# Read WAV and MP3 files to array
from pydub import AudioSegment
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#from scipy.io import wavfile
#from plotly.offline import init_notebook_mode
#import plotly.graph_objs as go
#import plotly
import sklearn
import IPython
from IPython.display import Image
import librosa
import librosa.display
#import eli5
#import logguru

import os
import warnings
warnings.filterwarnings('ignore')

path=os.path.dirname(__file__)
os.chdir(path)
sampleFolder = os.path.join(path, 'AudioSamples')
sampleList = os.listdir(sampleFolder)
for sample_fileName in sampleList:
    print(sample_fileName)
    #print(sampleList[0])
    filepath = os.path.join(sampleFolder,sample_fileName)
    #filepath = os.path.join(sampleFolder,sampleList[0])
    y,sr = librosa.load(filepath,duration=2.97)
    ps = librosa.feature.melspectrogram(y=y, sr=sr)
    #print(ps.shape)
    #print(ps)
    #print(y)
    ##verschiedene spectral anzeigen
    #librosa.display.specshow(ps,y_axis='mel',x_axis='time')
    
    #librosa.display.waveplot(ps, sr=sr)
    
    X = librosa.stft(y)
    Xdb = librosa.amplitude_to_db(abs(X))
    plt.figure(figsize=(14,5))
    librosa.display.specshow(Xdb,sr=sr,x_axis='time',y_axis='hz')

    plt.show()