (int8) | Activation:none |
| | | 8x7x8 (int8) | | |
| 24 | conv_2d | 8x7x8 (int8) | 8x7x48 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x8 (int8) | | |
| | | 48 (int32) | | |
| 25 | pad | 8x7x48 (int8) | 9x9x48 (int8) | BuiltinOptionsType=22 |
| | | 2 (int32) | | |
| 26 | depthwise_conv_2d | 9x9x48 (int8) | 4x4x48 (int8) | Multipler:1 padding:valid stride:2x2 activation:relu6 |
| | | 3x3x48 (int8) | | |
| | | 48 (int32) | | |
| 27 | conv_2d | 4x4x48 (int8) | 4x4x16 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x48 (int8) | | |
| | | 16 (int32) | | |
| 28 | conv_2d | 4x4x16 (int8) | 4x4x96 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x16 (int8) | | |
| | | 96 (int32) | | |
| 29 | depthwise_conv_2d | 4x4x96 (int8) | 4x4x96 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x96 (int8) | | |
| | | 96 (int32) | | |
| 30 | conv_2d | 4x4x96 (int8) | 4x4x16 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x96 (int8) | | |
| | | 16 (int32) | | |
| 31 | add | 4x4x16 (int8) | 4x4x16 (int8) | Activation:none |
| | | 4x4x16 (int8) | | |
| 32 | conv_2d | 4x4x16 (int8) | 4x4x96 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x16 (int8) | | |
| | | 96 (int32) | | |
| 33 | depthwise_conv_2d | 4x4x96 (int8) | 4x4x96 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x96 (int8) | | |
| | | 96 (int32) | | |
| 34 | conv_2d | 4x4x96 (int8) | 4x4x16 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x96 (int8) | | |
| | | 16 (int32) | | |
| 35 | add | 4x4x16 (int8) | 4x4x16 (int8) | Activation:none |
| | | 4x4x16 (int8) | | |
| 36 | conv_2d | 4x4x16 (int8) | 4x4x96 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x16 (int8) | | |
| | | 96 (int32) | | |
| 37 | depthwise_conv_2d | 4x4x96 (int8) | 4x4x96 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x96 (int8) | | |
| | | 96 (int32) | | |
| 38 | conv_2d | 4x4x96 (int8) | 4x4x16 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x96 (int8) | | |
| | | 16 (int32) | | |
| 39 | add | 4x4x16 (int8) | 4x4x16 (int8) | Activation:none |
| | | 4x4x16 (int8) | | |
| 40 | conv_2d | 4x4x16 (int8) | 4x4x96 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x16 (int8) | | |
| | | 96 (int32) | | |
| 41 | depthwise_conv_2d | 4x4x96 (int8) | 4x4x96 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x96 (int8) | | |
| | | 96 (int32) | | |
| 42 | conv_2d | 4x4x96 (int8) | 4x4x16 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x96 (int8) | | |
| | | 16 (int32) | | |
| 43 | add | 4x4x16 (int8) | 4x4x16 (int8) | Activation:none |
| | | 4x4x16 (int8) | | |
| 44 | conv_2d | 4x4x16 (int8) | 4x4x96 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x16 (int8) | | |
| | | 96 (int32) | | |
| 45 | depthwise_conv_2d | 4x4x96 (int8) | 4x4x96 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x96 (int8) | | |
| | | 96 (int32) | | |
| 46 | conv_2d | 4x4x96 (int8) | 4x4x16 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x96 (int8) | | |
| | | 16 (int32) | | |
| 47 | add | 4x4x16 (int8) | 4x4x16 (int8) | Activation:none |
| | | 4x4x16 (int8) | | |
| 48 | conv_2d | 4x4x16 (int8) | 4x4x96 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x16 (int8) | | |
| | | 96 (int32) | | |
| 49 | depthwise_conv_2d | 4x4x96 (int8) | 4x4x96 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x96 (int8) | | |
| | | 96 (int32) | | |
| 50 | conv_2d | 4x4x96 (int8) | 4x4x16 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x96 (int8) | | |
| | | 16 (int32) | | |
| 51 | add | 4x4x16 (int8) | 4x4x16 (int8) | Activation:none |
| | | 4x4x16 (int8) | | |
| 52 | conv_2d | 4x4x16 (int8) | 4x4x96 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x16 (int8) | | |
| | | 96 (int32) | | |
| 53 | pad | 4x4x96 (int8) | 5x5x96 (int8) | BuiltinOptionsType=22 |
| | | 2 (int32) | | |
| 54 | depthwise_conv_2d | 5x5x96 (int8) | 2x2x96 (int8) | Multipler:1 padding:valid stride:2x2 activation:relu6 |
| | | 3x3x96 (int8) | | |
| | | 96 (int32) | | |
| 55 | conv_2d | 2x2x96 (int8) | 2x2x24 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x96 (int8) | | |
