.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_plot_extract_augment_train_classifier.py:
==================================================
Extract, Augment, and Train an Acoustic Classifier
==================================================
Extract and augment features as an acoustic classifier is trained on speech.
To see how soundpy implements this, see `soundpy.models.builtin.envclassifier_extract_train`.
.. code-block:: default
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
.. code-block:: default
import soundpy as sp
As well as the deep learning component of soundpy
.. code-block:: default
from soundpy import models as spdl
Prepare for Training: Data Organization
=======================================
I will use a sample speech commands data set:
Designate path relevant for accessing audiodata
.. code-block:: default
data_dir = '{}../mini-audio-datasets/speech_commands/'.format(sp_dir)
Setup a Feature Settings Dictionary
-----------------------------------
.. code-block:: default
feature_type = 'fbank'
num_filters = 40
rate_of_change = False
rate_of_acceleration = False
dur_sec = 1
win_size_ms = 25
percent_overlap = 0.5
sr = 22050
fft_bins = None
num_mfcc = None
real_signal = True
get_feats_kwargs = dict(feature_type = feature_type,
sr = sr,
dur_sec = dur_sec,
win_size_ms = win_size_ms,
percent_overlap = percent_overlap,
fft_bins = fft_bins,
num_filters = num_filters,
num_mfcc = num_mfcc,
rate_of_change = rate_of_change,
rate_of_acceleration = rate_of_acceleration,
real_signal = real_signal)
Setup an Augmentation Dictionary
--------------------------------
This will apply augmentations at random at each epoch.
.. code-block:: default
augmentation_all = dict([('add_white_noise',True),
('speed_decrease', True),
('speed_increase', True),
('pitch_decrease', True),
('pitch_increase', True),
('harmonic_distortion', True),
('vtlp', True)
])
see the default values for these augmentations
.. code-block:: default
augment_settings_dict = {}
for key in augmentation_all.keys():
augment_settings_dict[key] = sp.augment.get_augmentation_settings_dict(key)
for key, value in augment_settings_dict.items():
print(key, ' : ', value)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
add_white_noise : {'noise_level': 0.01, 'snr': 10, 'random_seed': None}
speed_decrease : {'perc': 0.15}
speed_increase : {'perc': 0.15}
pitch_decrease : {'num_semitones': 2}
pitch_increase : {'num_semitones': 2}
harmonic_distortion : {}
vtlp : {'a': (0.8, 1.2), 'random_seed': None, 'oversize_factor': 16, 'win_size_ms': 50, 'percent_overlap': 0.5, 'bilinear_warp': True, 'real_signal': True, 'fft_bins': 1024, 'window': 'hann', 'zeropad': True, 'expected_shape': None, 'visualize': False}
Adjust Augmentation Defaults
----------------------------
Adjust Add White Noise
~~~~~~~~~~~~~~~~~~~~~~
I want the SNR of the white noise to vary between several:
SNR 10, 15, and 20.
.. code-block:: default
augment_settings_dict['add_white_noise']['snr'] = [10,15,20]
Adjust Pitch Decrease
~~~~~~~~~~~~~~~~~~~~~
I found the pitch changes too exaggerated, so I will
set those to 1 instead of 2 semitones.
.. code-block:: default
augment_settings_dict['pitch_decrease']['num_semitones'] = 1
Adjust Pitch Increase
~~~~~~~~~~~~~~~~~~~~~
.. code-block:: default
augment_settings_dict['pitch_increase']['num_semitones'] = 1
Adjust Speed Decrease
~~~~~~~~~~~~~~~~~~~~~
.. code-block:: default
augment_settings_dict['speed_decrease']['perc'] = 0.1
Adjust Speed Increase
~~~~~~~~~~~~~~~~~~~~~
.. code-block:: default
augment_settings_dict['speed_increase']['perc'] = 0.1
Update an Augmentation Dictionary
---------------------------------
We'll include in the dictionary the settings we want for augmentations:
.. code-block:: default
augmentation_all.update(
dict(augment_settings_dict = augment_settings_dict))
Train the Model
===============
Note: disregard the warning:
WARNING: Only the power spectrum of the VTLP augmented signal can be returned due to resizing the augmentation from (56, 4401) to (79, 276)
This is due to the hyper frequency resolution applied to the audio during
vocal-tract length perturbation, and then deresolution to bring to correct size.
The current implementation applies the deresolution to the power spectrum rather than
directly to the STFT.
