preprocess_for_audio_genre_classification.py

import os
import librosa
import math
import json

DATASET_PATH = 'genres_original'
JSON_PATH = 'data.json'

SAMPLE_RATE = 22050
DURATION = 30  # measured in seconds
SAMPLES_PER_TRACK = SAMPLE_RATE * DURATION

def save_mfcc(dataset_path, json_path, n_mfcc=23, n_fft=2048, hop_length=512, num_segments=10):

    # dictionary to store data
    data = {
        "mapping": [],
        "mfcc": [],
        "labels": []
    }

    num_samples_per_segment = int(SAMPLES_PER_TRACK / num_segments)
    expected_num_mfcc_vectors_per_segment = math.ceil(num_samples_per_segment / hop_length)

    # loop through all the genres
    for i, (dirpath, dirnames, filenames) in enumerate(os.walk(dataset_path)):

        # ensure that we are not at the root level
        if dirpath is not dataset_path:

            # save the semantic label
            dirpath_components = dirpath.split("/")  # genre/blues => ['genre', 'blues']
            semantic_label = dirpath_components[-1]
            data['mapping'].append(semantic_label)
            print("\n\nProcessing {}".format(semantic_label))

            # process files for a specific genre
            for f in filenames:

                # load audio file
                file_path = os.path.join(dirpath, f)
                signal, sr = librosa.load(file_path, sr=SAMPLE_RATE)

                # process segments, extracting mfcc and storing data
                for s in range(num_segments):
                    start_sample = num_samples_per_segment * s
                    finish_sample = start_sample + num_samples_per_segment

                    mfcc = librosa.feature.mfcc(signal[start_sample:finish_sample],
                                                sr=sr,
                                                n_fft=n_fft,
                                                n_mfcc=n_mfcc,
                                                hop_length=hop_length)
                    mfcc = mfcc.T

                    # store mfcc for segment if it has the expected length
                    if len(mfcc) == expected_num_mfcc_vectors_per_segment:
                        data['mfcc'].append(mfcc.tolist())
                        data['labels'].append(i-1)
                        print("{}, segment:{}".format(file_path, s))

    with open(json_path, 'w') as fp:
        json.dump(data, fp, indent=4)


if __name__ == "__main__":
    save_mfcc(DATASET_PATH, JSON_PATH, num_segments=10)