Train an Acoustic Classifier

Train an acoustic classifier on speech or noise features.

To see how soundpy implements this, see soundpy.models.builtin.envclassifier_train.

import os, sys
import inspect
currentdir = os.path.dirname(os.path.abspath(
    inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
parparentdir = os.path.dirname(parentdir)
packagedir = os.path.dirname(parparentdir)
sys.path.insert(0, packagedir)

import matplotlib.pyplot as plt
import IPython.display as ipd
package_dir = '../../../'
os.chdir(package_dir)
sp_dir = package_dir

Let’s import soundpy for handling sound

import soundpy as sp

As well as the deep learning component of soundpy

from soundpy import models as spdl

Prepare for Training: Data Organization

Set path relevant for audio data for this example

I will load previously extracted features (from the Speech Commands Dataset) See soundpy.feats.save_features_datasets or soundpy.builtin.envclassifier_feats

feature_extraction_dir = '{}audiodata2/example_feats_models/'.format(sp_dir)+\
    'envclassifier/example_feats_fbank/'

What is in this folder?

feature_extraction_dir = sp.utils.check_dir(feature_extraction_dir)
files = list(feature_extraction_dir.glob('*.*'))
for f in files:
    print(f.name)

Out:

dict_encode.csv
dataset_audio_assignments.csv
test_data_fbank.npy
log_extraction_settings.csv
dataset_audiofiles.csv
train_data_fbank.npy
val_data_fbank.npy
dict_decode.csv
dict_encdodedlabel2audio.csv

The .npy files contain the features themselves, in train, validation, and test datasets:

files = list(feature_extraction_dir.glob('*.npy'))
for f in files:
    print(f.name)

Out:

test_data_fbank.npy
train_data_fbank.npy
val_data_fbank.npy

The .csv files contain information about how the features were extracted

files = list(feature_extraction_dir.glob('*.csv'))
for f in files:
    print(f.name)

Out:

dict_encode.csv
dataset_audio_assignments.csv
log_extraction_settings.csv
dataset_audiofiles.csv
dict_decode.csv
dict_encdodedlabel2audio.csv

We’ll have a look at which features were extracted and other settings:

feat_settings = sp.utils.load_dict(
    feature_extraction_dir.joinpath('log_extraction_settings.csv'))
for key, value in feat_settings.items():
    print(key, ' --> ', value)

Out:

dur_sec  -->  1
feature_type  -->  fbank
feat_type  -->  fbank
complex_vals  -->  False
sr  -->  22050
num_feats  -->  40
n_fft  -->  441
win_size_ms  -->  20
frame_length  -->  441
percent_overlap  -->  0.5
frames_per_sample  -->
labeled_data  -->  True
visualize  -->  True
input_shape  -->  (101, 41)
desired_shape  -->  (101, 40)
use_librosa  -->  True
center  -->  True
mode  -->  reflect
subsection_data  -->  False
divide_factor  -->  5
kwargs  -->  {'sr': 22050, 'feature_type': 'fbank', 'win_size_ms': 20, 'percent_overlap': 0.5, 'num_filters': 40, 'num_mfcc': 40, 'dur_sec': 1, 'center': True}

For more about these settings, see soundpy.feats.save_features_datasets.

We’ll have a look at the audio files that were assigned to the train, val, and test datasets.

audio_datasets = sp.utils.load_dict(
    feature_extraction_dir.joinpath('dataset_audiofiles.csv'))
count = 0
for key, value in audio_datasets.items():
    print(key, ' --> ', value)
    count += 1
    if count > 5:
        break

Out:

Dictionary values or size is too large.
Maxing out field size limit for loading this dictionary:
../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/dataset_audiofiles.csv

The new field size limit is:
9223372036854775807
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Built-In Functionality: soundpy does everything for you

For more about this function, see soundpy.models.builtin.envclassifier_train.

model_dir, history = spdl.envclassifier_train(
    feature_extraction_dir = feature_extraction_dir,
    epochs = 10,
    patience = 5)

Out:

