mxnet-audio

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Implementation of music genre classification, audio-to-vec, song recommender, and music search in mxnet

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# mxnet-audio

Implementation of music genre classification, audio-to-vec, song recommender, and music search in mxnet

# Principles

* The classifier [ResNetV2AudioClassifier](mxnet_audio/library/resnet_v2.py) converts audio into mel-spectrogram and uses a simplified
 resnet DCnn architecture to classifier audios based on its associated labels. 
* The classifier [Cifar10AudioClassifier](mxnet_audio/library/cifar10.py) converts audio into mel-spectrogram and uses the cifar-10
DCnn architecture to classifier audios based on its associated labels.

The classifiers differ from those used in image classification in that:
* they use softrelu instead relu. 
* they have elongated max pooling shape (as the mel-spectrogram is elongated "image")
* Dropout being added

# Usage

### Dependencies

Make sure you have the right dependencies in your python environment by running:

```bash
pip install -r requirements.txt
```

### Train a deep learning model

The audio training uses [Gtzan](http://opihi.cs.uvic.ca/sound/genres.tar.gz) data set to train the
music classifier to recognize the genre of songs.

The training works by converting audio or song file into a mel-spectrogram which can be thought of
a 3-dimension tensor in a similar manner to an image. With the trained model, it is possible to build other interesting
application such as music recommendation, music search, audio2vec, etc.

To train on the Gtzan data set, run the following command:

```bash
cd demo
python cifar10_train.py
```

The [sample codes](demo/cifar10_train.py) below show how to train Cifar10AudioClassifier to classify songs
based on its genre labels:

```python
from mxnet_audio.library.cifar10 import Cifar10AudioClassifier
from mxnet_audio.library.utility.gtzan_loader import download_gtzan_genres_if_not_found
import mxnet

def load_audio_path_label_pairs(max_allowed_pairs=None):
    download_gtzan_genres_if_not_found('./very_large_data/gtzan')
    audio_paths = []
    with open('./data/lists/test_songs_gtzan_list.txt', 'rt') as file:
        for line in file:
            audio_path = './very_large_data/' + line.strip()
            audio_paths.append(audio_path)
    pairs = []
    with open('./data/lists/test_gt_gtzan_list.txt', 'rt') as file:
        for line in file:
            label = int(line)
            if max_allowed_pairs is None or len(pairs) < max_allowed_pairs:
                pairs.append((audio_paths[len(pairs)], label))
            else:
                break
    return pairs

def main():
    audio_path_label_pairs = load_audio_path_label_pairs()
    print('loaded: ', len(audio_path_label_pairs))

classifier = Cifar10AudioClassifier(model_ctx=mxnet.gpu(0), data_ctx=mxnet.gpu(0))
    batch_size = 8
    epochs = 100
    history = classifier.fit(audio_path_label_pairs, model_dir_path='./models',
                             batch_size=batch_size, epochs=epochs,
                             checkpoint_interval=2)

if __name__ == '__main__':
    main()
```

After training, the trained models are saved to [demo/models](demo/models).

To test the trained Cifar10AudioClassifier model, run the following command:

```bash
cd demo
python cifar10_predict.py
```

### Model Comparison

Below compares training quality of 
[ResNetV2AudioClassifier](mxnet_audio/library/resnet_v2.py) and [Cifar10AudioClassifier](mxnet_audio/library/cifar10.py):

![training-comppare](https://raw.githubusercontent.com/chen0040/mxnet-audio/master/demo/models/training-history-comparison.png)

### Predict Music Genres

The [sample codes](demo/cifar10_predict.py) shows how to use the trained Cifar10AudioClassifier model to predict the
music genres:

```python
from random import shuffle

from mxnet_audio.library.cifar10 import Cifar10AudioClassifier
from mxnet_audio.library.utility.gtzan_loader import download_gtzan_genres_if_not_found, gtzan_labels

def main():
    audio_path_label_pairs = load_audio_path_label_pairs()
    shuffle(audio_path_label_pairs)
    print('loaded: ', len(audio_path_label_pairs))

classifier = Cifar10AudioClassifier()
    classifier.load_model(model_dir_path='./models')

for i in range(0, 20):
        audio_path, actual_label_id = audio_path_label_pairs[i]
        predicted_label_id = classifier.predict_class(audio_path)
        print(audio_path)
        predicted_label = gtzan_labels[predicted_label_id]
        actual_label = gtzan_labels[actual_label_id]
        
        print('predicted: ', predicted_label, 'actual: ', actual_label)

if __name__ == '__main__':
    main()

