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In this colab notebook, you'll learn how to use the TensorFlow Lite Model Maker to train a custom audio classification model.
The Model Maker library uses transfer learning to simplify the process of training a TensorFlow Lite model using a custom dataset. Retraining a TensorFlow Lite model with your own custom dataset reduces the amount of training data and time required.
It is part of the Codelab to Customize an Audio model and deploy on Android.
You'll use a custom birds dataset and export a TFLite model that can be used on a phone, a TensorFlow.JS model that can be used for inference in the browser and also a SavedModel version that you can use for serving.
Intalling dependencies
sudo apt -y install libportaudio2pip install tflite-model-maker
Import TensorFlow, Model Maker and other libraries
Among the dependencies that are needed, you'll use TensorFlow and Model Maker. Aside those, the others are for audio manipulation, playing and visualizations.
import tensorflow as tf
import tflite_model_maker as mm
from tflite_model_maker import audio_classifier
import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import itertools
import glob
import random
from IPython.display import Audio, Image
from scipy.io import wavfile
print(f"TensorFlow Version: {tf.__version__}")
print(f"Model Maker Version: {mm.__version__}")
TensorFlow Version: 2.9.1 Model Maker Version: 0.4.0
The Birds dataset
The Birds dataset is an education collection of 5 types of birds songs:
- White-breasted Wood-Wren
- House Sparrow
- Red Crossbill
- Chestnut-crowned Antpitta
- Azara's Spinetail
The original audio came from Xeno-canto which is a website dedicated to sharing bird sounds from all over the world.
Let's start by downloading the data.
birds_dataset_folder = tf.keras.utils.get_file('birds_dataset.zip',
'https://storage.googleapis.com/laurencemoroney-blog.appspot.com/birds_dataset.zip',
cache_dir='./',
cache_subdir='dataset',
extract=True)
Downloading data from https://storage.googleapis.com/laurencemoroney-blog.appspot.com/birds_dataset.zip 343680986/343680986 [==============================] - 3s 0us/step
Explore the data
The audios are already split in train and test folders. Inside each split folder, there's one folder for each bird, using their bird_code as name.
The audios are all mono and with 16kHz sample rate.
For more information about each file, you can read the metadata.csv file. It contains all the files authors, lincenses and some more information. You won't need to read it yourself on this tutorial.
# @title [Run this] Util functions and data structures.
data_dir = './dataset/small_birds_dataset'
bird_code_to_name = {
'wbwwre1': 'White-breasted Wood-Wren',
'houspa': 'House Sparrow',
'redcro': 'Red Crossbill',
'chcant2': 'Chestnut-crowned Antpitta',
'azaspi1': "Azara's Spinetail",
}
birds_images = {
'wbwwre1': 'https://upload.wikimedia.org/wikipedia/commons/thumb/2/22/Henicorhina_leucosticta_%28Cucarachero_pechiblanco%29_-_Juvenil_%2814037225664%29.jpg/640px-Henicorhina_leucosticta_%28Cucarachero_pechiblanco%29_-_Juvenil_%2814037225664%29.jpg', # Alejandro Bayer Tamayo from Armenia, Colombia
'houspa': 'https://upload.wikimedia.org/wikipedia/commons/thumb/5/52/House_Sparrow%2C_England_-_May_09.jpg/571px-House_Sparrow%2C_England_-_May_09.jpg', # Diliff
'redcro': 'https://upload.wikimedia.org/wikipedia/commons/thumb/4/49/Red_Crossbills_%28Male%29.jpg/640px-Red_Crossbills_%28Male%29.jpg', # Elaine R. Wilson, www.naturespicsonline.com
'chcant2': 'https://upload.wikimedia.org/wikipedia/commons/thumb/6/67/Chestnut-crowned_antpitta_%2846933264335%29.jpg/640px-Chestnut-crowned_antpitta_%2846933264335%29.jpg', # Mike's Birds from Riverside, CA, US
'azaspi1': 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b2/Synallaxis_azarae_76608368.jpg/640px-Synallaxis_azarae_76608368.jpg', # https://www.inaturalist.org/photos/76608368
}
test_files = os.path.abspath(os.path.join(data_dir, 'test/*/*.wav'))
def get_random_audio_file():
test_list = glob.glob(test_files)
random_audio_path = random.choice(test_list)
return random_audio_path
def show_bird_data(audio_path):
sample_rate, audio_data = wavfile.read(audio_path, 'rb')
bird_code = audio_path.split('/')[-2]
print(f'Bird name: {bird_code_to_name[bird_code]}')
print(f'Bird code: {bird_code}')
display(Image(birds_images[bird_code]))
plttitle = f'{bird_code_to_name[bird_code]} ({bird_code})'
plt.title(plttitle)
plt.plot(audio_data)
display(Audio(audio_data, rate=sample_rate))
print('functions and data structures created')
functions and data structures created
Playing some audio
To have a better understanding about the data, lets listen to a random audio files from the test split.
