I am working on a gender classification project by voice
My dataset contains male and female audio files, which are 4528 .wav files.
I want to use the spectrogram to feed the neural network
I did this with the librosa
library with the (librosa.core.stft
) command and saved the extracted spectrograms from the audio files to a .npz file.
My problem is that my audio files have different lengths (some of them are less than one second and some of them are more than one second).
I want to use recurrent neural networks
these are the shapes of the first five data:
Unfortunately, data has a different shapes...
(32, 1025)
(26, 1025)
(40, 1025)
(31, 1025)
(45, 1025)
when I get the shape of my whole dataset:
X = np.array(data["specs"])
print(X.shape)
it returns (4528,) # number of all samples
also when I fit the network with input_shape=(32,1025)
get me the error: Failed to convert a NumPy array to a Tensor (Unsupported object type list).
what can I do with this problem ?!
this is how I extract spectrogram and store them into the .npz file:
def save_spec (npz_path , dataset_path , sample_rate=22050 , hop_lenght=512 , n_fft=2048):
# dictionary for storing data
data = {"mapping" : [],
"specs": [],
"labels": []}
# loop through all the labels
for i , (dirpath , dirname , filenames) in enumerate(os.walk(dataset_path)):
# ensure that we're not at the root level
if dirpath is not dataset_path:
# save the semantic label
dirpath_components = dirpath.split("/") # train/female => [train , "female"]
semantic_label = dirpath_components[-1]
data["mapping"].append(semantic_label)
# process files for a specific gender
for file in filenames:
file_path = os.path.join(dirpath,file)
try:
print(Fore.CYAN "Loading File...: {} :".format(file))
Signal, sample_rate = librosa.load(file_path, sr=sample_rate)
except:
print(Fore.RED "Loading FAILED...")
try:
print(Fore.BLUE "\t Extracting Spectrogram...")
spectrogram = librosa.core.stft(Signal , n_fft=n_fft , hop_length=hop_lenght)
spectrogram = np.abs(spectrogram)
spectrogram = spectrogram.T
except:
print(Fore.RED "\t Extracting FAILED...")
try:
print(Fore.YELLOW "\t\t Storing Data...")
data["specs"].append(spectrogram.tolist())
data["labels"].append(i-1)
except:
print(Fore.RED "\t\t Storing FAILED")
print(Fore.GREEN "\t\t\t Preprocessing Complete!")
print(Fore.WHITE "\t\t\tFile: {} : \n".format(file))
time.sleep(0.1)
np.savez_compressed(NPZ_PATH , x_train=data["specs"] , y_train=data["labels"] , mapping=data["mapping"])
and this is my network design:
DATA_PATH = "/content/drive/MyDrive/AI/Data/Per-Gender_Rec/data.npz"
DATA = np.load(DATA_PATH , allow_pickle=True)
Y = np.array(DATA["y_train"][0])
for i in range(5):
X = np.array(DATA["x_train"][i]) # return (4528,)
print(X.shape)
Network = Sequential()
Network.add(Flatten(input_shape=(32,1025)))
Network.add(Dense(512 , activation="relu"))
Network.add(Dense(256 , activation="relu"))
Network.add(Dense(64 , activation="relu"))
Network.add(Dense(1 , activation="sigmoid"))
Network.compile(optimizer="adam",
loss="binary_crossentropy",
metrics=["accuracy"])
Network.summary()
Network.fit(X , Y , batch_size=32 , epochs=5)
how can I fix that?
CodePudding user response:
Pad your shorter sequences so that they're all the same length
Assuming you had the following. If you wanted to pad below:
arr = np.array([[1,2,3,],[2,4,5]]) # shape (2,3)
arr = np.vstack([arr, np.zeros([1,3])]) # add a row of 0's at the bottom
So pick your biggest one, and experiment to see if padding zeros to the beginning, end or symmetrically works better