The model will be trained 10 epochs per training session.
Total possible epochs: 10



TRAINING SESSION  1
Training on:
../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/train_data_fbank.npy

<FlatMapDataset shapes: ((1, 101, 40, 1), (1,)), types: (tf.float64, tf.float64)>
<FlatMapDataset shapes: ((1, 101, 40, 1), (1,)), types: (tf.float64, tf.float64)>
<FlatMapDataset shapes: ((1, 101, 40, 1), (1,)), types: (tf.float64, tf.float64)>
Epoch 1/10

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Epoch 00001: val_loss improved from inf to 0.40003, saving model to ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms/model_cnn_classifier_9m3d13h26m22s320ms.h5

7433/7433 [==============================] - 13s 2ms/step - loss: 0.6625 - accuracy: 0.7475 - val_loss: 0.4000 - val_accuracy: 0.8704
Epoch 2/10

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Epoch 00002: val_loss improved from 0.40003 to 0.35578, saving model to ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms/model_cnn_classifier_9m3d13h26m22s320ms.h5

7433/7433 [==============================] - 13s 2ms/step - loss: 0.4150 - accuracy: 0.8456 - val_loss: 0.3558 - val_accuracy: 0.8877
Epoch 3/10

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Epoch 00003: val_loss improved from 0.35578 to 0.35275, saving model to ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms/model_cnn_classifier_9m3d13h26m22s320ms.h5

7433/7433 [==============================] - 13s 2ms/step - loss: 0.3585 - accuracy: 0.8733 - val_loss: 0.3527 - val_accuracy: 0.8963
Epoch 4/10

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Epoch 00004: val_loss improved from 0.35275 to 0.35026, saving model to ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms/model_cnn_classifier_9m3d13h26m22s320ms.h5

7433/7433 [==============================] - 14s 2ms/step - loss: 0.3257 - accuracy: 0.8847 - val_loss: 0.3503 - val_accuracy: 0.8931
Epoch 5/10

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Epoch 00005: val_loss did not improve from 0.35026

7433/7433 [==============================] - 14s 2ms/step - loss: 0.2927 - accuracy: 0.8984 - val_loss: 0.3726 - val_accuracy: 0.8888
Epoch 6/10

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Epoch 00006: val_loss did not improve from 0.35026

7433/7433 [==============================] - 14s 2ms/step - loss: 0.2735 - accuracy: 0.9085 - val_loss: 0.3748 - val_accuracy: 0.8898
Epoch 7/10

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Epoch 00007: val_loss improved from 0.35026 to 0.30664, saving model to ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms/model_cnn_classifier_9m3d13h26m22s320ms.h5

7433/7433 [==============================] - 14s 2ms/step - loss: 0.2549 - accuracy: 0.9142 - val_loss: 0.3066 - val_accuracy: 0.8985
Epoch 8/10

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Epoch 00008: val_loss did not improve from 0.30664

7433/7433 [==============================] - 13s 2ms/step - loss: 0.2391 - accuracy: 0.9177 - val_loss: 0.3397 - val_accuracy: 0.8898
Epoch 9/10

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Epoch 00009: val_loss did not improve from 0.30664

7433/7433 [==============================] - 13s 2ms/step - loss: 0.2173 - accuracy: 0.9251 - val_loss: 0.3421 - val_accuracy: 0.9104
Epoch 10/10

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Epoch 00010: val_loss did not improve from 0.30664

7433/7433 [==============================] - 14s 2ms/step - loss: 0.2105 - accuracy: 0.9269 - val_loss: 0.4356 - val_accuracy: 0.8909

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500/500 [==============================] - 0s 919us/step - loss: 0.5479 - accuracy: 0.8640
Test loss: 0.5479004979133606
Test accuracy: 0.8640000224113464

Finished training the model. The model and associated files can be found here:
../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms

Where the model and logs are located:

model_dir

Out:

PosixPath('../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms')

Let’s plot how the model performed (on this mini dataset)

import matplotlib.pyplot as plt
plt.clf()
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper right')
plt.savefig('accuracy.png')