random_audio = get_random_audio_file()
show_bird_data(random_audio)
Bird name: Azara's Spinetail Bird code: azaspi1
Training the Model
When using Model Maker for audio, you have to start with a model spec. This is the base model that your new model will extract information to learn about the new classes. It also affects how the dataset will be transformed to respect the models spec parameters like: sample rate, number of channels.
YAMNet is an audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology.
It's input is expected to be at 16kHz and with 1 channel.
You don't need to do any resampling yourself. Model Maker takes care of that for you.
frame_lengthis to decide how long each traininng sample is. in this caase EXPECTED_WAVEFORM_LENGTH * 3sframe_stepsis to decide how far appart are the training samples. In this case, the ith sample will start at EXPECTED_WAVEFORM_LENGTH * 6s after the (i-1)th sample.
The reason to set these values is to work around some limitation in real world dataset.
For example, in the bird dataset, birds don't sing all the time. They sing, rest and sing again, with noises in between. Having a long frame would help capture the singing, but setting it too long will reduce the number of samples for training.
spec = audio_classifier.YamNetSpec(
keep_yamnet_and_custom_heads=True,
frame_step=3 * audio_classifier.YamNetSpec.EXPECTED_WAVEFORM_LENGTH,
frame_length=6 * audio_classifier.YamNetSpec.EXPECTED_WAVEFORM_LENGTH)
INFO:tensorflow:Checkpoints are stored in /tmpfs/tmp/tmpoi1i1xc7
Loading the data
Model Maker has the API to load the data from a folder and have it in the expected format for the model spec.
The train and test split are based on the folders. The validation dataset will be created as 20% of the train split.
train_data = audio_classifier.DataLoader.from_folder(
spec, os.path.join(data_dir, 'train'), cache=True)
train_data, validation_data = train_data.split(0.8)
test_data = audio_classifier.DataLoader.from_folder(
spec, os.path.join(data_dir, 'test'), cache=True)
Training the model
the audio_classifier has the create method that creates a model and already start training it.
You can customize many parameterss, for more information you can read more details in the documentation.
On this first try you'll use all the default configurations and train for 100 epochs.