```

### Audio to Vector

The [sample codes](demo/cifar10_encode_audio.py) shows how to use the trained Cifar10AudioClassifier model to encode an
audio file into a fixed-length numerical vector:

```python
from random import shuffle

from mxnet_audio.library.cifar10 import Cifar10AudioClassifier
from mxnet_audio.library.utility.gtzan_loader import download_gtzan_genres_if_not_found

def main():
    audio_path_label_pairs = load_audio_path_label_pairs()
    shuffle(audio_path_label_pairs)
    print('loaded: ', len(audio_path_label_pairs))

classifier = Cifar10AudioClassifier()
    classifier.load_model(model_dir_path='./models')

for i in range(0, 20):
        audio_path, actual_label_id = audio_path_label_pairs[i]
        audio2vec = classifier.encode_audio(audio_path)
        print(audio_path)

print('audio-to-vec: ', audio2vec)

if __name__ == '__main__':
    main()

```

### Music Search Engine

The [sample codes](demo/cifar10_search_music.py) shows how to use Cifar10AudioSearch with the trained model to search for
similar musics given a music file:

```python
from mxnet_audio.library.cifar10 import Cifar10AudioSearch
from mxnet_audio.library.utility.gtzan_loader import download_gtzan_genres_if_not_found

def main():
    search_engine = Cifar10AudioSearch()
    search_engine.load_model(model_dir_path='./models')
    for path, _ in load_audio_path_label_pairs():
        search_engine.index_audio(path)

query_audio = './data/audio_samples/example.mp3'
    search_result = search_engine.query(query_audio, top_k=10)

for idx, similar_audio in enumerate(search_result):
        print('result #%s: %s' % (idx+1, similar_audio))

if __name__ == '__main__':
    main()

```

### Recommend Songs

The [sample codes](demo/cifar10_recommend_music.py) shows how to use Cifar10AudioRecommender with the trained model to 
recommend songs based on user's listening history:

###

```python
from random import shuffle

from mxnet_audio.library.cifar10 import Cifar10AudioRecommender
from mxnet_audio.library.utility.gtzan_loader import download_gtzan_genres_if_not_found

def main():
    music_recommender = Cifar10AudioRecommender()
    music_recommender.load_model(model_dir_path='./models')
    music_archive = load_audio_path_label_pairs()
    for path, _ in music_archive:
        music_recommender.index_audio(path)

# create fake user history on musics listening to
    shuffle(music_archive)
    for i in range(30):
        song_i_am_listening = music_archive[i]
        music_recommender.track(song_i_am_listening)

for idx, similar_audio in enumerate(music_recommender.recommend(limits=10)):
        print('result #%s: %s' % (idx+1, similar_audio))

if __name__ == '__main__':
    main()

```

# Note

### On pre-processing

To pre-generate the mel-spectrograms from the audio files for classification, one can also first run the following scripts
before starting training, which will make the training faster:

```bash
cd demo/utility
python gtzan_loader.py
```

### audioread.NoBackend

The audio processing depends on librosa version 0.6 which depends on audioread.

If you are on Windows and sees the error "audioread.NoBackend", go to [ffmpeg](https://ffmpeg.zeranoe.com/builds/)
and download the shared linking build, unzip to a local directory and then add the bin folder of the 
ffmpeg to the Windows $PATH environment variable. Restart your cmd or powershell, Python should now be
able to locate the backend for audioread in librosa

### Training with GPU

Note that the default training scripts in the [demo](demo) folder use GPU for training, therefore, you must configure your
graphic card for this (or remove the "model_ctx=mxnet.gpu(0)" in the training scripts).

* Step 1: Download and install the [CUDA® Toolkit 9.0](https://developer.nvidia.com/cuda-90-download-archive) (you should download CUDA® Toolkit 9.0)
* Step 2: Download and unzip the [cuDNN 7.0.4 for CUDA@ Toolkit 9.0](https://developer.nvidia.com/cudnn) and add the
bin folder of the unzipped directory to the $PATH of your Windows environment

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