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datagen.py
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datagen.py
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import json
import librosa
import numpy as np
class DataGenerator():
def __init__(self, x_key, y_key, batch_size):
self.x_key = x_key
self.y_key = y_key
self.batch_size = batch_size
self.dataset_map = self.load_dataset_map()
def load_dataset_map(self, path):
with open(path) as json_file:
data = json.load(json_file)
return data
def extract_transients(audio, sr, ws, start_pad, hop=512, backtrack=True):
# grab onset times; backtrack detects minimum before transient
beats = librosa.onset.onset_detect(y=audio, sr=sr, units='frames', hop_length=hop, backtrack=backtrack)
frames = librosa.util.frame(audio, frame_length=ws, hop_length=hop)
return frames.T, beats
# verify transients are at same position
def correlate_transients(x, y):
shared = np.intersect1d(x, y)
print(f'len x {len(x)} len y {len(y)} len shared {len(shared)}')
return shared
def analyze_contrast(block, sr=44100):
S = np.abs(librosa.stft(block))
contrast = librosa.feature.spectral_contrast(S=S, sr=sr)
this_avg_contrast = np.mean(contrast)
return this_avg_contrast
def analyze_envelope(rms_blocks, plot_curves=False):
# check whether there is more energy in the first or second half
f_env = np.linspace(1, 0, num=rms_blocks.shape[1]) # forward envelope
r_env = np.linspace(0, 1, num=rms_blocks.shape[1]) # reverse envelope
rms_start = rms_blocks * f_env
rms_end = rms_blocks * r_env
if plot_curves:
plt.plot(rms_start.reshape((rms_start.shape[0] * rms_start.shape[1])), color='red')
plt.plot(rms_end.reshape((rms_end.shape[0] * rms_end.shape[1])))
plt.show()
rms_start = np.mean(rms_start)
rms_end = np.mean(rms_end)
if rms_start > rms_end:
return True
else:
return False
def validate_transients(x, y, sr=44100, visualize_rejects=False):
rms_total = librosa.feature.rms(y=y.reshape((y.shape[0] * y.shape[1])))
avg_rms = np.median(rms_total)
x_valid = []
y_valid = []
rejects_x = []
rejects_y = []
for X, Y in zip(x, y):
rms_blocks = (librosa.feature.rms(y=Y))
this_avg = np.mean(rms_blocks)
# determine if transient happens in first half
envelope_skew = analyze_envelope(rms_blocks)
if envelope_skew and this_avg > avg_rms:
x_valid.append(X)
y_valid.append(Y)
else:
rejects_x.append(X)
rejects_y.append(Y)
x_valid = np.asarray(x_valid)
y_valid = np.asarray(y_valid)
print(f'valid samples {x_valid.shape[0]}')
if visualize_rejects:
rejects_x = np.asarray(rejects_x)
rejects_y = np.asarray(rejects_y)
visualize_audio_data(rejects_x, rejects_y, sr=sample_rate) #see what is rejected
return x_valid, y_valid
`
def visualize_audio_data(data_x, data_y, sr=44100):
for x, y in zip(data_x, data_y):
print('x data:')
plt.plot(x)
print('y data:')
plt.plot(y, color='red')
plt.show()
print('x data:')
ipd.display(ipd.Audio(x, rate=sr, autoplay=False))
print('y data:')
ipd.display(ipd.Audio(y, rate=sr, autoplay=False))
plt.show()
print('\n')
def gen_dataset(data:"JSON database",
ws:"window size",
x_key,
y_key,
normalize_stems=False,
normalize_transients=False,
max_examples=100,
sample_rate=44100,
difference_mask=False): #"difference_mask = output y as (x - y)"
x_train = []
y_train = []
for i, k in enumerate(data.keys()):
x = data[k][x_key]
y = data[k][y_key]
try:
if len(x) > 0 and len(y) > 0:
print(f'loading {data[k][x_key][0]}')
print(f'loaded {i} of {max_examples}')
audio_x, sr = librosa.load(data[k][x_key][0], sr=sample_rate, res_type='kaiser_fast')
audio_y, _ = librosa.load(data[k][y_key][0], sr=sample_rate, res_type='kaiser_fast')
if normalize_stems: # NORMALIZES ENTIRE STEM, NOT INDIVIDUAL SAMPLES
audio_x = librosa.util.normalize(audio_x)
audio_y = librosa.util.normalize(audio_y)
print('loaded files, analyzing transients')
frames_x, bx = extract_transients(audio_x, sr, ws, 0)
frames_y, by = extract_transients(audio_y, sr, ws, 0)
idx_shared = correlate_transients(bx, by)
tx = frames_x[idx_shared]
ty = frames_y[idx_shared]
tx, ty = validate_transients(tx, ty) # verify transients are clean
for x_trans, y_trans in zip(tx, ty):
if normalize_transients:
x_trans = librosa.util.normalize(x_trans)
y_trans = librosa.util.normalize(y_trans)
if difference_mask: # calcuate difference
y_trans = x_trans - y_trans
x_train.append(x_trans)
y_train.append(y_trans)
except:
print('error with loading file, skipping')
continue
if i+1 > max_examples:
break
x_train = np.asarray(x_train)
y_train = np.asarray(y_train)
print(f'x shape {x_train.shape} y shape {y_train.shape}')
return x_train, y_train
# ======= LOAD IN DATASET ========
cambridge_dataset = '/content/drive/My Drive/Datasets/MultitrackStems/train/cambridge_dataset.json'
win_size = 16384
shuffle_dataset = False
with open(cambridge_dataset) as json_file:
data = json.load(json_file)
print(f'total num stems: {len(data)}')
x_key = 'overhead'
y_key = 'snare'
x_train, y_train = gen_dataset(data, win_size, x_key, y_key,
normalize_stems=False, normalize_transients=False,
max_examples=180)
# preview random 10 samples
print(x_train.shape)
rand_idx = np.random.randint(0, high=x_train.shape[0])
visualize_audio_data(x_train[rand_idx:rand_idx+10], y_train[rand_idx:rand_idx+10], sr=44100)
# ==== PREPARE DATASET DIMENSIONS ========
val_ratio = 0.15
val_examps = int(len(x_train) * val_ratio) # number of validation samples (takes from training)
x_val = x_train[len(x_train)-1-val_examps:]
y_val = y_train[len(x_train)-1-val_examps:]
x_train = x_train[:len(x_train)-1-val_examps]
y_train = y_train[:len(y_train)-1-val_examps]
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], 1)
y_train = y_train.reshape(y_train.shape[0], y_train.shape[1], 1)
x_val = x_val.reshape(x_val.shape[0], x_val.shape[1], 1)
y_val = y_val.reshape(y_val.shape[0], y_val.shape[1], 1)
print(x_train.shape, y_train.shape)
print(x_val.shape, y_val.shape)
# ==== SHUFFLE AND PREPROCESS ============
from sklearn.utils import shuffle
x_train, y_train = shuffle(x_train, y_train, random_state=0)
x_val, y_val = shuffle(x_val, y_val, random_state=42)