.. code-block:: default
model_dir, history = spdl.envclassifier_extract_train(
model_name = 'augment_builtin_speechcommands',
audiodata_path = data_dir,
augment_dict = augmentation_all,
labeled_data = True,
batch_size = 1,
epochs = 50,
patience = 5,
visualize = True,
vis_every_n_items = 1,
**get_feats_kwargs)
.. image:: /auto_examples/images/sphx_glr_plot_extract_augment_train_classifier_001.png
:alt: "RIGHT" FBANK Aug: pitchdecrease1semitones-
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
/home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/files.py:352: UserWarning: Some files did not match those acceptable by this program. (i.e. non-audio files) The number of files not included: 4
warnings.warn(message)
Extracting validation data for use in training:
8% through val fbank feature extraction
16% through val fbank feature extraction
25% through val fbank feature extraction
33% through val fbank feature extraction
41% through val fbank feature extraction
50% through val fbank feature extraction
58% through val fbank feature extraction
66% through val fbank feature extraction
75% through val fbank feature extraction
83% through val fbank feature extraction
91% through val fbank feature extraction
100% through val fbank feature extraction
Features saved at example_feats_models/envclassifer/features_fbank_9m21d21h17m39s685ms/val_data.npy
8% through test fbank feature extraction
16% through test fbank feature extraction
25% through test fbank feature extraction
33% through test fbank feature extraction
41% through test fbank feature extraction
50% through test fbank feature extraction
58% through test fbank feature extraction
66% through test fbank feature extraction
75% through test fbank feature extraction
83% through test fbank feature extraction
91% through test fbank feature extraction
100% through test fbank feature extraction
Features saved at example_feats_models/envclassifer/features_fbank_9m21d21h17m39s685ms/test_data.npy
/home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/feats.py:1027: UserWarning:
Warning: voice-activity-detection works best with sample rates above 44100 Hz. Current `sr` set at 22050.
warnings.warn(msg)
/home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/dsp.py:2782: UserWarning:
Warning: VAD works best with sample rates above 44100 Hz.
warnings.warn(msg)
/home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/dsp.py:769: UserWarning:
Warning: `soundpy.dsp.clip_at_zero` found no samples close to zero. Clipping was not applied.
warnings.warn(msg)
/home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/augment.py:322: UserWarning:
WARNING: Only the power spectrum of the VTLP augmented signal can be returned due to resizing the augmentation from (80, 4401) to (79, 276)
warnings.warn(msg)
/home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/feats.py:117: UserWarning: Due to matplotlib using AGG backend, cannot display plot. Therefore, the plot will be saved here: train_feats9m21d21h17m41s118ms.png
warnings.warn(msg)
/home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/feats.py:117: UserWarning: Due to matplotlib using AGG backend, cannot display plot. Therefore, the plot will be saved here: val_feats9m21d21h17m41s280ms.png
warnings.warn(msg)
/home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/feats.py:117: UserWarning: Due to matplotlib using AGG backend, cannot display plot. Therefore, the plot will be saved here: test_feats9m21d21h17m41s431ms.png
warnings.warn(msg)
Shapes of X and y data from the train, val, and test generators:
-------------------------------------------------------------------------------
Augmentation(s) applied (at random):
ADD_WHITE_NOISE
- Settings: {'noise_level': 0.01, 'snr': [10, 15, 20], 'random_seed': None}
SPEED_DECREASE
- Settings: {'perc': 0.1}
SPEED_INCREASE
- Settings: {'perc': 0.1}
PITCH_DECREASE
- Settings: {'num_semitones': 1}
PITCH_INCREASE
- Settings: {'num_semitones': 1}
HARMONIC_DISTORTION
- Settings: {}
VTLP
- Settings: {'a': (0.8, 1.2), 'random_seed': None, 'oversize_factor': 16, 'win_size_ms': 50, 'percent_overlap': 0.5, 'bilinear_warp': True, 'real_signal': True, 'fft_bins': 1024, 'window': 'hann', 'zeropad': True, 'expected_shape': None, 'visualize': False}
-------------------------------------------------------------------------------
Epoch 1/50
/home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/feats.py:1027: UserWarning:
Warning: voice-activity-detection works best with sample rates above 44100 Hz. Current `sr` set at 22050.
warnings.warn(msg)
/home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/dsp.py:2782: UserWarning:
Warning: VAD works best with sample rates above 44100 Hz.
warnings.warn(msg)
/home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/dsp.py:769: UserWarning:
Warning: `soundpy.dsp.clip_at_zero` found no samples close to zero. Clipping was not applied.
warnings.warn(msg)
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No voice activity detected in target signal.
