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micplacementconvnet_full.py
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micplacementconvnet_full.py
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# -*- coding: utf-8 -*-
"""MicPlacementConvNet.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1HZdAsF77IqUOvP6MaweNqCxOr8JBugD7
"""
from google.colab import drive
drive.mount('/content/drive')
!pip install comet_ml
from comet_ml import Experiment
import keras
from keras.models import Model, Sequential
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dense, Dropout, Activation, Flatten, RepeatVector, TimeDistributed, MaxPooling1D, UpSampling1D
from keras.layers import LSTM, Dense, Dropout, Input, concatenate, Conv1D, LeakyReLU
from keras.callbacks import ModelCheckpoint
import numpy as np
import librosa
import matplotlib.pyplot as plt
from librosa import display
from sklearn import preprocessing
import IPython.display as ipd
import datetime
class callback(keras.callbacks.Callback):
def __init__(self, x_val, y_val, model, num_tests=1, audio_preview=True, sr=44100):
self.losses = []
self.model = model
self.x_val = x_val
self.y_val = y_val
self.num_examples = x_val.shape[0]
self.num_tests = num_tests
self.sr = sr
self.audio_preview = audio_preview
self.difference_mask = False
def on_train_begin(self, logs={}):
return
def on_train_end(self, logs={}):
return
def on_epoch_begin(self, epoch, logs={}):
return
def random_sample(self):
rand_idx = np.random.randint(0, high=self.num_examples)
return self.x_val[rand_idx, :, :], self.y_val[rand_idx, :, :]
def on_epoch_end(self, epoch, logs={}):
for _ in range(self.num_tests):
x, y = self.random_sample()
y_p = self.model.predict(x.reshape((1,x.shape[0],1)))
x = np.squeeze(x)
y = np.squeeze(y)
y_p = np.squeeze(y_p)
if self.difference_mask:
y = x + y
y_p = x + y_p
print('x/y_p diff:')
print(abs(np.sum(x) - np.sum(y_p)))
print('x vs predicted y')
plt.plot(x, color='red')
plt.plot(y_p)
plt.show()
print('ground truth y vs predicted y')
plt.plot(y, color='red')
plt.plot(y_p)
plt.show()
if self.audio_preview:
print('input sample:')
ipd.display(ipd.Audio(x, rate=self.sr, autoplay=False))
print('ground truth:')
ipd.display(ipd.Audio(y, rate=self.sr, autoplay=False))
print('prediction')
ipd.display(ipd.Audio(y_p, rate=self.sr, autoplay=False))
return
def on_batch_begin(self, batch, logs={}):
return
def on_batch_end(self, batch, logs={}):
self.losses.append(logs.get('loss'))
return
import librosa
import numpy as np
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 ========
import json
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)
# ===== RUN EXPERIMENT ===================
experiment = Experiment(api_key="OKhPlin1BVQJFzniHu1f3K1t3",
project_name="micplacementwavenet", workspace="cm5409a")
# Hyperparameters --------
batch_size = 32
num_epochs = 5
Fc = 24 # num filters per layer (which is multiplied by depth)
sources_to_estimate = 1
# -----------------------
init_x = x_train[0]
init_y = y_train[0]
input_shape = (x_train.shape[1], 1)
output_shape = (y_train.shape[1], 1)
main_dim = input_shape[1]
# MODEL =======================================================
# wave-U-net keras implementation, details: https://arxiv.org/pdf/1806.03185.pdf
# NOTES:
# change padding from 'same' to 'causal'
input_layer = Input(shape=input_shape)