| | | 24 (int32) | | |
| 56 | conv_2d | 2x2x24 (int8) | 2x2x144 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x24 (int8) | | |
| | | 144 (int32) | | |
| 57 | depthwise_conv_2d | 2x2x144 (int8) | 2x2x144 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x144 (int8) | | |
| | | 144 (int32) | | |
| 58 | conv_2d | 2x2x144 (int8) | 2x2x24 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x144 (int8) | | |
| | | 24 (int32) | | |
| 59 | add | 2x2x24 (int8) | 2x2x24 (int8) | Activation:none |
| | | 2x2x24 (int8) | | |
| 60 | conv_2d | 2x2x24 (int8) | 2x2x144 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x24 (int8) | | |
| | | 144 (int32) | | |
| 61 | depthwise_conv_2d | 2x2x144 (int8) | 2x2x144 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x144 (int8) | | |
| | | 144 (int32) | | |
| 62 | conv_2d | 2x2x144 (int8) | 2x2x24 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x144 (int8) | | |
| | | 24 (int32) | | |
| 63 | add | 2x2x24 (int8) | 2x2x24 (int8) | Activation:none |
| | | 2x2x24 (int8) | | |
| 64 | conv_2d | 2x2x24 (int8) | 2x2x144 (int8) | Padding:same stride:1x1 activation:relu6 |
| | | 1x1x24 (int8) | | |
| | | 144 (int32) | | |
| 65 | depthwise_conv_2d | 2x2x144 (int8) | 2x2x144 (int8) | Multipler:1 padding:same stride:1x1 activation:relu6 |
| | | 3x3x144 (int8) | | |
| | | 144 (int32) | | |
| 66 | conv_2d | 2x2x144 (int8) | 2x2x48 (int8) | Padding:same stride:1x1 activation:none |
| | | 1x1x144 (int8) | | |
| | | 48 (int32) | | |
| 67 | conv_2d | 2x2x48 (int8) | 2x2x384 (int8) | Padding:valid stride:1x1 activation:relu6 |
| | | 1x1x48 (int8) | | |
| | | 384 (int32) | | |
| 68 | mean | 2x2x384 (int8) | 384 (int8) | BuiltinOptionsType=27 |
| | | 2 (int32) | | |
| 69 | fully_connected | 384 (int8) | 8 (int8) | Activation:none |
| | | 384 (int8) | | |
| | | 8 (int32) | | |
| 70 | softmax | 8 (int8) | 8 (int8) | BuiltinOptionsType=9 |
+-------+-------------------+-----------------+-----------------+-------------------------------------------------------+
Total MACs: 1.737 M
Total OPs: 3.977 M
Name: keyword_spotting_mobilenetv2
Version: 1
Description: Keyword spotting classifier using MobileNetv2 to detect: left, right, up, down, stop, go
Classes: left, right, up, down, stop, go, _unknown_, _silence_
hash: e7018a67a673713fe4935f20ca88b492
date: 2022-02-04T18:59:27.420Z
runtime_memory_size: 105016
average_window_duration_ms: 1000
detection_threshold: 145
suppression_ms: 1000
minimum_count: 3
volume_db: 5.0
latency_ms: 0
log_level: info
samplewise_norm.rescale: 0.0
samplewise_norm.mean_and_std: False
fe.sample_rate_hz: 16000
fe.sample_length_ms: 1200
fe.window_size_ms: 30
fe.window_step_ms: 20
fe.filterbank_n_channels: 49
fe.filterbank_upper_band_limit: 3999.0
fe.filterbank_lower_band_limit: 125.0
fe.noise_reduction_enable: True
fe.noise_reduction_smoothing_bits: 10
fe.noise_reduction_even_smoothing: 0.02500000037252903
fe.noise_reduction_odd_smoothing: 0.05999999865889549
fe.noise_reduction_min_signal_remaining: 0.029999999329447746
fe.pcan_enable: False
fe.pcan_strength: 0.949999988079071
fe.pcan_offset: 80.0
fe.pcan_gain_bits: 21
fe.log_scale_enable: True
fe.log_scale_shift: 6
fe.fft_length: 512
.tflite file size: 259.0kB
Model Profiling Report¶
# Profile on physical EFR32xG24 using MVP accelerator
mltk profile keyword_spotting_mobilenetv2 --device --accelerator MVP
Profiling Summary
Name: keyword_spotting_mobilenetv2
Accelerator: MVP
Input Shape: 1x59x49x1
Input Data Type: int8
Output Shape: 1x8
Output Data Type: int8
Flash, Model File Size (bytes): 258.9k
RAM, Runtime Memory Size (bytes): 104.9k
Operation Count: 4.2M
Multiply-Accumulate Count: 1.7M
Layer Count: 71
Unsupported Layer Count: 0
Accelerator Cycle Count: 2.2M
CPU Cycle Count: 3.1M
CPU Utilization (%): 67.9
Clock Rate (hz): 78.0M
Time (s): 58.3m
Ops/s: 72.6M
MACs/s: 29.8M
Inference/s: 17.1
Model Layers
+-------+-------------------+--------+--------+------------+------------+----------+-------------------------+--------------+--------------------------------------------------------+