batch_size = 128
epochs = 100
print('Training the model')
model = audio_classifier.create(
train_data,
spec,
validation_data,
batch_size=batch_size,
epochs=epochs)
Training the model
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
classification_head (Dense) (None, 5) 5125
=================================================================
Total params: 5,125
Trainable params: 5,125
Non-trainable params: 0
_________________________________________________________________
Epoch 1/100
22/22 [==============================] - 19s 766ms/step - loss: 1.6695 - acc: 0.2116 - val_loss: 1.4794 - val_acc: 0.3480
Epoch 2/100
22/22 [==============================] - 0s 10ms/step - loss: 1.4015 - acc: 0.3965 - val_loss: 1.2727 - val_acc: 0.6364
Epoch 3/100
22/22 [==============================] - 0s 11ms/step - loss: 1.2427 - acc: 0.5195 - val_loss: 1.1172 - val_acc: 0.7131
Epoch 4/100
22/22 [==============================] - 0s 11ms/step - loss: 1.1184 - acc: 0.6103 - val_loss: 1.0111 - val_acc: 0.7543
Epoch 5/100
22/22 [==============================] - 0s 11ms/step - loss: 1.0331 - acc: 0.6629 - val_loss: 0.9327 - val_acc: 0.7699
Epoch 6/100
22/22 [==============================] - 0s 12ms/step - loss: 0.9552 - acc: 0.6911 - val_loss: 0.8741 - val_acc: 0.7770
Epoch 7/100
22/22 [==============================] - 0s 11ms/step - loss: 0.9038 - acc: 0.7163 - val_loss: 0.8244 - val_acc: 0.7827
Epoch 8/100
22/22 [==============================] - 0s 13ms/step - loss: 0.8673 - acc: 0.7199 - val_loss: 0.7859 - val_acc: 0.7940
Epoch 9/100
22/22 [==============================] - 0s 15ms/step - loss: 0.8205 - acc: 0.7354 - val_loss: 0.7507 - val_acc: 0.8026
Epoch 10/100
22/22 [==============================] - 0s 15ms/step - loss: 0.7849 - acc: 0.7523 - val_loss: 0.7199 - val_acc: 0.8111
Epoch 11/100
22/22 [==============================] - 0s 11ms/step - loss: 0.7608 - acc: 0.7545 - val_loss: 0.6991 - val_acc: 0.8168
Epoch 12/100
22/22 [==============================] - 0s 11ms/step - loss: 0.7405 - acc: 0.7556 - val_loss: 0.6718 - val_acc: 0.8253
Epoch 13/100
22/22 [==============================] - 0s 11ms/step - loss: 0.7047 - acc: 0.7826 - val_loss: 0.6537 - val_acc: 0.8281
Epoch 14/100
22/22 [==============================] - 0s 11ms/step - loss: 0.6927 - acc: 0.7725 - val_loss: 0.6376 - val_acc: 0.8352
Epoch 15/100
22/22 [==============================] - 0s 11ms/step - loss: 0.6727 - acc: 0.7891 - val_loss: 0.6181 - val_acc: 0.8438
Epoch 16/100
22/22 [==============================] - 0s 12ms/step - loss: 0.6572 - acc: 0.7891 - val_loss: 0.6035 - val_acc: 0.8494
Epoch 17/100
22/22 [==============================] - 0s 13ms/step - loss: 0.6375 - acc: 0.7963 - val_loss: 0.5917 - val_acc: 0.8594
Epoch 18/100
22/22 [==============================] - 0s 13ms/step - loss: 0.6294 - acc: 0.7996 - val_loss: 0.5801 - val_acc: 0.8636
Epoch 19/100
22/22 [==============================] - 0s 11ms/step - loss: 0.6112 - acc: 0.8014 - val_loss: 0.5646 - val_acc: 0.8665
Epoch 20/100
22/22 [==============================] - 0s 13ms/step - loss: 0.5948 - acc: 0.8143 - val_loss: 0.5581 - val_acc: 0.8636
Epoch 21/100
22/22 [==============================] - 0s 11ms/step - loss: 0.5948 - acc: 0.8089 - val_loss: 0.5465 - val_acc: 0.8707
Epoch 22/100
22/22 [==============================] - 0s 12ms/step - loss: 0.5743 - acc: 0.8147 - val_loss: 0.5408 - val_acc: 0.8750
Epoch 23/100
