.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_plot_train_denoiser.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_train_denoiser.py:


=============================
Train a Denoising Autoencoder
=============================

Train a denoising autoencoder with clean and noisy acoustic features.

To see how soundpy implements this, see `soundpy.models.builtin.denoiser_train`, 
`soundpy.builtin.denoiser_feats` and `soundpy.builtin.create_denoise_data`.


.. 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
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Designate path relevant for accessing audiodata

I will load previously extracted features (sample data), see `soundpy.feats.save_features_datasets` or `soundpy.builtin.denoiser_feats`


.. code-block:: default

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








What is in this folder?


.. code-block:: default

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




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    test_data_clean_fbank.npy
    dataset_audio_assignments.csv
    train_data_noisy_fbank.npy
    audiofiles_datasets_clean.csv
    log_extraction_settings.csv
    clean_audio.csv
    test_data_noisy_fbank.npy
    val_data_noisy_fbank.npy
    noisy_audio.csv
    train_data_clean_fbank.npy
    audiofiles_datasets_noisy.csv
    val_data_clean_fbank.npy




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


.. code-block:: default

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




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    test_data_clean_fbank.npy
    train_data_noisy_fbank.npy
    test_data_noisy_fbank.npy
    val_data_noisy_fbank.npy
    train_data_clean_fbank.npy
    val_data_clean_fbank.npy




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


.. code-block:: default

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





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    dataset_audio_assignments.csv
    audiofiles_datasets_clean.csv
    log_extraction_settings.csv
    clean_audio.csv
    noisy_audio.csv
    audiofiles_datasets_noisy.csv




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


.. code-block:: default

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




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    dur_sec  -->  3
    feature_type  -->  fbank noisy
    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  -->  11
    labeled_data  -->  False
    visualize  -->  True
    input_shape  -->  (28, 11, 40)
    desired_shape  -->  (308, 40)
    use_librosa  -->  True
    center  -->  True
    mode  -->  reflect
    subsection_data  -->  False
    divide_factor  -->  5
    kwargs  -->  {}




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.


.. code-block:: default

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





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    train  -->  ['../../../../mini-audio-datasets/denoise/clean/S_20_03.wav', '../../../../mini-audio-datasets/denoise/clean/S_07_09.wav', '../../../../mini-audio-datasets/denoise/clean/S_29_06.wav', '../../../../mini-audio-datasets/denoise/clean/S_16_07.wav', '../../../../mini-audio-datasets/denoise/clean/S_04_10.wav', '../../../../mini-audio-datasets/denoise/clean/S_01_03.wav']
    val  -->  ['../../../../mini-audio-datasets/denoise/clean/S_01_02.wav', '../../../../mini-audio-datasets/denoise/clean/S_17_06.wav']
    test  -->  ['../../../../mini-audio-datasets/denoise/clean/S_18_08.wav', '../../../../mini-audio-datasets/denoise/clean/S_18_01.wav']




Built-In Functionality: soundpy does everything for you
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
For more about this, see `soundpy.builtin.denoiser_train`.


.. code-block:: default

    model_dir, history = spdl.denoiser_train(
        feature_extraction_dir = feature_extraction_dir,
        epochs = 10)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none



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



    TRAINING SESSION  1
    Training on: 
    ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/train_data_noisy_fbank.npy
    ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/train_data_clean_fbank.npy

    <FlatMapDataset shapes: ((1, 308, 40, 1), (1, 308, 40, 1)), types: (tf.float64, tf.float64)>
    <FlatMapDataset shapes: ((1, 308, 40, 1), (1, 308, 40, 1)), types: (tf.float64, tf.float64)>
    Epoch 1/10