| Index | OpCode | # Ops | # MACs | Acc Cycles | CPU Cycles | Time (s) | Input Shape | Output Shape | Options |
+-------+-------------------+--------+--------+------------+------------+----------+-------------------------+--------------+--------------------------------------------------------+
| 0 | conv_2d | 126.0k | 54.0k | 86.7k | 27.9k | 1.3m | 1x59x49x1,8x3x3x1,8 | 1x30x25x8 | Padding:same stride:2x2 activation:relu6 |
| 1 | depthwise_conv_2d | 126.0k | 54.0k | 189.4k | 214.1k | 4.4m | 1x30x25x8,1x3x3x8,8 | 1x30x25x8 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 2 | conv_2d | 102.0k | 48.0k | 57.7k | 5.9k | 780.0u | 1x30x25x8,8x1x1x8,8 | 1x30x25x8 | Padding:same stride:1x1 activation:none |
| 3 | add | 6.0k | 0 | 21.0k | 4.6k | 300.0u | 1x30x25x8,1x30x25x8 | 1x30x25x8 | Activation:none |
| 4 | conv_2d | 684.0k | 288.0k | 327.7k | 5.3k | 4.2m | 1x30x25x8,48x1x1x8,48 | 1x30x25x48 | Padding:same stride:1x1 activation:relu6 |
| 5 | pad | 241.1k | 0 | 0 | 967.9k | 12.2m | 1x30x25x48,4x2 | 1x31x27x48 | Type=padoptions |
| 6 | depthwise_conv_2d | 196.6k | 84.2k | 157.8k | 282.4k | 3.6m | 1x31x27x48,1x3x3x48,48 | 1x15x13x48 | Multiplier:1 padding:valid stride:2x2 activation:relu6 |
| 7 | conv_2d | 151.3k | 74.9k | 65.6k | 5.5k | 870.0u | 1x15x13x48,8x1x1x48,8 | 1x15x13x8 | Padding:same stride:1x1 activation:none |
| 8 | conv_2d | 177.8k | 74.9k | 85.1k | 5.3k | 1.1m | 1x15x13x8,48x1x1x8,48 | 1x15x13x48 | Padding:same stride:1x1 activation:relu6 |
| 9 | depthwise_conv_2d | 196.6k | 84.2k | 145.4k | 280.3k | 3.5m | 1x15x13x48,1x3x3x48,48 | 1x15x13x48 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 10 | conv_2d | 151.3k | 74.9k | 65.8k | 5.4k | 870.0u | 1x15x13x48,8x1x1x48,8 | 1x15x13x8 | Padding:same stride:1x1 activation:none |
| 11 | add | 1.6k | 0 | 5.5k | 2.7k | 120.0u | 1x15x13x8,1x15x13x8 | 1x15x13x8 | Activation:none |
| 12 | conv_2d | 177.8k | 74.9k | 85.5k | 5.3k | 1.1m | 1x15x13x8,48x1x1x8,48 | 1x15x13x48 | Padding:same stride:1x1 activation:relu6 |
| 13 | pad | 73.4k | 0 | 0 | 277.1k | 3.5m | 1x15x13x48,4x2 | 1x17x15x48 | Type=padoptions |
| 14 | depthwise_conv_2d | 56.4k | 24.2k | 45.3k | 82.0k | 1.1m | 1x17x15x48,1x3x3x48,48 | 1x8x7x48 | Multiplier:1 padding:valid stride:2x2 activation:relu6 |
| 15 | conv_2d | 43.5k | 21.5k | 18.9k | 5.5k | 300.0u | 1x8x7x48,8x1x1x48,8 | 1x8x7x8 | Padding:same stride:1x1 activation:none |
| 16 | conv_2d | 51.1k | 21.5k | 24.5k | 5.3k | 360.0u | 1x8x7x8,48x1x1x8,48 | 1x8x7x48 | Padding:same stride:1x1 activation:relu6 |
| 17 | depthwise_conv_2d | 56.4k | 24.2k | 38.8k | 81.0k | 1.0m | 1x8x7x48,1x3x3x48,48 | 1x8x7x48 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 18 | conv_2d | 43.5k | 21.5k | 19.2k | 5.4k | 270.0u | 1x8x7x48,8x1x1x48,8 | 1x8x7x8 | Padding:same stride:1x1 activation:none |
| 19 | add | 448.0 | 0 | 1.6k | 2.7k | 60.0u | 1x8x7x8,1x8x7x8 | 1x8x7x8 | Activation:none |
| 20 | conv_2d | 51.1k | 21.5k | 24.5k | 5.3k | 390.0u | 1x8x7x8,48x1x1x8,48 | 1x8x7x48 | Padding:same stride:1x1 activation:relu6 |
| 21 | depthwise_conv_2d | 56.4k | 24.2k | 38.8k | 81.1k | 1.0m | 1x8x7x48,1x3x3x48,48 | 1x8x7x48 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 22 | conv_2d | 43.5k | 21.5k | 19.2k | 5.4k | 300.0u | 1x8x7x48,8x1x1x48,8 | 1x8x7x8 | Padding:same stride:1x1 activation:none |
| 23 | add | 448.0 | 0 | 1.6k | 2.7k | 60.0u | 1x8x7x8,1x8x7x8 | 1x8x7x8 | Activation:none |
| 24 | conv_2d | 51.1k | 21.5k | 24.6k | 5.3k | 360.0u | 1x8x7x8,48x1x1x8,48 | 1x8x7x48 | Padding:same stride:1x1 activation:relu6 |
| 25 | pad | 23.3k | 0 | 0 | 81.4k | 1.0m | 1x8x7x48,4x2 | 1x9x9x48 | Type=padoptions |
| 26 | depthwise_conv_2d | 16.1k | 6.9k | 12.9k | 24.3k | 330.0u | 1x9x9x48,1x3x3x48,48 | 1x4x4x48 | Multiplier:1 padding:valid stride:2x2 activation:relu6 |
| 27 | conv_2d | 24.8k | 12.3k | 10.6k | 5.5k | 210.0u | 1x4x4x48,16x1x1x48,16 | 1x4x4x16 | Padding:same stride:1x1 activation:none |
