def create_label(request):
# get the address of the wav audio
if request.method == 'POST':
# wav_path = request.POST.get('path')
file = request.FILES['file']
# print(wav_path)
wavName = "./music/wav/" + file.name
print(wavName)
with open(wavName, 'wb') as wav:
for c in file.chunks():
wav.write(c)
music = Music()
music.wav_path = wavName
# load the web of ASR
gen = load_model('music\MusicClass\music_cla_model.h5')
# check the length of the audio and cut into a proper length
x, sr = librosa.load(wavName)
if len(x) < 88200:
x = np.pad(x, (0, 88200 - x.shape[0]), 'constant')
else:
x = x[0:88200:1]
# add some feature and reshape it
mfcc = librosa.feature.mfcc(y=x, sr=sr, n_mfcc=40)
norm_mfcc = sklearn.preprocessing.scale(mfcc, axis=1)
mfcc = norm_mfcc.reshape(1, mfcc.shape[0], mfcc.shape[1], 1)
# predict the result and get the best one and its label
result = gen.predict(mfcc)
probability = []
result = result.reshape(10, 1)
for num in result:
probability.append(float(num))
print(probability, sum(probability), max(probability))
music.probability = max(probability)
music.label = probability.index(max(probability))
print("label：" + str(music.label))
music.save()
return HttpResponse("succeed!!!")
# def create_label(request):
# # get the address of the wav audio
# if request.method == 'POST':
# # wav_path = request.POST.get('path')
# file = request.FILES['file']
# print(type(file))
# print(file.name)
# wavName = "./music/wav/" + file.name
# print(wavName)
# with open(wavName, 'wb') as wav:
# for c in file.chunks():
# wav.write(c)
# return HttpResponse("succeed!!!")