22/22 [==============================] - 0s 11ms/step - loss: 0.5750 - acc: 0.8046 - val_loss: 0.5306 - val_acc: 0.8750
Epoch 24/100
22/22 [==============================] - 0s 11ms/step - loss: 0.5711 - acc: 0.8194 - val_loss: 0.5222 - val_acc: 0.8821
Epoch 25/100
22/22 [==============================] - 0s 12ms/step - loss: 0.5501 - acc: 0.8176 - val_loss: 0.5131 - val_acc: 0.8878
Epoch 26/100
22/22 [==============================] - 0s 11ms/step - loss: 0.5495 - acc: 0.8230 - val_loss: 0.5071 - val_acc: 0.8892
Epoch 27/100
22/22 [==============================] - 0s 11ms/step - loss: 0.5368 - acc: 0.8241 - val_loss: 0.5030 - val_acc: 0.8935
Epoch 28/100
22/22 [==============================] - 0s 12ms/step - loss: 0.5300 - acc: 0.8320 - val_loss: 0.4943 - val_acc: 0.8935
Epoch 29/100
22/22 [==============================] - 0s 11ms/step - loss: 0.5201 - acc: 0.8284 - val_loss: 0.4883 - val_acc: 0.8977
Epoch 30/100
22/22 [==============================] - 0s 11ms/step - loss: 0.5212 - acc: 0.8320 - val_loss: 0.4809 - val_acc: 0.8991
Epoch 31/100
22/22 [==============================] - 0s 11ms/step - loss: 0.5143 - acc: 0.8353 - val_loss: 0.4771 - val_acc: 0.8977
Epoch 32/100
22/22 [==============================] - 0s 13ms/step - loss: 0.5020 - acc: 0.8356 - val_loss: 0.4752 - val_acc: 0.9006
Epoch 33/100
22/22 [==============================] - 0s 12ms/step - loss: 0.5018 - acc: 0.8291 - val_loss: 0.4671 - val_acc: 0.9006
Epoch 34/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4881 - acc: 0.8381 - val_loss: 0.4632 - val_acc: 0.9020
Epoch 35/100
22/22 [==============================] - 0s 13ms/step - loss: 0.4970 - acc: 0.8324 - val_loss: 0.4606 - val_acc: 0.9020
Epoch 36/100
22/22 [==============================] - 0s 11ms/step - loss: 0.4840 - acc: 0.8396 - val_loss: 0.4540 - val_acc: 0.9077
Epoch 37/100
22/22 [==============================] - 0s 11ms/step - loss: 0.4797 - acc: 0.8468 - val_loss: 0.4514 - val_acc: 0.9062
Epoch 38/100
22/22 [==============================] - 0s 13ms/step - loss: 0.4690 - acc: 0.8417 - val_loss: 0.4490 - val_acc: 0.9091
Epoch 39/100
22/22 [==============================] - 0s 11ms/step - loss: 0.4653 - acc: 0.8493 - val_loss: 0.4456 - val_acc: 0.9077
Epoch 40/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4649 - acc: 0.8472 - val_loss: 0.4381 - val_acc: 0.9105
Epoch 41/100
22/22 [==============================] - 0s 11ms/step - loss: 0.4626 - acc: 0.8464 - val_loss: 0.4377 - val_acc: 0.9119
Epoch 42/100
22/22 [==============================] - 0s 11ms/step - loss: 0.4578 - acc: 0.8461 - val_loss: 0.4350 - val_acc: 0.9134
Epoch 43/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4490 - acc: 0.8551 - val_loss: 0.4310 - val_acc: 0.9134
Epoch 44/100
22/22 [==============================] - 0s 13ms/step - loss: 0.4489 - acc: 0.8500 - val_loss: 0.4288 - val_acc: 0.9105
Epoch 45/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4499 - acc: 0.8529 - val_loss: 0.4273 - val_acc: 0.9134
Epoch 46/100
22/22 [==============================] - 0s 11ms/step - loss: 0.4456 - acc: 0.8508 - val_loss: 0.4222 - val_acc: 0.9119
Epoch 47/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4388 - acc: 0.8522 - val_loss: 0.4194 - val_acc: 0.9148
Epoch 48/100
22/22 [==============================] - 0s 11ms/step - loss: 0.4332 - acc: 0.8601 - val_loss: 0.4164 - val_acc: 0.9176
Epoch 49/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4245 - acc: 0.8619 - val_loss: 0.4142 - val_acc: 0.9148