| 28 | conv_2d | 53.8k | 24.6k | 23.4k | 5.3k | 360.0u | 1x4x4x16,96x1x1x16,96 | 1x4x4x96 | Padding:same stride:1x1 activation:relu6 |
| 29 | depthwise_conv_2d | 32.2k | 13.8k | 18.9k | 24.3k | 390.0u | 1x4x4x96,1x3x3x96,96 | 1x4x4x96 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 30 | conv_2d | 49.4k | 24.6k | 20.4k | 5.4k | 300.0u | 1x4x4x96,16x1x1x96,16 | 1x4x4x16 | Padding:same stride:1x1 activation:none |
| 31 | add | 256.0 | 0 | 908.0 | 2.7k | 60.0u | 1x4x4x16,1x4x4x16 | 1x4x4x16 | Activation:none |
| 32 | conv_2d | 53.8k | 24.6k | 23.4k | 5.3k | 360.0u | 1x4x4x16,96x1x1x16,96 | 1x4x4x96 | Padding:same stride:1x1 activation:relu6 |
| 33 | depthwise_conv_2d | 32.2k | 13.8k | 18.9k | 24.4k | 390.0u | 1x4x4x96,1x3x3x96,96 | 1x4x4x96 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 34 | conv_2d | 49.4k | 24.6k | 20.4k | 5.4k | 300.0u | 1x4x4x96,16x1x1x96,16 | 1x4x4x16 | Padding:same stride:1x1 activation:none |
| 35 | add | 256.0 | 0 | 908.0 | 2.7k | 60.0u | 1x4x4x16,1x4x4x16 | 1x4x4x16 | Activation:none |
| 36 | conv_2d | 53.8k | 24.6k | 23.4k | 5.3k | 360.0u | 1x4x4x16,96x1x1x16,96 | 1x4x4x96 | Padding:same stride:1x1 activation:relu6 |
| 37 | depthwise_conv_2d | 32.2k | 13.8k | 18.9k | 24.4k | 390.0u | 1x4x4x96,1x3x3x96,96 | 1x4x4x96 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 38 | conv_2d | 49.4k | 24.6k | 20.4k | 5.4k | 300.0u | 1x4x4x96,16x1x1x96,16 | 1x4x4x16 | Padding:same stride:1x1 activation:none |
| 39 | add | 256.0 | 0 | 908.0 | 2.7k | 30.0u | 1x4x4x16,1x4x4x16 | 1x4x4x16 | Activation:none |
| 40 | conv_2d | 53.8k | 24.6k | 23.4k | 5.3k | 360.0u | 1x4x4x16,96x1x1x16,96 | 1x4x4x96 | Padding:same stride:1x1 activation:relu6 |
| 41 | depthwise_conv_2d | 32.2k | 13.8k | 18.9k | 24.4k | 390.0u | 1x4x4x96,1x3x3x96,96 | 1x4x4x96 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 42 | conv_2d | 49.4k | 24.6k | 20.4k | 5.4k | 330.0u | 1x4x4x96,16x1x1x96,16 | 1x4x4x16 | Padding:same stride:1x1 activation:none |
| 43 | add | 256.0 | 0 | 908.0 | 2.7k | 30.0u | 1x4x4x16,1x4x4x16 | 1x4x4x16 | Activation:none |
| 44 | conv_2d | 53.8k | 24.6k | 23.4k | 5.3k | 360.0u | 1x4x4x16,96x1x1x16,96 | 1x4x4x96 | Padding:same stride:1x1 activation:relu6 |
| 45 | depthwise_conv_2d | 32.2k | 13.8k | 18.9k | 24.4k | 390.0u | 1x4x4x96,1x3x3x96,96 | 1x4x4x96 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 46 | conv_2d | 49.4k | 24.6k | 20.4k | 5.4k | 300.0u | 1x4x4x96,16x1x1x96,16 | 1x4x4x16 | Padding:same stride:1x1 activation:none |
| 47 | add | 256.0 | 0 | 908.0 | 2.7k | 30.0u | 1x4x4x16,1x4x4x16 | 1x4x4x16 | Activation:none |
| 48 | conv_2d | 53.8k | 24.6k | 23.4k | 5.3k | 360.0u | 1x4x4x16,96x1x1x16,96 | 1x4x4x96 | Padding:same stride:1x1 activation:relu6 |
| 49 | depthwise_conv_2d | 32.2k | 13.8k | 18.9k | 24.4k | 390.0u | 1x4x4x96,1x3x3x96,96 | 1x4x4x96 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 50 | conv_2d | 49.4k | 24.6k | 20.4k | 5.4k | 300.0u | 1x4x4x96,16x1x1x96,16 | 1x4x4x16 | Padding:same stride:1x1 activation:none |
| 51 | add | 256.0 | 0 | 908.0 | 2.7k | 60.0u | 1x4x4x16,1x4x4x16 | 1x4x4x16 | Activation:none |
| 52 | conv_2d | 53.8k | 24.6k | 23.4k | 5.3k | 360.0u | 1x4x4x16,96x1x1x16,96 | 1x4x4x96 | Padding:same stride:1x1 activation:relu6 |
| 53 | pad | 14.4k | 0 | 0 | 41.5k | 510.0u | 1x4x4x96,4x2 | 1x5x5x96 | Type=padoptions |
| 54 | depthwise_conv_2d | 8.1k | 3.5k | 6.3k | 7.0k | 150.0u | 1x5x5x96,1x3x3x96,96 | 1x2x2x96 | Multiplier:1 padding:valid stride:2x2 activation:relu6 |
| 55 | conv_2d | 18.5k | 9.2k | 7.6k | 5.4k | 150.0u | 1x2x2x96,24x1x1x96,24 | 1x2x2x24 | Padding:same stride:1x1 activation:none |
| 56 | conv_2d | 29.4k | 13.8k | 12.4k | 5.3k | 240.0u | 1x2x2x24,144x1x1x24,144 | 1x2x2x144 | Padding:same stride:1x1 activation:relu6 |
| 57 | depthwise_conv_2d | 12.1k | 5.2k | 5.0k | 6.9k | 120.0u | 1x2x2x144,1x3x3x144,144 | 1x2x2x144 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 58 | conv_2d | 27.7k | 13.8k | 11.2k | 5.4k | 210.0u | 1x2x2x144,24x1x1x144,24 | 1x2x2x24 | Padding:same stride:1x1 activation:none |