downsample_0 = Conv1D(filters=Fc*1, kernel_size=15, padding='same')(input_layer)
downsample_0 = LeakyReLU(alpha=0.05)(downsample_0) # ACTIVATION
downsample_0 = MaxPooling1D(pool_size=2)(downsample_0) # DOWNSAMPLE
downsample_1 = Conv1D(filters=Fc*2, kernel_size=15, padding='same')(downsample_0)
downsample_1 = LeakyReLU(alpha=0.05)(downsample_1) # ACTIVATION
downsample_1 = MaxPooling1D(pool_size=2)(downsample_1) # DOWNSAMPLE
downsample_2 = Conv1D(filters=Fc*3, kernel_size=15, padding='same')(downsample_1)
downsample_2 = LeakyReLU(alpha=0.05) (downsample_2)# ACTIVATION
downsample_2 = MaxPooling1D(pool_size=2)(downsample_2) # DOWNSAMPLE
downsample_3 = Conv1D(filters=Fc*4, kernel_size=15, padding='same')(downsample_2)
downsample_3 = LeakyReLU(alpha=0.05) (downsample_3)# ACTIVATION
downsample_3 = MaxPooling1D(pool_size=2)(downsample_3) # DOWNSAMPLE
downsample_4 = Conv1D(filters=Fc*5, kernel_size=15, padding='same')(downsample_3)
downsample_4 = LeakyReLU(alpha=0.05) (downsample_4)# ACTIVATION
downsample_4 = MaxPooling1D(pool_size=2)(downsample_4) # DOWNSAMPLE
downsample_5 = Conv1D(filters=Fc*6, kernel_size=15, padding='same')(downsample_4)
downsample_5 = LeakyReLU(alpha=0.05) (downsample_5)# ACTIVATION
downsample_5 = MaxPooling1D(pool_size=2)(downsample_5) # DOWNSAMPLE
downsample_6 = Conv1D(filters=Fc*7, kernel_size=15, padding='same')(downsample_5)
downsample_6 = LeakyReLU(alpha=0.05) (downsample_6)# ACTIVATION
downsample_6 = MaxPooling1D(pool_size=2)(downsample_6) # DOWNSAMPLE
downsample_7 = Conv1D(filters=Fc*8, kernel_size=15, padding='same')(downsample_6)
downsample_7 = LeakyReLU(alpha=0.05) (downsample_7)# ACTIVATION
downsample_7 = MaxPooling1D(pool_size=2)(downsample_7) # DOWNSAMPLE
downsample_8 = Conv1D(filters=Fc*9, kernel_size=15, padding='same')(downsample_7)
downsample_8 = LeakyReLU(alpha=0.05) (downsample_8)# ACTIVATION
downsample_8 = MaxPooling1D(pool_size=2)(downsample_8) # DOWNSAMPLE
downsample_9 = Conv1D(filters=Fc*10, kernel_size=15, padding='same')(downsample_8)
downsample_9 = LeakyReLU(alpha=0.05) (downsample_9)# ACTIVATION
downsample_9 = MaxPooling1D(pool_size=2)(downsample_9) # DOWNSAMPLE
downsample_10 = Conv1D(filters=Fc*11, kernel_size=15, padding='same')(downsample_9)
downsample_10 = LeakyReLU(alpha=0.05) (downsample_10)# ACTIVATION
downsample_10 = MaxPooling1D(pool_size=2)(downsample_10) # DOWNSAMPLE
downsample_11 = Conv1D(filters=Fc*12, kernel_size=15, padding='same')(downsample_10)
downsample_11 = LeakyReLU(alpha=0.05) (downsample_11)# ACTIVATION
downsample_11 = MaxPooling1D(pool_size=2)(downsample_11) # DOWNSAMPLE
# =====================================================
# consider extending this so that shape in center reaches 4 or even 2 (12 layer)
upsample_11 = Conv1D(filters=Fc*12, kernel_size=5, padding='same')(downsample_11)
upsample_11 = LeakyReLU(alpha=0.05)(upsample_11) # ACTIVATION
upsample_11 = UpSampling1D(size=2)(upsample_11) # UPSAMPLE
upsample_10 = concatenate([upsample_11, downsample_10])
upsample_10 = Conv1D(filters=Fc*11, kernel_size=5, padding='same')(upsample_10)
upsample_10 = LeakyReLU(alpha=0.05)(upsample_10) # ACTIVATION
upsample_10 = UpSampling1D(size=2)(upsample_10) # UPSAMPLE