Epoch 50/100
22/22 [==============================] - 0s 13ms/step - loss: 0.4307 - acc: 0.8569 - val_loss: 0.4135 - val_acc: 0.9119
Epoch 51/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4236 - acc: 0.8576 - val_loss: 0.4118 - val_acc: 0.9148
Epoch 52/100
22/22 [==============================] - 0s 11ms/step - loss: 0.4194 - acc: 0.8659 - val_loss: 0.4088 - val_acc: 0.9148
Epoch 53/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4220 - acc: 0.8619 - val_loss: 0.4058 - val_acc: 0.9148
Epoch 54/100
22/22 [==============================] - 0s 11ms/step - loss: 0.4242 - acc: 0.8594 - val_loss: 0.4038 - val_acc: 0.9148
Epoch 55/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4156 - acc: 0.8630 - val_loss: 0.4047 - val_acc: 0.9134
Epoch 56/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4113 - acc: 0.8731 - val_loss: 0.3991 - val_acc: 0.9134
Epoch 57/100
22/22 [==============================] - 0s 11ms/step - loss: 0.4056 - acc: 0.8641 - val_loss: 0.3974 - val_acc: 0.9134
Epoch 58/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3994 - acc: 0.8684 - val_loss: 0.3969 - val_acc: 0.9119
Epoch 59/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4112 - acc: 0.8637 - val_loss: 0.3944 - val_acc: 0.9105
Epoch 60/100
22/22 [==============================] - 0s 12ms/step - loss: 0.4077 - acc: 0.8616 - val_loss: 0.3954 - val_acc: 0.9134
Epoch 61/100
22/22 [==============================] - 0s 10ms/step - loss: 0.3998 - acc: 0.8612 - val_loss: 0.3927 - val_acc: 0.9119
Epoch 62/100
22/22 [==============================] - 0s 11ms/step - loss: 0.4066 - acc: 0.8580 - val_loss: 0.3913 - val_acc: 0.9105
Epoch 63/100
22/22 [==============================] - 0s 10ms/step - loss: 0.3976 - acc: 0.8634 - val_loss: 0.3891 - val_acc: 0.9091
Epoch 64/100
22/22 [==============================] - 0s 12ms/step - loss: 0.3954 - acc: 0.8695 - val_loss: 0.3877 - val_acc: 0.9119
Epoch 65/100
22/22 [==============================] - 0s 10ms/step - loss: 0.3987 - acc: 0.8688 - val_loss: 0.3838 - val_acc: 0.9119
Epoch 66/100
22/22 [==============================] - 0s 12ms/step - loss: 0.3954 - acc: 0.8695 - val_loss: 0.3846 - val_acc: 0.9119
Epoch 67/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3840 - acc: 0.8738 - val_loss: 0.3811 - val_acc: 0.9091
Epoch 68/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3800 - acc: 0.8745 - val_loss: 0.3844 - val_acc: 0.9091
Epoch 69/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3878 - acc: 0.8767 - val_loss: 0.3812 - val_acc: 0.9105
Epoch 70/100
22/22 [==============================] - 0s 12ms/step - loss: 0.3946 - acc: 0.8663 - val_loss: 0.3779 - val_acc: 0.9162
Epoch 71/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3811 - acc: 0.8738 - val_loss: 0.3786 - val_acc: 0.9148
Epoch 72/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3788 - acc: 0.8782 - val_loss: 0.3783 - val_acc: 0.9134
Epoch 73/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3841 - acc: 0.8688 - val_loss: 0.3754 - val_acc: 0.9176
Epoch 74/100
22/22 [==============================] - 0s 12ms/step - loss: 0.3762 - acc: 0.8742 - val_loss: 0.3741 - val_acc: 0.9162
Epoch 75/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3696 - acc: 0.8749 - val_loss: 0.3768 - val_acc: 0.9134
Epoch 76/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3773 - acc: 0.8727 - val_loss: 0.3751 - val_acc: 0.9148
Epoch 77/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3674 - acc: 0.8767 - val_loss: 0.3702 - val_acc: 0.9176
