python 中的音频数据聚合

Question

I am using below function to augment audio data generated from wav audio files.

def generate_augmented_data(file_path):
augmented_data = []
samples = load_wav(file_path,get_duration=False)
for time_value in [0.7, 1, 1.3]:
    for pitch_value in [-1, 0, 1]:
        time_stretch_data = librosa.effects.time_stretch(samples, rate=time_value)
        final_data = librosa.effects.pitch_shift(time_stretch_data, sr=sample_rate, n_steps=pitch_value)
        augmented_data.append(final_data)
return augmented_data

I also need to augment the class labels and facing difficulties with it. Tried below cod, but its not getting me the expected result

## generating augmented data. 
def generate_augmented_data_label(file_path, label):
augmented_data = []
augmented_label = []
samples = load_wav(file_path,get_duration=False)
for time_value in [0.7, 1, 1.3]:
    for pitch_value in [-1, 0, 1]:
        time_stretch_data = librosa.effects.time_stretch(samples, rate=time_value)
        final_data = librosa.effects.pitch_shift(time_stretch_data, sr=sample_rate, n_steps=pitch_value)
        augmented_data.append(final_data)
        augmented_label.append(label)
return augmented_data,augmented_label

Before augmentation shape for data and labels are as below,

X_train.reset_index(inplace=True, drop=True)
y_train.reset_index(inplace=True, drop=True)
X_train_augmented_data = []
y_train_augmented_data = []
for i in range(len(X_train)):
#print(i)
   t1 = X_train.iloc[i]
   t2 = y_train[i]
   tmp1,tmp2 = generate_augmented_data_label(t1,t2)
#print(tmp1,tmp2)
   X_train_augmented_data.append(tmp1)
   y_train_augmented_data.append(tmp2)

len(X_train)
1600
len(y_train)
1600
print(len(X_train_augmented_data))
print(len(y_train_augmented_data))

After data augmentation and an additional masking step, shape is coming as

 augmented_train_data_mask = []
 for i in range(0,len(augmented_train_data_pad)):
   augmented_train_data_mask.append(list(map(bool,augmented_train_data_pad[i])))
   augmented_train_data_mask = np.array(augmented_train_data_mask)
 print(augmented_train_data_pad.shape)
 print(augmented_train_data_mask.shape)
 (14400, 17640)
 (14400, 17640)

However, label len is still 1600. Later when I pass these into an LSTM model, I am getting a shape mismatch error.

ValueError: Data cardinality is ambiguous:
x sizes: 14400, 14400
y sizes: 1600
Make sure all arrays contain the same number of samples.

Looking for some help to resolve this issue.

Answer 1

You may refer link for reference:

# https://www.geeksforgeeks.org/python-add-similar-value-multiple-times-in-list/

---

type(y_train)= panda series

from itertools import repeat

new_label=[]

for index, value in y_train.items(): new_label.extend(repeat(value, 2))

len(new_label)

Answer 2

You can use numpy repeat function to replicate your numpy array.

ex: In: arr = np.arange(3) out: array([0, 1, 2])

In: arr.repeat(3) Out: array([0, 0, 0, 1, 1, 1, 2, 2, 2])

Hope this will suffice your requirement.