| 59 | add | 96.0 | 0 | 348.0 | 2.7k | 30.0u | 1x2x2x24,1x2x2x24 | 1x2x2x24 | Activation:none |
| 60 | conv_2d | 29.4k | 13.8k | 12.4k | 5.3k | 210.0u | 1x2x2x24,144x1x1x24,144 | 1x2x2x144 | Padding:same stride:1x1 activation:relu6 |
| 61 | depthwise_conv_2d | 12.1k | 5.2k | 5.0k | 7.0k | 120.0u | 1x2x2x144,1x3x3x144,144 | 1x2x2x144 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 62 | conv_2d | 27.7k | 13.8k | 11.2k | 5.4k | 180.0u | 1x2x2x144,24x1x1x144,24 | 1x2x2x24 | Padding:same stride:1x1 activation:none |
| 63 | add | 96.0 | 0 | 348.0 | 2.7k | 30.0u | 1x2x2x24,1x2x2x24 | 1x2x2x24 | Activation:none |
| 64 | conv_2d | 29.4k | 13.8k | 12.4k | 5.3k | 210.0u | 1x2x2x24,144x1x1x24,144 | 1x2x2x144 | Padding:same stride:1x1 activation:relu6 |
| 65 | depthwise_conv_2d | 12.1k | 5.2k | 5.0k | 7.0k | 120.0u | 1x2x2x144,1x3x3x144,144 | 1x2x2x144 | Multiplier:1 padding:same stride:1x1 activation:relu6 |
| 66 | conv_2d | 55.5k | 27.6k | 21.8k | 5.4k | 360.0u | 1x2x2x144,48x1x1x144,48 | 1x2x2x48 | Padding:same stride:1x1 activation:none |
| 67 | conv_2d | 152.1k | 73.7k | 60.5k | 5.3k | 810.0u | 1x2x2x48,384x1x1x48,384 | 1x2x2x384 | Padding:valid stride:1x1 activation:relu6 |
| 68 | mean | 0 | 0 | 0 | 246.6k | 3.1m | 1x2x2x384,2 | 1x384 | Type=reduceroptions |
| 69 | fully_connected | 6.2k | 3.1k | 4.7k | 2.2k | 90.0u | 1x384,8x384,8 | 1x8 | Activation:none |
| 70 | softmax | 40.0 | 0 | 0 | 6.9k | 90.0u | 1x8 | 1x8 | Type=softmaxoptions |
+-------+-------------------+--------+--------+------------+------------+----------+-------------------------+--------------+--------------------------------------------------------+
Model Diagram¶
mltk view keyword_spotting_mobilenetv2 --tflite
Model Specification¶
from typing import List, Tuple
# Import the Tensorflow packages
# required to build the model layout
import numpy as np
import tensorflow as tf
# Import the MLTK model object
# and necessary mixins
# Later in this script we configure the various properties
from mltk.core import (
MltkModel,
TrainMixin,
AudioDatasetMixin,
EvaluateClassifierMixin
)
# Import the Google speech_commands dataset package
# This manages downloading and extracting the dataset
from mltk.datasets.audio.speech_commands import speech_commands_v2
# Import the ParallelAudioDataGenerator
# This has two main jobs:
# 1. Process the Google speech_commands dataset and apply random augmentations during training
# 2. Generate a spectrogram using the AudioFeatureGenerator from each augmented audio sample
# and give the spectrogram to Tensorflow for model training
from mltk.core.preprocess.audio.parallel_generator import ParallelAudioDataGenerator, ParallelProcessParams
# Import the AudioFeatureGeneratorSettings which we'll configure
# and give to the ParallelAudioDataGenerator
from mltk.core.preprocess.audio.audio_feature_generator import AudioFeatureGeneratorSettings
from mltk.models.shared import MobileNetV2
# Define a custom model object with the following 'mixins':
# - TrainMixin - Provides classifier model training operations and settings
# - AudioDatasetMixin - Provides audio data generation operations and settings
# - EvaluateClassifierMixin - Provides classifier evaluation operations and settings
# @mltk_model # NOTE: This tag is required for this model be discoverable
class MyModel(
MltkModel,
TrainMixin,
AudioDatasetMixin,
EvaluateClassifierMixin
):
pass
# Instantiate our custom model object
# The rest of this script simply configures the properties
# of our custom model object
my_model = MyModel()
#################################################
# General Settings
# For better tracking, the version should be incremented any time a non-trivial change is made
# NOTE: The version is optional and not used directly used by the MLTK
my_model.version = 1
# Provide a brief description about what this model models
# This description goes in the "description" field of the .tflite model file
my_model.description = 'Keyword spotting classifier using MobileNetv2 to detect: left, right, up, down, stop, go'