upsample_9 = concatenate([upsample_10, downsample_9])
upsample_9 = Conv1D(filters=Fc*10, kernel_size=5, padding='same')(upsample_9)
upsample_9 = LeakyReLU(alpha=0.05)(upsample_9) # ACTIVATION
upsample_9 = UpSampling1D(size=2)(upsample_9) # UPSAMPLE
upsample_8 = concatenate([upsample_9, downsample_8])
upsample_8 = Conv1D(filters=Fc*9, kernel_size=5, padding='same')(upsample_8)
upsample_8 = LeakyReLU(alpha=0.05)(upsample_8) # ACTIVATION
upsample_8 = UpSampling1D(size=2)(upsample_8) # UPSAMPLE
upsample_7 = concatenate([upsample_8, downsample_7])
upsample_7 = Conv1D(filters=Fc*8, kernel_size=5, padding='same')(upsample_7)
upsample_7 = LeakyReLU(alpha=0.05)(upsample_7) # ACTIVATION
upsample_7 = UpSampling1D(size=2)(upsample_7) # UPSAMPLE
upsample_6 = concatenate([upsample_7, downsample_6])
upsample_6 = Conv1D(filters=Fc*7, kernel_size=5, padding='same')(upsample_6)
upsample_6 = LeakyReLU(alpha=0.05)(upsample_6) # ACTIVATION
upsample_6 = UpSampling1D(size=2)(upsample_6) # UPSAMPLE
upsample_5 = concatenate([upsample_6, downsample_5])
upsample_5 = Conv1D(filters=Fc*6, kernel_size=5, padding='same')(upsample_5)
upsample_5 = LeakyReLU(alpha=0.05)(upsample_5) # ACTIVATION
upsample_5 = UpSampling1D(size=2)(upsample_5) # UPSAMPLE
upsample_4 = concatenate([upsample_5, downsample_4])
upsample_4 = Conv1D(filters=Fc*5, kernel_size=5, padding='same')(upsample_4)
upsample_4 = LeakyReLU(alpha=0.05)(upsample_4) # ACTIVATION
upsample_4 = UpSampling1D(size=2)(upsample_4) # UPSAMPLE
upsample_3 = concatenate([upsample_4, downsample_3])
upsample_3 = Conv1D(filters=Fc*4, kernel_size=5, padding='same')(upsample_3)
upsample_3 = LeakyReLU(alpha=0.05)(upsample_3) # ACTIVATION
upsample_3 = UpSampling1D(size=2)(upsample_3) # UPSAMPLE
upsample_2 = concatenate([upsample_3, downsample_2])
upsample_2 = Conv1D(filters=Fc*3, kernel_size=5, padding='same')(upsample_2)
upsample_2 = LeakyReLU(alpha=0.05)(upsample_2) # ACTIVATION
upsample_2 = UpSampling1D(size=2)(upsample_2) # UPSAMPLE
upsample_1 = concatenate([upsample_2, downsample_1])
upsample_1 = Conv1D(filters=Fc*2, kernel_size=5, padding='same')(upsample_1)
upsample_1 = LeakyReLU(alpha=0.05)(upsample_1) # ACTIVATION
upsample_1 = UpSampling1D(size=2)(upsample_1) # UPSAMPLE
upsample_0 = concatenate([upsample_1, downsample_0]) # CONCATENATE SKIP
upsample_0 = Conv1D(filters=Fc*1, kernel_size=1, padding='same')(upsample_0)
upsample_0 = LeakyReLU(alpha=0.05)(upsample_0) # ACTIVATION
upsample_0 = UpSampling1D(size=2)(upsample_0) # UPSAMPLE
output_layer = Conv1D(filters=sources_to_estimate, kernel_size=1, padding='same')(upsample_0)
model = Model(input_layer, output_layer)
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
model.summary()
cb = callback(x_val, y_val, model, num_tests=1)
result = model.fit(x_train,
y_train,
batch_size=batch_size,
shuffle=True,
epochs=num_epochs,
validation_data=(x_val, y_val),
callbacks=[cb])
model.save('/content/drive/My Drive/Datasets/MultitrackStems/models/oh_to_snare_5_epoch.h5')
# ========= CONTINUE TRAINING ==================
epochs_continue = 30
cb = callback(x_val, y_val, model, num_tests=1)
result = model.fit(x_train,
y_train,
batch_size=batch_size,
shuffle=True,
epochs=epochs_continue,
validation_data=(x_val, y_val),
callbacks=[cb])