    1/6 [====>.........................] - ETA: 0s - loss: 0.6914
    2/6 [=========>....................] - ETA: 0s - loss: 0.6911
    3/6 [==============>...............] - ETA: 0s - loss: 0.6903
    4/6 [===================>..........] - ETA: 0s - loss: 0.6898
    5/6 [========================>.....] - ETA: 0s - loss: 0.6894
    6/6 [==============================] - ETA: 0s - loss: 0.6882
    Epoch 00001: val_loss improved from inf to 0.68425, saving model to ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms/model_autoencoder_denoise_9m3d13h26m6s116ms.h5
    
    6/6 [==============================] - 1s 156ms/step - loss: 0.6882 - val_loss: 0.6843
    Epoch 2/10

    1/6 [====>.........................] - ETA: 0s - loss: 0.6845
    2/6 [=========>....................] - ETA: 0s - loss: 0.6844
    3/6 [==============>...............] - ETA: 0s - loss: 0.6830
    4/6 [===================>..........] - ETA: 0s - loss: 0.6824
    5/6 [========================>.....] - ETA: 0s - loss: 0.6818
    6/6 [==============================] - ETA: 0s - loss: 0.6800
    Epoch 00002: val_loss improved from 0.68425 to 0.67458, saving model to ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms/model_autoencoder_denoise_9m3d13h26m6s116ms.h5
    
    6/6 [==============================] - 1s 137ms/step - loss: 0.6800 - val_loss: 0.6746
    Epoch 3/10

    1/6 [====>.........................] - ETA: 0s - loss: 0.6746
    2/6 [=========>....................] - ETA: 0s - loss: 0.6745
    3/6 [==============>...............] - ETA: 0s - loss: 0.6724
    4/6 [===================>..........] - ETA: 0s - loss: 0.6715
    5/6 [========================>.....] - ETA: 0s - loss: 0.6708
    6/6 [==============================] - ETA: 0s - loss: 0.6681
    Epoch 00003: val_loss improved from 0.67458 to 0.66083, saving model to ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms/model_autoencoder_denoise_9m3d13h26m6s116ms.h5
    
    6/6 [==============================] - 1s 142ms/step - loss: 0.6681 - val_loss: 0.6608
    Epoch 4/10

    1/6 [====>.........................] - ETA: 0s - loss: 0.6598
    2/6 [=========>....................] - ETA: 0s - loss: 0.6597
    3/6 [==============>...............] - ETA: 0s - loss: 0.6568
    4/6 [===================>..........] - ETA: 0s - loss: 0.6554
    5/6 [========================>.....] - ETA: 0s - loss: 0.6544
    6/6 [==============================] - ETA: 0s - loss: 0.6509
    Epoch 00004: val_loss improved from 0.66083 to 0.64138, saving model to ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms/model_autoencoder_denoise_9m3d13h26m6s116ms.h5
    
    6/6 [==============================] - 1s 141ms/step - loss: 0.6509 - val_loss: 0.6414
    Epoch 5/10

    1/6 [====>.........................] - ETA: 0s - loss: 0.6380
    2/6 [=========>....................] - ETA: 0s - loss: 0.6376
    3/6 [==============>...............] - ETA: 0s - loss: 0.6337
    4/6 [===================>..........] - ETA: 0s - loss: 0.6317
    5/6 [========================>.....] - ETA: 0s - loss: 0.6303
    6/6 [==============================] - ETA: 0s - loss: 0.6262
    Epoch 00005: val_loss improved from 0.64138 to 0.61368, saving model to ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms/model_autoencoder_denoise_9m3d13h26m6s116ms.h5
    
    6/6 [==============================] - 1s 137ms/step - loss: 0.6262 - val_loss: 0.6137
    Epoch 6/10

    1/6 [====>.........................] - ETA: 0s - loss: 0.6063
    2/6 [=========>....................] - ETA: 0s - loss: 0.6050
    3/6 [==============>...............] - ETA: 0s - loss: 0.6002
    4/6 [===================>..........] - ETA: 0s - loss: 0.5971
    5/6 [========================>.....] - ETA: 0s - loss: 0.5952
    6/6 [==============================] - ETA: 0s - loss: 0.5906
    Epoch 00006: val_loss improved from 0.61368 to 0.57419, saving model to ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms/model_autoencoder_denoise_9m3d13h26m6s116ms.h5
    