#################################################
# Training Basic Settings
# This specifies the number of times we run the training
# samples through the model to update the model weights.
# Typically, a larger value leads to better accuracy at the expense of training time.
# Set to -1 to use the early_stopping callback and let the scripts
# determine how many epochs to train for (see below).
# Otherwise set this to a specific value (typically 40-200)
my_model.epochs = 70
# Specify how many samples to pass through the model
# before updating the training gradients.
# Typical values are 10-64
# NOTE: Larger values require more memory and may not fit on your GPU
my_model.batch_size = 32
# This specifies the algorithm used to update the model gradients
# during training. Adam is very common
# See https://www.tensorflow.org/api_docs/python/tf/keras/optimizers
my_model.optimizer = 'adam'
# List of metrics to be evaluated by the model during training and testing
my_model.metrics = ['accuracy']
# The "loss" function used to update the weights
# This is a classification problem with more than two labels so we use categorical_crossentropy
# See https://www.tensorflow.org/api_docs/python/tf/keras/losses
my_model.loss = 'categorical_crossentropy'
#################################################
# Training callback Settings
# Generate checkpoints every time the validation accuracy improves
# See https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint
my_model.checkpoint['monitor'] = 'val_accuracy'
# If the training accuracy doesn't improve after 'patience' epochs
# then decrease the learning rate by 'factor'
# https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ReduceLROnPlateau
# NOTE: Alternatively, we could define our own learn rate schedule
# using my_model.lr_schedule
# my_model.reduce_lr_on_plateau = dict(
# monitor='accuracy',
# factor = 0.6,
# patience = 2,
# min_delta=0.001,
# min_lr=1e-7,
# verbose=1
# )
# https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LearningRateScheduler
# Update the learning rate each epoch based on the given callback
def lr_schedule(epoch):
initial_learning_rate = 0.001
decay_per_epoch = 0.95
lrate = initial_learning_rate * (decay_per_epoch ** epoch)
return lrate
my_model.lr_schedule = dict(
schedule = lr_schedule,
verbose = 1
)
# If the validation accuracy doesn't improve after 'patience' epochs
# then stop training, the epochs must be -1 to use this callback
# See https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
my_model.early_stopping = dict(
monitor='val_accuracy',
patience=20
)
#################################################
# TF-Lite converter settings
# These are the settings used to quantize the model
# We want all the internal ops as well as
# model input/output to be int8
my_model.tflite_converter['optimizations'] = [tf.lite.Optimize.DEFAULT]
my_model.tflite_converter['supported_ops'] = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
my_model.tflite_converter['inference_input_type'] = np.int8
my_model.tflite_converter['inference_output_type'] = np.int8
# Automatically generate a representative dataset from the validation data
my_model.tflite_converter['representative_dataset'] = 'generate'
#################################################
# Audio Data Provider Settings
# Specify the dataset
# NOTE: This can also be an absolute path to a directory
# or a Python function
# See: https://siliconlabs.github.io/mltk/docs/python_api/mltk_model/audio_dataset_mixin.html#mltk.core.AudioDatasetMixin.dataset
my_model.dataset = speech_commands_v2
# We're using a 'categorical_crossentropy' loss
# so must also use a `categorical` class mode for the data generation
my_model.class_mode = 'categorical'
# Specify the keywords we want to detect
# In this model, we detect: left, right, up, down, stop, go
# plus two pseudo classes: _unknown_ and _silence_
my_model.classes = ['left', 'right', 'up', 'down', 'stop', 'go', '_unknown_', '_silence_']
# The numbers of samples for each class is different
# Then ensures each class contributes equally to training the model
my_model.class_weights = 'balanced'
#################################################
# AudioFeatureGenerator Settings
#
# These are the settings used by the AudioFeatureGenerator
# to generate spectrograms from the audio samples
# These settings must be used during modeling training
# AND by embedded device at runtime
#
# See https://siliconlabs.github.io/mltk/docs/audio/audio_feature_generator.html
frontend_settings = AudioFeatureGeneratorSettings()
frontend_settings.sample_rate_hz = 16000 # We use 16k for slightly better performance at the cost of more RAM
frontend_settings.sample_length_ms = 1200 # We use a 1.2s buffer to ensure we can process a sample multiple times
frontend_settings.window_size_ms = 30
frontend_settings.window_step_ms = 20
frontend_settings.filterbank_n_channels = 49
frontend_settings.filterbank_upper_band_limit = 4000.0-1 # Spoken language usually only goes up to 4k
frontend_settings.filterbank_lower_band_limit = 125.0
frontend_settings.noise_reduction_enable = True
frontend_settings.noise_reduction_smoothing_bits = 10
frontend_settings.noise_reduction_even_smoothing = 0.025
frontend_settings.noise_reduction_odd_smoothing = 0.06
frontend_settings.noise_reduction_min_signal_remaining = 0.03
frontend_settings.pcan_enable = False
frontend_settings.pcan_strength = 0.95
frontend_settings.pcan_offset = 80.0
frontend_settings.pcan_gain_bits = 21
frontend_settings.log_scale_enable = True
frontend_settings.log_scale_shift = 6
import os
import librosa
import random
def get_batches_samples(
batch_index:int,
filenames:List[str],
classes:List[int],
params:ParallelProcessParams
) -> Tuple[int, Tuple[np.ndarray, np.ndarray]]:
"""This slightly modified from the standard function that comes with the MLTK:
https://github.com/siliconlabs/mltk/blob/master/mltk/core/preprocess/audio/parallel_generator/iterator.py#L241
This implementation crops "known" samples and adds
them to the "unknown" samples.