Epoch 78/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3723 - acc: 0.8745 - val_loss: 0.3735 - val_acc: 0.9119
Epoch 79/100
22/22 [==============================] - 0s 13ms/step - loss: 0.3613 - acc: 0.8778 - val_loss: 0.3705 - val_acc: 0.9134
Epoch 80/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3682 - acc: 0.8724 - val_loss: 0.3705 - val_acc: 0.9148
Epoch 81/100
22/22 [==============================] - 0s 12ms/step - loss: 0.3709 - acc: 0.8742 - val_loss: 0.3670 - val_acc: 0.9134
Epoch 82/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3691 - acc: 0.8706 - val_loss: 0.3676 - val_acc: 0.9134
Epoch 83/100
22/22 [==============================] - 0s 12ms/step - loss: 0.3658 - acc: 0.8760 - val_loss: 0.3657 - val_acc: 0.9148
Epoch 84/100
22/22 [==============================] - 0s 12ms/step - loss: 0.3602 - acc: 0.8832 - val_loss: 0.3665 - val_acc: 0.9148
Epoch 85/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3636 - acc: 0.8709 - val_loss: 0.3621 - val_acc: 0.9162
Epoch 86/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3572 - acc: 0.8767 - val_loss: 0.3612 - val_acc: 0.9205
Epoch 87/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3549 - acc: 0.8818 - val_loss: 0.3624 - val_acc: 0.9162
Epoch 88/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3518 - acc: 0.8850 - val_loss: 0.3616 - val_acc: 0.9162
Epoch 89/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3533 - acc: 0.8810 - val_loss: 0.3629 - val_acc: 0.9176
Epoch 90/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3437 - acc: 0.8846 - val_loss: 0.3625 - val_acc: 0.9119
Epoch 91/100
22/22 [==============================] - 0s 12ms/step - loss: 0.3585 - acc: 0.8800 - val_loss: 0.3588 - val_acc: 0.9190
Epoch 92/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3515 - acc: 0.8792 - val_loss: 0.3621 - val_acc: 0.9148
Epoch 93/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3524 - acc: 0.8864 - val_loss: 0.3629 - val_acc: 0.9119
Epoch 94/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3466 - acc: 0.8760 - val_loss: 0.3570 - val_acc: 0.9176
Epoch 95/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3451 - acc: 0.8843 - val_loss: 0.3581 - val_acc: 0.9148
Epoch 96/100
22/22 [==============================] - 0s 11ms/step - loss: 0.3458 - acc: 0.8836 - val_loss: 0.3583 - val_acc: 0.9134
Epoch 97/100
22/22 [==============================] - 0s 12ms/step - loss: 0.3481 - acc: 0.8803 - val_loss: 0.3593 - val_acc: 0.9134
Epoch 98/100
22/22 [==============================] - 0s 12ms/step - loss: 0.3455 - acc: 0.8818 - val_loss: 0.3560 - val_acc: 0.9134
Epoch 99/100
22/22 [==============================] - 0s 10ms/step - loss: 0.3467 - acc: 0.8836 - val_loss: 0.3572 - val_acc: 0.9119
Epoch 100/100
22/22 [==============================] - 0s 12ms/step - loss: 0.3418 - acc: 0.8893 - val_loss: 0.3577 - val_acc: 0.9134
The accuracy looks good but it's important to run the evaluation step on the test data and vefify your model achieved good results on unseed data.
print('Evaluating the model')
model.evaluate(test_data)
Evaluating the model 28/28 [==============================] - 5s 143ms/step - loss: 0.8298 - acc: 0.7646 [0.8297802209854126, 0.764638364315033]
Understanding your model
When training a classifier, it's useful to see the confusion matrix. The confusion matrix gives you detailed knowledge of how your classifier is performing on test data.
