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speech2mri_FC-DNN_train.py
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'''
Written by Tamas Gabor Csapo <[email protected]>
First version Jan 21, 2019
Restructured Jan 21, 2020 - for MRI data
Keras implementation of Csapó T.G., ,,Speaker dependent acoustic-to-articulatory inversion using real-time MRI of the vocal tract'', accepted at Interspeech 2020
code for training FC-DNN
'''
import numpy as np
import matplotlib.pyplot as plt
import scipy.io.wavfile as io_wav
import os
import os.path
import datetime
import pickle
import cv2
import random
import vocoder_LSP_sptk
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
# do not use all GPU memory
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.3
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
# from LipReading with slight modifications
# https://github.com/hassanhub/LipReading/blob/master/codes/data_integration.py
################## VIDEO INPUT ##################
def load_video_3D(path, framesPerSec):
cap = cv2.VideoCapture(path)
frameCount = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frameHeight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT ))
frameWidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH ))
fps = cap.get(cv2.CAP_PROP_FPS)
# make sure that all the videos are the same FPS
if (np.abs(fps - framesPerSec) > 0.01):
print('fps:', fps, '(' + path + ')')
raise
buf = np.empty((frameHeight, frameWidth, frameCount), np.dtype('float32'))
fc = 0
ret = True
while (fc < frameCount and ret):
ret, frame = cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame = frame.astype('float32')
# min-max scaling to [0-1]
frame = frame-np.amin(frame)
# make sure not to divide by zero
if np.amax(frame) != 0:
frame = frame/np.amax(frame)
buf[:,:,fc]=frame
fc += 1
cap.release()
return buf
# load vocoder features,
# or calculate, if they are not available
def get_mgc_lsp_coeff(basefilename):
if os.path.isfile(basefilename + '.mgclsp'):
mgc_lsp_coeff = np.fromfile(basefilename + '.mgclsp', dtype=np.float32).reshape(-1, order + 1)
lf0 = np.fromfile(basefilename + '.lf0', dtype=np.float32)
else:
(mgc_lsp_coeff, lf0) = vocoder_LSP_sptk.encode(basefilename, samplingFrequency, frameLength, frameShift, order, alpha, stage)
return (mgc_lsp_coeff, lf0)
for speaker in ['f1', 'f2', 'm1', 'm2']:
# TODO: modify this according to your data path
dir_mri = '/home/csapot/deep_learning_mri/usctimit_mri/' + speaker + '/'
# Parameters of vocoder
samplingFrequency = 20000
frameLength = 1024 #
frameShift = 863 # 43.14 ms at 20000 Hz sampling, correspondong to 23.18 fps (MRI video)
order = 24
alpha = 0.42
stage = 3
n_mgc = order + 1
# properties of MRI videos
framesPerSec = 23.18
n_width = 68
n_height = 68
# USC-TIMIT contains 92 files (460 sentences) for each speaker
# train-valid-test split (random) :
# - 4 files for valid
# - 2 files for test
# - the remaining (86 files) for training
files_mri = dict()
mri = dict()
mgc = dict()
files_mri['all'] = []
if os.path.isdir(dir_mri):
for file in sorted(os.listdir(dir_mri)):
if ".avi" in file:
files_mri['all'] += [file]
# randomize file order
random.seed(17)
random.shuffle(files_mri['all'])
files_mri['valid'] = files_mri['all'][0:4]
files_mri['test'] = files_mri['all'][4:6]
files_mri['train'] = files_mri['all'][6:]
print('valid files', files_mri['valid'])
print('test files', files_mri['test']) # ['usctimit_mri_f1_146_150.avi', 'usctimit_mri_f1_441_445.avi']
for train_valid in ['train', 'valid']:
n_files = len(files_mri[train_valid])
n_file = 0
n_max_mri_frames = n_files * 1000
mri[train_valid] = np.empty((n_max_mri_frames, n_width, n_height))
mgc[train_valid] = np.empty((n_max_mri_frames, n_mgc))
mri_size = 0
mgc_size = 0
for file in files_mri[train_valid]:
try:
print('starting', train_valid, file)
mri_data = load_video_3D(dir_mri + file, framesPerSec)
(mgc_lsp_coeff, lf0) = get_mgc_lsp_coeff(dir_mri + file[:-4])
except ValueError as e:
print("wrong data, check manually!", e)
else:
print('minmax:', np.min(mri_data), np.max(mri_data))
n_file += 1
mgc_mri_len = np.min((mri_data.shape[2], len(mgc_lsp_coeff)))
mri_data = mri_data[:, :, 0:mgc_mri_len]
mgc_lsp_coeff = mgc_lsp_coeff[0:mgc_mri_len]
if mri_size + mgc_mri_len > n_max_mri_frames:
raise
for i in range(mgc_mri_len):
mri[train_valid][mri_size + i] = mri_data[:, :, i] # original, 68x68
mgc[train_valid][mgc_size + i] = mgc_lsp_coeff[i]
mri_size += mgc_mri_len
mgc_size += mgc_mri_len
print('n_frames_all: ', mri_size, 'mgc_size: ', mgc_size)
mri[train_valid] = mri[train_valid][0 : mri_size].reshape(-1, n_width*n_height)
mgc[train_valid] = mgc[train_valid][0 : mgc_size]
# target: min max scaler to [0,1] range
# already scaled in load_video
# input: normalization to zero mean, unit variance
# feature by feature
mgc_scalers = []
for i in range(n_mgc):
mgc_scaler = StandardScaler(with_mean=True, with_std=True)
mgc_scalers.append(mgc_scaler)
mgc['train'][:, i] = mgc_scalers[i].fit_transform(mgc['train'][:, i].reshape(-1, 1)).ravel()
mgc['valid'][:, i] = mgc_scalers[i].transform(mgc['valid'][:, i].reshape(-1, 1)).ravel()
### single training
model = Sequential()
model.add(Dense(1000, input_dim=n_mgc, kernel_initializer='normal', activation='relu'))
model.add(Dense(1000, kernel_initializer='normal', activation='relu'))
model.add(Dense(1000, kernel_initializer='normal', activation='relu'))
model.add(Dense(1000, kernel_initializer='normal', activation='relu'))
model.add(Dense(1000, kernel_initializer='normal', activation='relu'))
model.add(Dense(n_width*n_height, kernel_initializer='normal', activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
print(model.summary())
current_date = '{date:%Y-%m-%d_%H-%M-%S}'.format( date=datetime.datetime.now() )
model_name = 'models/SPEECH2MRI_FC-DNN_baseline_' + speaker + '_' + current_date
print('starting training', speaker, current_date)
# early stopping to avoid over-training
# csv logging of loss
# save best model
callbacks = [EarlyStopping(monitor='val_loss', patience=5, verbose=0), \
CSVLogger(model_name + '.csv', append=True, separator=';'),
ModelCheckpoint(model_name + '_weights.h5', monitor='val_loss')]
# run training
history = model.fit(mgc['train'], mri['train'],
epochs = 100, batch_size = 128, shuffle = True, verbose = 1,
validation_data=(mgc['valid'], mri['valid']),
callbacks=callbacks)
# here the training of the DNN is finished
# save model
model_json = model.to_json()
with open(model_name + '_model.json', "w") as json_file:
json_file.write(model_json)
# serialize scalers to pickle
pickle.dump(mgc_scalers, open(model_name + '_mgc_scalers.sav', 'wb'))
# save test files
with open(model_name + '_test_files.txt', 'w') as txt_file:
for file in files_mri['test']:
txt_file.write(file + '\n')
print('finished training', speaker, current_date)