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import numpy as np
import matplotlib
from pathlib import Path
import shutil
from tqdm import tqdm
from tools import save_results, VAE_out_put_to_spc, show_spc
def test_reconstruction(encoder, decoder, data, n_sample=5, f=0, path_name="./data/test_reconstruction", save_data=False):
"""Generate and show reconstruction results. Randomly reconstruct 'n_sample' samples in 'data'.
You can manually set the index of the first reconstructed sample by 'f'.
Parameters
----------
encoder: keras.engine.functional.Functional
The VAE encoder.
decoder: keras.engine.functional.Functional
Sample rate of the audio to generate.
data: numpy array
The VAE decoder.
n_sample: int
Number of samples to reconstruct.
f: int
Index of the first reconstructed sample.
path_name: String
Path to save the results.
save_data: bool
Whether save the results.
Returns
-------
"""
if save_data:
if Path(path_name).exists():
shutil.rmtree(path_name)
Path(path_name).mkdir(parents=True, exist_ok=True)
for i in range(n_sample):
index = np.random.randint(np.shape(data)[0])
if i == 0:
index = f
print("######################################################")
print(f"index: {index}")
input = data[index]
print(f"Original:")
show_spc(VAE_out_put_to_spc(input))
if save_data:
save_results(VAE_out_put_to_spc(input), f"{path_name}/origin_{index}.png", f"{path_name}/origin_{index}.wav")
input = data[index:index + 1]
timbre_encode = encoder.predict(input)[0]
encode = timbre_encode
reconstruction = decoder.predict(encode)[0]
reconstruction = VAE_out_put_to_spc(reconstruction)
reconstruction = np.minimum(5000, reconstruction)
print(f"Reconstruction:")
show_spc(reconstruction)
if save_data:
save_results(reconstruction, f"{path_name}/reconstruction_{index}.png", f"{path_name}/reconstruction_{index}.wav")
def test_interpulation(data0, data1, encoder, decoder, path_name = "./data/test_interpolation", save_data=False):
"""Generate new sounds by latent space interpolation.
Parameters
----------
data0: numpy array
First input for interpolation.
data1: numpy array
Second input for interpolation.
encoder: keras.engine.functional.Functional
The VAE encoder.
decoder: keras.engine.functional.Functional
Sample rate of the audio to generate.
path_name: String
Path to save the results.
save_data: bool
Whether save the results.
Returns
-------
"""
if save_data:
if Path(path_name).exists():
shutil.rmtree(path_name)
Path(path_name).mkdir(parents=True, exist_ok=True)
if save_data:
save_results(VAE_out_put_to_spc(data0), f"{path_name}/origin_0.png", f"{path_name}/origin_0.wav")
save_results(VAE_out_put_to_spc(data1), f"{path_name}/origin_1.png", f"{path_name}/origin_1.wav")
print("First Original:")
show_spc(VAE_out_put_to_spc(data0))
print("Second Original:")
show_spc(VAE_out_put_to_spc(data1))
print("######################################################")
print("Interpolations:")
data0 = np.reshape(data0, (1, 512, 256, 1))
data1 = np.reshape(data1, (1, 512, 256, 1))
timbre_encode0 = encoder.predict(data0)[0]
timbre_encode1 = encoder.predict(data1)[0]
n_f = 8
for i in tqdm(range(n_f+1)):
rate = 1 - i/n_f
new_timbre = rate * timbre_encode0 + (1-rate) * timbre_encode1
output = decoder.predict(new_timbre)
spc = np.reshape(VAE_out_put_to_spc(output), (512,256))
if save_data:
save_results(spc, f"{path_name}/test_interpolation_{i}.png", f"{path_name}/test_interpolation_{i}.wav")
show_spc(spc)
def test_random_sampling(decoder, n_sample=20, mu=np.zeros(20), sigma=np.ones(20), save_data = False, path_name = "./data/test_random_sampling"):
"""Generate new sounds by random sampling in the latent space.
Parameters
----------
decoder: keras.engine.functional.Functional
Sample rate of the audio to generate.
path_name: String
Path to save the results.
save_data: bool
Whether save the results.
Returns
-------
"""
if save_data:
if Path(path_name).exists():
shutil.rmtree(path_name)
Path(path_name).mkdir(parents=True, exist_ok=True)
for i in tqdm(range(n_sample)):
off_set = np.random.normal(mu,np.square(sigma))
new_timbre = np.reshape(off_set, (1,20))
output = decoder.predict(new_timbre)
spc = np.reshape(VAE_out_put_to_spc(output), (512,256))
if save_data:
save_results(spc, f"{path_name}/random_sampling_{i}.png", f"{path_name}/random_sampling_{i}.wav")
show_spc(spc)
def test_style_transform(original, encoder, decoder, perceptual_label_predictor, n_samples=10, save_data = False, goal=0, direction=0, path_name = "./data/test_style_transform"):
"""Generate new sounds by latent space interpolation.
Parameters
----------
original: numpy array
Original for style transform.
encoder: keras.engine.functional.Functional
The VAE encoder.
decoder: keras.engine.functional.Functional
Sample rate of the audio to generate.
perceptual_label_predictor: keras.engine.functional.Functional
Model that selects the output.
path_name: String
Path to save the results.
save_data: bool
Whether save the results.
Returns
-------
"""
if save_data:
if Path(path_name).exists():
shutil.rmtree(path_name)
Path(path_name).mkdir(parents=True, exist_ok=True)
save_results(VAE_out_put_to_spc(original), f"{path_name}/origin.png", f"{path_name}/origin.wav")
labels_names = ["metallic", "warm", "breathy", "evolving", "aggressiv"]
timbre_dim = 20
print("Original:")
show_spc(VAE_out_put_to_spc(original))
print("######################################################")
original_code = encoder.predict(np.reshape(original, (1,512,256,1)))[0]
new_encodes = np.zeros((n_samples, timbre_dim)) + original_code
new_encodes = [new_encode + np.random.normal(np.zeros(timbre_dim) * 0.2,np.ones(timbre_dim)) for new_encode in new_encodes]
new_encodes = np.array(new_encodes, dtype=np.float32)
perceptual_labels = perceptual_label_predictor.predict(new_encodes)[:,goal]
if direction == 0:
best_index = np.argmin(perceptual_labels)
suffix = f"less_{labels_names[goal]}"
else:
best_index = np.argmax(perceptual_labels)
suffix = f"more_{labels_names[goal]}"
output = decoder.predict(new_encodes[best_index:best_index+1])
spc = np.reshape(VAE_out_put_to_spc(output), (512,256))
if save_data:
save_results(spc, f"{path_name}/{suffix}.png", f"{path_name}/{suffix}.wav")
print("Manipulated (suffix):")
show_spc(spc)
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