    6/6 [==============================] - 1s 139ms/step - loss: 0.5906 - val_loss: 0.5742
    Epoch 7/10

    1/6 [====>.........................] - ETA: 0s - loss: 0.5608
    2/6 [=========>....................] - ETA: 0s - loss: 0.5577
    3/6 [==============>...............] - ETA: 0s - loss: 0.5524
    4/6 [===================>..........] - ETA: 0s - loss: 0.5478
    5/6 [========================>.....] - ETA: 0s - loss: 0.5450
    6/6 [==============================] - ETA: 0s - loss: 0.5408
    Epoch 00007: val_loss improved from 0.57419 to 0.51923, saving model to ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms/model_autoencoder_denoise_9m3d13h26m6s116ms.h5
    
    6/6 [==============================] - 1s 134ms/step - loss: 0.5408 - val_loss: 0.5192
    Epoch 8/10

    1/6 [====>.........................] - ETA: 0s - loss: 0.4977
    2/6 [=========>....................] - ETA: 0s - loss: 0.4912
    3/6 [==============>...............] - ETA: 0s - loss: 0.4858
    4/6 [===================>..........] - ETA: 0s - loss: 0.4794
    5/6 [========================>.....] - ETA: 0s - loss: 0.4756
    6/6 [==============================] - ETA: 0s - loss: 0.4732
    Epoch 00008: val_loss improved from 0.51923 to 0.44650, saving model to ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms/model_autoencoder_denoise_9m3d13h26m6s116ms.h5
    
    6/6 [==============================] - 1s 134ms/step - loss: 0.4732 - val_loss: 0.4465
    Epoch 9/10

    1/6 [====>.........................] - ETA: 0s - loss: 0.4141
    2/6 [=========>....................] - ETA: 0s - loss: 0.4031
    3/6 [==============>...............] - ETA: 0s - loss: 0.3988
    4/6 [===================>..........] - ETA: 0s - loss: 0.3909
    5/6 [========================>.....] - ETA: 0s - loss: 0.3870
    6/6 [==============================] - ETA: 0s - loss: 0.3891
    Epoch 00009: val_loss improved from 0.44650 to 0.36376, saving model to ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms/model_autoencoder_denoise_9m3d13h26m6s116ms.h5
    
    6/6 [==============================] - 1s 135ms/step - loss: 0.3891 - val_loss: 0.3638
    Epoch 10/10

    1/6 [====>.........................] - ETA: 0s - loss: 0.3176
    2/6 [=========>....................] - ETA: 0s - loss: 0.3024
    3/6 [==============>...............] - ETA: 0s - loss: 0.3015
    4/6 [===================>..........] - ETA: 0s - loss: 0.2936
    5/6 [========================>.....] - ETA: 0s - loss: 0.2914
    6/6 [==============================] - ETA: 0s - loss: 0.3017
    Epoch 00010: val_loss improved from 0.36376 to 0.29168, saving model to ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms/model_autoencoder_denoise_9m3d13h26m6s116ms.h5
    
    6/6 [==============================] - 1s 138ms/step - loss: 0.3017 - val_loss: 0.2917

    Finished training the model. The model and associated files can be found here: 
    ../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms




Where the model and logs are located:


.. code-block:: default

    model_dir






.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    PosixPath('../../../audiodata2/example_feats_models/denoiser/example_feats_fbank/model_autoencoder_denoise_9m3d13h26m6s116ms')



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


.. code-block:: default

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



.. image:: /auto_examples/images/sphx_glr_plot_train_denoiser_001.png
    :alt: model loss
    :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  12.956 seconds)


.. _sphx_glr_download_auto_examples_plot_train_denoiser.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_train_denoiser.py <plot_train_denoiser.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_train_denoiser.ipynb <plot_train_denoiser.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_