preparation.py

import glob
import os
import pandas as pd
import numpy as np
import shutil
import librosa
from tqdm import tqdm


def extract_feature(file_name, **kwargs):
    """
    Extract feature from audio file `file_name`
        Features supported:
            - MFCC (mfcc)
            - Chroma (chroma)
            - MEL Spectrogram Frequency (mel)
            - Contrast (contrast)
            - Tonnetz (tonnetz)
        e.g:
        `features = extract_feature(path, mel=True, mfcc=True)`
    """
    mfcc = kwargs.get("mfcc")
    chroma = kwargs.get("chroma")
    mel = kwargs.get("mel")
    contrast = kwargs.get("contrast")
    tonnetz = kwargs.get("tonnetz")
    X, sample_rate = librosa.core.load(file_name)
    if chroma or contrast:
        stft = np.abs(librosa.stft(X))
    result = np.array([])
    if mfcc:
        mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)
        result = np.hstack((result, mfccs))
    if chroma:
        chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)
        result = np.hstack((result, chroma))
    if mel:
        mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)
        result = np.hstack((result, mel))
    if contrast:
        contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)
        result = np.hstack((result, contrast))
    if tonnetz:
        tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)
        result = np.hstack((result, tonnetz))
    return result

dirname = "data"

if not os.path.isdir(dirname):
    os.mkdir(dirname)


csv_files = glob.glob("*.csv")

for j, csv_file in enumerate(csv_files):
    print("[+] Preprocessing", csv_file)
    df = pd.read_csv(csv_file)
    # only take filename and gender columns
    new_df = df[["filename", "gender"]]
    print("Previously:", len(new_df), "rows")
    # take only male & female genders (i.e droping NaNs & 'other' gender)
    new_df = new_df[np.logical_or(new_df['gender'] == 'female', new_df['gender'] == 'male')]
    print("Now:", len(new_df), "rows")
    new_csv_file = os.path.join(dirname, csv_file)
    # save new preprocessed CSV 
    new_df.to_csv(new_csv_file, index=False)
    # get the folder name
    folder_name, _ = csv_file.split(".")
    audio_files = glob.glob(f"{folder_name}/{folder_name}/*")
    all_audio_filenames = set(new_df["filename"])
    for i, audio_file in tqdm(list(enumerate(audio_files)), f"Extracting features of {folder_name}"):
        splited = os.path.split(audio_file)
        # audio_filename = os.path.join(os.path.split(splited[0])[-1], splited[-1])
        audio_filename = f"{os.path.split(splited[0])[-1]}/{splited[-1]}"
        # print("audio_filename:", audio_filename)
        if audio_filename in all_audio_filenames:
            # print("Copyying", audio_filename, "...")
            src_path = f"{folder_name}/{audio_filename}"
            target_path = f"{dirname}/{audio_filename}"
            #create that folder if it doesn't exist
            if not os.path.isdir(os.path.dirname(target_path)):
                os.mkdir(os.path.dirname(target_path))
            features = extract_feature(src_path, mel=True)
            target_filename = target_path.split(".")[0]
            np.save(target_filename, features)
            # shutil.copyfile(src_path, target_path)