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deep_audio_features: training an using CNNs on audio classification tasks

1 About

deep_audio_features is a Python library for training Convolutional Neural Netowrks as audio classifiers. The library provides wrappers to pytorch for training CNNs on audio classification tasks, and using the CNNs as feature extractors.

2 Installation

Εither use the source code

git clone https://github.com/tyiannak/deep_audio_features

Or install using pip

pip3 install deep-audio-features -U

3 Functionality

3.1 Training a CNN

To train a CNN you can use the following command:

python3 deep_audio_features/bin/basic_training.py -i /path/to/folder1 /path/to/folder2

-i : select the folders where the data will be loaded from. -o : select the exported file name.

Or call the following function in Python:

from deep_audio_features.bin import basic_training as bt
bt.train_model(["low","medium","high"], "energy")

The code above reads the WAV files in 3 folders, uses the folder names as classnames, extracts spectrogram representations from the respective sounds, trains and validates the CNN and saves the trained model in pkl/energy.pt

3.2 Testing a CNN

3.2.1 Inference

To perform inference for one file only, run:

python3 deep_audio_features/bin/basic_test.py -m /path/to/model/ -i /path/to/file (-s)

-i : select the file where the testing data will be loaded from.

-m : select a model to apply testing.

-s : if included extracts segment level predictions of a sequence

Or call the following function in Python:

from deep_audio_features.bin import basic_test as btest
d, p = btest.test_model("pkl/energy.pt", 'some_file.wav', layers_dropped=0, test_segmentation=False)

The code above will use the CNN trained befre to classify an audio signal stored in some_file.wav. d stores the decision (class indices) and p the soft outputs of the classes. If layers_dropped is positive, d is empty an p contains the outputs of the N-layers_dropped layer (N is the total number of layers in the CNN). E.g. if layers_dropped, p will contain the outputs of the last fully connected layer, before softmax.

3.2.2 Evaluate on new data

To perform evaluation on different data, run:

python3 deep_audio_features/bin/classification_report.py -m /path/to/model/ -i /path/to/folder1 /path/to/folder2

-i : select the folders where the testing data will be loaded from.

-m : select a model to apply testing.

Or call the following function in Python:

from deep_audio_features.bin import classification_report as creport
creport.test_report("/path/to/model/", ["low","medium","high"], layers_dropped=0)

3.3 Transfer learning

To transfer knowledge from a pre-trained model and fit it on a new target task you can use the following command:

python3 deep_audio_features/bin/transfer_learning.py -m /path/to/model -i /path/to/folder1 /path/to/folder2 -l layers_freezed -s

-m : select a model to apply fine-tuning. -i : select the folders where the data will be loaded from. -l : the number of layers (layers_freezed) to be freezed (counting from the first convolutional layer to the last linear layer) -s : is an optional default strategy (it cancels -l flag) that freezes all the convolutional layers and trains just the linear ones

Similarly, you will need the same params to call the deep_audio_features.bin.transfer_learning.transfer_learning() function to transfer knowledge from a task to another:

from deep_audio_features.bin import transfer_learning as tl
tl.transfer_learning('pkl/emotion_energy.pt', ['test/low/', 'test/high'] , strategy=0, layers_freezed=0)

(The model will be saved in a local filename based on the timestamp)

3.4 Combine CNN features

In deep_audio_features/combine/config.yaml choose (i) which CNN models you want to combine by modifying either the model_paths or the google_drive_ids fields (in case the models are stored in google drive), (ii) whether you want to combine different CNN models (extract_nn_features boolean variable), use hand-crafted audio features using the pyAudioAnalysis library (extract_basic_features boolean variable), or combine the aforementioned choices (both variables set to True).

3.4.1 Train a combination of CNNs

python3 deep_audio_features/combine/trainer.py -i 4class_small/music_small 4class_small/speech_small -c deep_audio_features/combine/config.yaml

or in Python:

from deep_audio_features.combine import trainer
trainer.train(["4class_small/music_small", "4class_small/speech_small"], None, "config.yaml")

3.4.2 Evaluate the combiner

python3 deep_audio_features/combine/classification_report.py -m pkl/SVM_Thu_Jul_29_20:06:51_2021.pt -i 4class_small/music_small 4class_small/speech_small

or in Python:

from deep_audio_features.combine import classification_report
import pickle
modification = pickle.load(open("pkl/SVM_Thu_Jul_29_20:31:35_2021.pt", 'rb'))
classification_report.combine_test_report(["4class_small/music_small", "4class_small/speech_small"], modification)

3.4.3 Predict on an unknown sample using the combiner

python3 deep_audio_features/combine/predict.py -m pkl/SVM_Thu_Jul_29_20:06:51_2021.pt -i 4class_balanced/speech/s_BDYDHQBQMX_30.0_31.0.wav

or in Python:

from deep_audio_features.combine import predict
predict.predict("4class_balanced/speech/s_BDYDHQBQMX_30.0_31.0.wav", modification)

(load model as above)

3.5 Train a Convolutional Autoencoder

To train a Convolutional Autoencoder you can use the following command:

python3 deep_audio_features/bin/basic_training.py -t representation -i /path/to/folder1 /path/to/folder2

-t : performed task is representation learning. -i : select the unique folder or multiple folders where the data will be loaded from. -o : select the exported file name.

Or call the following function in Python:

from deep_audio_features.bin import basic_training as bt
bt.train_model(["low","medium","high"], "energy", task="representation")

The code above reads the WAV files in 3 folders, but avoids to use the folder names as classnames since an unsupervised process is performed. Then it extracts spectrogram representations from the respective sounds, trains and validates the ConvAE and saves the trained model in pkl/energy.pt

The number of channels in the final representation is set from the REPRESENTATION_CHANNELS variable found in deep_audio_feature/bin/config.py.

3.6 Testing a Convolutional Autoencoder

python3 deep_audio_features/bin/basic_test.py -m /path/to/model/ -i /path/to/file (-s)

-i : select the file where the testing data will be loaded from.

-m : select a model to apply testing.

-s : if included extracts segment level predictions of a sequence

Or call the following function in Python:

from deep_audio_features.bin import basic_test as btest
emb, _ = btest.test_model("pkl/energy.pt", 'some_file.wav', test_segmentation=False)

The code above will find out that the model is a ConvAE and will load it in order to extract the embedding from an audio signal stored in some_file.wav. emb stores the produced embeddings.

	with contextlib.closing(wave.open(f, 'r')) as fp:
	frames = fp.getnframes()
	fs = fp.getframerate()
	duration = frames / float(fs)
	length = int((duration -
	(config.HOP_LENGTH - config.HOP_LENGTH)) / \
	(config.HOP_LENGTH) + 1)

tyiannak / deep_audio_features Goto Github PK

deep_audio_features's Introduction

deep_audio_features: training an using CNNs on audio classification tasks

1 About

2 Installation

3 Functionality

3.1 Training a CNN

3.2 Testing a CNN

3.2.1 Inference

3.2.2 Evaluate on new data

3.3 Transfer learning

3.4 Combine CNN features

3.4.1 Train a combination of CNNs

3.4.2 Evaluate the combiner

3.4.3 Predict on an unknown sample using the combiner

3.5 Train a Convolutional Autoencoder

3.6 Testing a Convolutional Autoencoder

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