This way, the model only considers "known" samples when
they fully appear in the spectrogram.
See: https://siliconlabs.github.io/mltk/docs/keyword_spotting_overview.html
"""
batch_shape = (len(filenames),) + params.sample_shape
batch_x = np.zeros(batch_shape, dtype=params.dtype)
# Find the indices of all the non-unknown samples in this batch
unknown_class_id = len(params.class_indices)-2
non_unknown_class_indices = []
for class_index, class_id in enumerate(classes):
if class_id < unknown_class_id:
non_unknown_class_indices.append(class_index)
for i, filename in enumerate(filenames):
if filename:
filepath = os.path.join(params.directory, filename)
x, orignal_sr = librosa.load(filepath, sr=None, mono=True, dtype='float32')
else:
orignal_sr = 16000
x = np.zeros((orignal_sr,), dtype='float32')
# At this point,
# x = [sample_length] dtype=float32
if params.noaug_preprocessing_function is not None:
x = params.noaug_preprocessing_function(params, x)
use_cropped_sample_as_unknown = False
if params.subset != 'validation' or params.audio_data_generator.validation_augmentation_enabled:
transform_params = params.audio_data_generator.get_random_transform()
# 50% of the time we want to replace an "unknown" sample with a cropped "known" sample
use_cropped_sample_as_unknown = len(non_unknown_class_indices) > 0 and classes[i] == unknown_class_id and random.randint(0, 1) == 1
else:
transform_params = params.audio_data_generator.default_transform
# Apply any audio augmentations
# NOTE: If transform_params = default_transform
# Then the audio sample is simply cropped/padded to fit the expected sample length
x = params.audio_data_generator.apply_transform(x, orignal_sr, transform_params)
if params.preprocessing_function is not None:
x = params.preprocessing_function(params, x)
# If we should replace the current "unknown" sample
# with a cropped "known" sample
if use_cropped_sample_as_unknown:
x_len = len(x)
# Replace the current "unknown" sample with background noise
bg_noise_offset = np.random.uniform(0.0, 1.0)
bg_noise = random.choice([*params.audio_data_generator.bg_noises.values()])
bg_noise = params.audio_data_generator.adjust_length(bg_noise, params.sample_rate, offset=bg_noise_offset, out_length=x_len)
x = bg_noise * (transform_params['bg_noise_factor'] * transform_params['loudness_factor'])
# Randomly find a "known" sample from the current batch
# And load it from the audio file
sample_index = random.choice(non_unknown_class_indices)
filepath = os.path.join(params.directory, filenames[sample_index])
sample_data, _ = librosa.load(filepath, sr=params.sample_rate, mono=True, dtype='float32')
# Trim any silence from the beginning and end of the "known" sample's audio
sample_trimmed, _ = librosa.effects.trim(sample_data, top_db=30)
# Determine how much of the "known" sample we want to keep
# Randomly pick a number between 20% & 50%
sample_length = len(sample_trimmed)
cropped_sample_percent = random.uniform(.2, .5)
cropped_sample_length = int(sample_length * cropped_sample_percent)
# Add the cropped "known" sample to the END of the current "unknown" sample
# This simulates part of a "known" word streaming into the audio buffer
# Again, we only want the model to consider the full word to be "known"
# A partial word should be considered "unknown"
x[x_len-cropped_sample_length:] += sample_trimmed[:cropped_sample_length]
if params.frontend_enabled:
# After point through the frontend,
# x = [height, width] dtype=self.dtype
x = params.audio_data_generator.apply_frontend(x, dtype=params.dtype)
# Perform any post processing as necessary
if params.postprocessing_function is not None:
x = params.postprocessing_function(params, x)
if params.frontend_enabled:
# Do any standardizations (which are done using float32 internally)
x = params.audio_data_generator.standardize(x)
# Convert the sample's shape from [height, width]
# to [height, width, 1]
batch_x[i] = np.expand_dims(x, axis=-1)
else:
batch_x[i] = x
# build batch of labels
if params.class_mode == 'input':
batch_y = batch_x.copy()
elif params.class_mode in {'binary', 'sparse'}:
batch_y = np.empty(len(batch_x), dtype=params.dtype)
for i, class_id in enumerate(classes):
batch_y[i] = class_id
elif params.class_mode == 'categorical':
batch_y = np.zeros((len(batch_x), len(params.class_indices)), dtype=params.dtype)
for i, class_id in enumerate(classes):
batch_y[i, class_id] = 1.