# ========= MODEL TESTING WITH VALIDATION SET ===========
rand_range = np.random.randint(0,high=len(x_val)-1)
x_test = x_val[rand_range:rand_range+10, :, :]
y_test = model.predict(x_test)
z_test = y_val[rand_range:rand_range+10, :, :]
print(y_test.shape)
for i in range(y_test.shape[0]):
print('x vs predicted y')
plt.plot(x_test[i, :, 0], color='red')
plt.plot(y_test[i, :, 0])
plt.show()
print('ground truth y vs predicted y')
plt.plot(z_test[i, :, 0], color='red')
plt.plot(y_test[i, :, 0])
plt.show()
print('input sample:')
ipd.display(ipd.Audio(x_test[i, :, 0], rate=44100, autoplay=False))
print('ground truth:')
ipd.display(ipd.Audio(z_test[i, :, 0], rate=44100, autoplay=False))
print('prediction')
ipd.display(ipd.Audio(y_test[i, :, 0], rate=44100, autoplay=False))
def test_model(model: "model to test",
audio_file: "path to audio sample",
win_size: "window size of model",
sample_rate=44100):
x_audio, _ = librosa.load(audio_file, sr=sample_rate)
num_win = x_audio.shape[0] // win_size
print(f'number of windows: {num_win}')
output = np.zeros((num_win * win_size))
x_audio = x_audio[:num_win*win_size]
x_test = x_audio.reshape((num_win, win_size, 1))
y_test = model.predict(x_test)
y_audio = y_test.reshape((num_win * win_size,))
return x_audio, y_audio
# ======= MODEL TESTING ==========
audio_file = '/content/Shroom LANDR Break04_70bpm.wav'
sample_rate = 44100
win_size = 16384
x_audio, y_audio = test_model(model, audio_file, win_size, sample_rate)
plt.plot(x_audio)
plt.plot(y_audio)
plt.show()
print('input audio')
ipd.display(ipd.Audio(x_audio, rate=sample_rate))
print('output prediction')
ipd.display(ipd.Audio(y_audio, rate=sample_rate))
mask = abs(y_audio)
plt.plot(mask)
plt.show()
masked = x_audio * mask
print('masked vs prediction')
plt.plot(y_audio)
plt.plot(masked)
plt.show()
print('masked vs ground truth')
plt.plot(x_audio)
plt.plot(masked)
plt.show()
print('ground truth')
ipd.display(ipd.Audio(x_audio, rate=sample_rate))
print('network prediction')
ipd.display(ipd.Audio(y_audio, rate=sample_rate))
print('masked')
ipd.display(ipd.Audio(masked, rate=sample_rate))
class TrainGenerator():
def __init__(self, audio_x, audio_y, win_size, batch_size):
self.audio_x = audio_x
self.audio_y = audio_y
self.audio_len = min(len(audio_x), len(audio_y)) # just in case there are 2 different sizes
self.win_size = win_size
self.batch_size = batch_size
def random_audio_sample(self):
startIdx = np.random.randint(0, high = self.audio_len - self.win_size)
sample_x = self.audio_x[startIdx:startIdx+self.win_size]
sample_y = self.audio_y[startIdx:startIdx+self.win_size]
return sample_x, sample_y
def generator(self):
while True:
x_batch = np.zeros((self.batch_size, self.win_size))
y_batch = np.zeros((self.batch_size, self.win_size))
for i in range(self.batch_size):
x, y = self.random_audio_sample()
x = x.reshape((1, x.shape[0]))
y = y.reshape((1, y.shape[0]))
x_batch[i, :] = x
y_batch[i, :] = y
yield x_batch, y_batch
def load_and_process_stems(x, y, sr): # work on this to produce all combinations of stems
x_audio = []
y_audio = []
for x_file in x:
this_x, _ = librosa.load(x_file, sr=sr, res_type='kaiser_fast')
x_audio.append(this_x)
for y_file in y:
this_y, _ = librosa.load(y_file, sr=sr, res_type='kaiser_fast')
y_audio.append(this_y)
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]