warnings.warn(msg)
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WARNING: Only the power spectrum of the VTLP augmented signal can be returned due to resizing the augmentation from (68, 4401) to (79, 276)
warnings.warn(msg)
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Epoch 00001: val_loss improved from inf to 1.11540, saving model to example_feats_models/envclassifer/features_fbank_9m21d21h17m39s685ms/augment_builtin_speechcommands_fbank/augment_builtin_speechcommands_fbank.h5
126/126 [==============================] - 45s 359ms/step - loss: 1.1010 - accuracy: 0.4206 - val_loss: 1.1154 - val_accuracy: 0.4167
Epoch 2/50
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WARNING: Only the power spectrum of the VTLP augmented signal can be returned due to resizing the augmentation from (63, 4401) to (79, 276)
warnings.warn(msg)
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WARNING: Only the power spectrum of the VTLP augmented signal can be returned due to resizing the augmentation from (74, 4401) to (79, 276)
warnings.warn(msg)
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126/126 [==============================] - ETA: 0s - loss: 0.9645 - accuracy: 0.5556
Epoch 00002: val_loss improved from 1.11540 to 1.07510, saving model to example_feats_models/envclassifer/features_fbank_9m21d21h17m39s685ms/augment_builtin_speechcommands_fbank/augment_builtin_speechcommands_fbank.h5
126/126 [==============================] - 45s 360ms/step - loss: 0.9645 - accuracy: 0.5556 - val_loss: 1.0751 - val_accuracy: 0.4167
Epoch 3/50
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Epoch 00003: val_loss improved from 1.07510 to 0.92848, saving model to example_feats_models/envclassifer/features_fbank_9m21d21h17m39s685ms/augment_builtin_speechcommands_fbank/augment_builtin_speechcommands_fbank.h5
126/126 [==============================] - 45s 361ms/step - loss: 0.6934 - accuracy: 0.7222 - val_loss: 0.9285 - val_accuracy: 0.5833
Epoch 4/50
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126/126 [==============================] - ETA: 0s - loss: 0.5623 - accuracy: 0.7778
Epoch 00004: val_loss did not improve from 0.92848
126/126 [==============================] - 47s 375ms/step - loss: 0.5623 - accuracy: 0.7778 - val_loss: 1.1171 - val_accuracy: 0.5000
Epoch 5/50
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126/126 [==============================] - ETA: 0s - loss: 0.5856 - accuracy: 0.7381
Epoch 00005: val_loss did not improve from 0.92848
126/126 [==============================] - 46s 362ms/step - loss: 0.5856 - accuracy: 0.7381 - val_loss: 1.1389 - val_accuracy: 0.6667
Epoch 6/50
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126/126 [==============================] - ETA: 0s - loss: 0.5291 - accuracy: 0.7857
Epoch 00006: val_loss improved from 0.92848 to 0.88322, saving model to example_feats_models/envclassifer/features_fbank_9m21d21h17m39s685ms/augment_builtin_speechcommands_fbank/augment_builtin_speechcommands_fbank.h5
126/126 [==============================] - 44s 351ms/step - loss: 0.5291 - accuracy: 0.7857 - val_loss: 0.8832 - val_accuracy: 0.5833
Epoch 7/50
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Epoch 00007: val_loss did not improve from 0.88322
126/126 [==============================] - 45s 361ms/step - loss: 0.4933 - accuracy: 0.7857 - val_loss: 0.9700 - val_accuracy: 0.5833
Epoch 8/50
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126/126 [==============================] - ETA: 0s - loss: 0.4383 - accuracy: 0.8175
Epoch 00008: val_loss did not improve from 0.88322
126/126 [==============================] - 46s 362ms/step - loss: 0.4383 - accuracy: 0.8175 - val_loss: 1.1063 - val_accuracy: 0.5833
Epoch 9/50
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126/126 [==============================] - ETA: 0s - loss: 0.4676 - accuracy: 0.8333
Epoch 00009: val_loss did not improve from 0.88322
126/126 [==============================] - 46s 363ms/step - loss: 0.4676 - accuracy: 0.8333 - val_loss: 1.0305 - val_accuracy: 0.6667
Epoch 10/50
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Epoch 00010: val_loss did not improve from 0.88322
126/126 [==============================] - 45s 357ms/step - loss: 0.4232 - accuracy: 0.8492 - val_loss: 1.3716 - val_accuracy: 0.5833
Epoch 11/50
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Epoch 00011: val_loss did not improve from 0.88322
126/126 [==============================] - 49s 387ms/step - loss: 0.3686 - accuracy: 0.8651 - val_loss: 1.0428 - val_accuracy: 0.6667
Finished training the model. The model and associated files can be found here:
example_feats_models/envclassifer/features_fbank_9m21d21h17m39s685ms/augment_builtin_speechcommands_fbank
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1000/1000 [==============================] - 1s 906us/step - loss: 1.0123 - accuracy: 0.5000
Test loss: 1.012262225151062
Test accuracy: 0.5
Entire program took 8.477654778957367 minutes.
-------------------------------------------------------------------------------
Let's plot how the model performed (on this small dataset)
.. code-block:: default
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')
.. image:: /auto_examples/images/sphx_glr_plot_extract_augment_train_classifier_002.png
:alt: model accuracy
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 8 minutes 30.795 seconds)
.. _sphx_glr_download_auto_examples_plot_extract_augment_train_classifier.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_extract_augment_train_classifier.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_extract_augment_train_classifier.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_