Model Maker already creates the confusion matrix for you.
def show_confusion_matrix(confusion, test_labels):
"""Compute confusion matrix and normalize."""
confusion_normalized = confusion.astype("float") / confusion.sum(axis=1)
axis_labels = test_labels
ax = sns.heatmap(
confusion_normalized, xticklabels=axis_labels, yticklabels=axis_labels,
cmap='Blues', annot=True, fmt='.2f', square=True)
plt.title("Confusion matrix")
plt.ylabel("True label")
plt.xlabel("Predicted label")
confusion_matrix = model.confusion_matrix(test_data)
show_confusion_matrix(confusion_matrix.numpy(), test_data.index_to_label)
1/1 [==============================] - 0s 159ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 37ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 37ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 20ms/step 1/1 [==============================] - 0s 20ms/step 1/1 [==============================] - 0s 40ms/step
Testing the model [Optional]
You can try the model on a sample audio from the test dataset just to see the results.
First you get the serving model.
serving_model = model.create_serving_model()
print(f'Model\'s input shape and type: {serving_model.inputs}')
print(f'Model\'s output shape and type: {serving_model.outputs}')
Model's input shape and type: [<KerasTensor: shape=(None, 15600) dtype=float32 (created by layer 'audio')>] Model's output shape and type: [<KerasTensor: shape=(None, 521) dtype=float32 (created by layer 'keras_layer')>, <KerasTensor: shape=(None, 5) dtype=float32 (created by layer 'sequential')>]
Coming back to the random audio you loaded earlier
# if you want to try another file just uncoment the line below
random_audio = get_random_audio_file()
show_bird_data(random_audio)
Bird name: White-breasted Wood-Wren Bird code: wbwwre1
The model created has a fixed input window.
For a given audio file, you'll have to split it in windows of data of the expected size. The last window might need to be filled with zeros.
sample_rate, audio_data = wavfile.read(random_audio, 'rb')
audio_data = np.array(audio_data) / tf.int16.max
input_size = serving_model.input_shape[1]
splitted_audio_data = tf.signal.frame(audio_data, input_size, input_size, pad_end=True, pad_value=0)
print(f'Test audio path: {random_audio}')
print(f'Original size of the audio data: {len(audio_data)}')
print(f'Number of windows for inference: {len(splitted_audio_data)}')
Test audio path: /tmpfs/src/temp/tensorflow/lite/g3doc/models/modify/model_maker/dataset/small_birds_dataset/test/wbwwre1/XC525148.wav Original size of the audio data: 291936 Number of windows for inference: 19
You'll loop over all the splitted audio and apply the model for each one of them.
The model you've just trained has 2 outputs: The original YAMNet's output and the one you've just trained. This is important because the real world environment is more complicated than just bird sounds. You can use the YAMNet's output to filter out non relevant audio, for example, on the birds use case, if YAMNet is not classifying Birds or Animals, this might show that the output from your model might have an irrelevant classification.