else:
return batch_index, batch_x
return batch_index, (batch_x, batch_y)
#################################################
# ParallelAudioDataGenerator Settings
#
# Configure the data generator settings
# This specifies how to augment the training samples
# See the command: "mltk view_audio"
# to get a better idea of how these augmentations affect
# the samples
my_model.datagen = ParallelAudioDataGenerator(
dtype=my_model.tflite_converter['inference_input_type'],
frontend_settings=frontend_settings,
cores=0.65, # Adjust this as necessary for your PC setup
debug=False, # Set this to true to enable debugging of the generator
# debug=True,
# cores=1,
max_batches_pending=32, # Adjust this as necessary for your PC setup (smaller -> less RAM)
validation_split= 0.15,
validation_augmentation_enabled=False,
samplewise_center=False,
samplewise_std_normalization=False,
get_batch_function=get_batches_samples,
rescale=None,
unknown_class_percentage=4.0, # Increasing this may help model robustness at the expense of training time
silence_class_percentage=0.2,
offset_range=(0.0,1.0),
trim_threshold_db=20,
noise_colors=None,
#loudness_range=(0.5, 1.0),
# speed_range=(0.9,1.1),
# pitch_range=(0.9,1.1),
# vtlp_range=(0.9,1.1),
bg_noise_range=(0.0,0.4),
bg_noise_dir='_background_noise_', # This is a directory provided by the google speech commands dataset, can also provide an absolute path
)
#################################################
# Model Layout
#
# We use the industry-standard MobileNetV2 model architecture.
#
# It is important to the note the usage of the
# "model" argument.
# Rather than hardcode values, the model is
# used to build the model, e.g.:
# classes=model.n_classes,
#
# This way, the various model properties above can be modified
# without having to re-write this section.
#
def my_model_builder(model: MyModel):
keras_model = MobileNetV2(
input_shape=model.input_shape,
classes=model.n_classes,
alpha=0.15,
last_block_filters=384,
weights=None
)
keras_model.compile(
loss=model.loss,
optimizer=model.optimizer,
metrics=model.metrics
)
return keras_model
my_model.build_model_function = my_model_builder
#################################################
# Audio Classifier Settings
#
# These are additional parameters to include in
# the generated .tflite model file.
# The settings are used by the "classify_audio" command
# or audio_classifier example application.
# NOTE: Corresponding command-line options will override these values.
# Controls the smoothing.
# Drop all inference results that are older than <now> minus window_duration
# Longer durations (in milliseconds) will give a higher confidence that the results are correct, but may miss some commands
my_model.model_parameters['average_window_duration_ms'] = 1000
# Minimum averaged model output threshold for a class to be considered detected, 0-255. Higher values increase precision at the cost of recall
my_model.model_parameters['detection_threshold'] = 145
# Amount of milliseconds to wait after a keyword is detected before detecting new keywords
my_model.model_parameters['suppression_ms'] = 750
# The minimum number of inference results to average when calculating the detection value
my_model.model_parameters['minimum_count'] = 3
# Set the volume gain scaler (i.e. amplitude) to apply to the microphone data. If 0 or omitted, no scaler is applied
my_model.model_parameters['volume_gain'] = 2
# This the amount of time in milliseconds an audio loop takes.
my_model.model_parameters['latency_ms'] = 100
# Enable verbose inference results
my_model.model_parameters['verbose_model_output_logs'] = False
##########################################################################################
# The following allows for running this model training script directly, e.g.:
# python keyword_spotting_mobilenetv2.py
#
# Note that this has the same functionality as:
# mltk train keyword_spotting_mobilenetv2
#
if __name__ == '__main__':
import mltk.core as mltk_core
from mltk import cli
# Setup the CLI logger
cli.get_logger(verbose=False)
# If this is true then this will do a "dry run" of the model testing
# If this is false, then the model will be fully trained
test_mode_enabled = True
# Train the model
# This does the same as issuing the command: mltk train keyword_spotting_mobilenetv2-test --clean
train_results = mltk_core.train_model(my_model, clean=True, test=test_mode_enabled)
print(train_results)
# Evaluate the model against the quantized .h5 (i.e. float32) model
# This does the same as issuing the command: mltk evaluate keyword_spotting_mobilenetv2-test
tflite_eval_results = mltk_core.evaluate_model(my_model, verbose=True, test=test_mode_enabled)
print(tflite_eval_results)
# Profile the model in the simulator
# This does the same as issuing the command: mltk profile keyword_spotting_mobilenetv2-test
profiling_results = mltk_core.profile_model(my_model, test=test_mode_enabled)
print(profiling_results)