Below both outpus are printed to make it easier to understand their relation. Most of the mistakes that your model make are when YAMNet's prediction is not related to your domain (eg: birds).
print(random_audio)
results = []
print('Result of the window ith: your model class -> score, (spec class -> score)')
for i, data in enumerate(splitted_audio_data):
yamnet_output, inference = serving_model(data)
results.append(inference[0].numpy())
result_index = tf.argmax(inference[0])
spec_result_index = tf.argmax(yamnet_output[0])
t = spec._yamnet_labels()[spec_result_index]
result_str = f'Result of the window {i}: ' \
f'\t{test_data.index_to_label[result_index]} -> {inference[0][result_index].numpy():.3f}, ' \
f'\t({spec._yamnet_labels()[spec_result_index]} -> {yamnet_output[0][spec_result_index]:.3f})'
print(result_str)
results_np = np.array(results)
mean_results = results_np.mean(axis=0)
result_index = mean_results.argmax()
print(f'Mean result: {test_data.index_to_label[result_index]} -> {mean_results[result_index]}')
/tmpfs/src/temp/tensorflow/lite/g3doc/models/modify/model_maker/dataset/small_birds_dataset/test/wbwwre1/XC525148.wav Result of the window ith: your model class -> score, (spec class -> score) Result of the window 0: wbwwre1 -> 0.854, (Cricket -> 0.777) Result of the window 1: wbwwre1 -> 0.903, (Cricket -> 0.931) Result of the window 2: wbwwre1 -> 0.889, (Bird -> 0.903) Result of the window 3: wbwwre1 -> 0.983, (Animal -> 0.822) Result of the window 4: houspa -> 0.402, (Animal -> 0.446) Result of the window 5: wbwwre1 -> 0.859, (Animal -> 0.963) Result of the window 6: wbwwre1 -> 0.990, (Insect -> 0.824) Result of the window 7: wbwwre1 -> 0.786, (Cricket -> 0.817) Result of the window 8: wbwwre1 -> 0.966, (Cricket -> 0.556) Result of the window 9: wbwwre1 -> 0.978, (Animal -> 0.988) Result of the window 10: wbwwre1 -> 0.741, (Animal -> 0.606) Result of the window 11: wbwwre1 -> 0.972, (Insect -> 0.575) Result of the window 12: wbwwre1 -> 0.848, (Insect -> 0.358) Result of the window 13: wbwwre1 -> 0.888, (Cricket -> 0.639) Result of the window 14: azaspi1 -> 0.460, (Silence -> 1.000) Result of the window 15: redcro -> 0.466, (Silence -> 1.000) Result of the window 16: wbwwre1 -> 0.344, (Speech -> 0.231) Result of the window 17: chcant2 -> 0.945, (Silence -> 1.000) Result of the window 18: chcant2 -> 0.709, (Silence -> 0.950) Mean result: wbwwre1 -> 0.6498250365257263
Exporting the model
The last step is exporting your model to be used on embedded devices or on the browser.
The export method export both formats for you.
models_path = './birds_models'
print(f'Exporing the TFLite model to {models_path}')
model.export(models_path, tflite_filename='my_birds_model.tflite')
Exporing the TFLite model to ./birds_models INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpxywpi1ab/assets INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpxywpi1ab/assets 2022-08-09 17:10:24.810732: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format. 2022-08-09 17:10:24.810789: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency. INFO:tensorflow:TensorFlow Lite model exported successfully: ./birds_models/my_birds_model.tflite INFO:tensorflow:TensorFlow Lite model exported successfully: ./birds_models/my_birds_model.tflite
You can also export the SavedModel version for serving or using on a Python environment.
model.export(models_path, export_format=[mm.ExportFormat.SAVED_MODEL, mm.ExportFormat.LABEL])
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model. WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model. INFO:tensorflow:Assets written to: ./birds_models/saved_model/assets INFO:tensorflow:Assets written to: ./birds_models/saved_model/assets INFO:tensorflow:Saving labels in ./birds_models/labels.txt INFO:tensorflow:Saving labels in ./birds_models/labels.txt
Next Steps
You did it.
Now your new model can be deployed on mobile devices using TFLite AudioClassifier Task API.
You can also try the same process with your own data with different classes and here is the documentation for Model Maker for Audio Classification.