def eval():
# Load graph
g = Graph(mode="eval"); print("Evaluation Graph loaded")
# Load data
fpaths, text_lengths, texts = load_data(mode="eval")
# Parse
text = np.fromstring(texts[0], np.int32) # (None,)
fname, mel, mag = load_spectrograms(fpaths[0])
x = np.expand_dims(text, 0) # (1, None)
y = np.expand_dims(mel, 0) # (1, None, n_mels*r)
z = np.expand_dims(mag, 0) # (1, None, n_mfccs)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(hp.logdir)); print("Restored!")
writer = tf.summary.FileWriter(hp.logdir, sess.graph)
# Feed Forward
## mel
y_hat = np.zeros((1, y.shape[1], y.shape[2]), np.float32) # hp.n_mels*hp.r
for j in range(y.shape[1]):
_y_hat = sess.run(g.y_hat, {g.x: x, g.y: y_hat})
y_hat[:, j, :] = _y_hat[:, j, :]
## mag
merged, gs = sess.run([g.merged, g.global_step], {g.x:x, g.y:y, g.y_hat: y_hat, g.z: z})
writer.add_summary(merged, global_step=gs)
writer.close()
def eval():
# Load graph
g = Graph(mode="eval"); print("Evaluation Graph loaded")
# Load data
fpaths, text_lengths, texts = load_data(mode="eval")
# Parse
text = np.fromstring(texts[0], np.int32) # (None,)
fname, mel, mag = load_spectrograms(fpaths[0])
x = np.expand_dims(text, 0) # (1, None)
y = np.expand_dims(mel, 0) # (1, None, n_mels*r)
z = np.expand_dims(mag, 0) # (1, None, n_mfccs)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(hp.logdir)); print("Restored!")
writer = tf.summary.FileWriter(hp.logdir, sess.graph)
# Feed Forward
## mel
y_hat = np.zeros((1, y.shape[1], y.shape[2]), np.float32) # hp.n_mels*hp.r
for j in range(y.shape[1]):
_y_hat = sess.run(g.y_hat, {g.x: x, g.y: y_hat})
y_hat[:, j, :] = _y_hat[:, j, :]
## mag
merged, gs = sess.run([g.merged, g.global_step], {g.x:x, g.y:y, g.y_hat: y_hat, g.z: z})
writer.add_summary(merged, global_step=gs)
writer.close()
def proc(fpath, hp, extension):
if not os.path.isfile(fpath):
return
fname, mel, mag, full_mel = load_spectrograms(hp, fpath)
np.save("{}/{}".format(hp.coarse_audio_dir, fname.replace(extension, ".npy")), mel)
np.save("{}/{}".format(hp.full_audio_dir, fname.replace(extension, ".npy")), mag)
np.save("{}/{}".format(hp.full_mel_dir, fname.replace(extension, ".npy")), full_mel)
def prepro(datadir):
# Load data
fpaths, _, _ = load_data(datadir) # list
for fpath in tqdm.tqdm(fpaths):
fname, mel, mag = load_spectrograms(fpath)
if not os.path.exists(datadir + hp.meldir):
os.makedirs(datadir + hp.meldir)
if not os.path.exists(datadir + hp.magdir):
os.makedirs(datadir + hp.magdir)
np.save(
datadir + hp.meldir + "/{}".format(fname.replace("wav", "npy")),
mel)
np.save(
datadir + hp.magdir + "/{}".format(fname.replace("wav", "npy")),
mag)
def synthesize():
if not os.path.exists(hp.sampledir): os.mkdir(hp.sampledir)
# Load data
texts = load_data(mode="synthesize")
# reference audio
mels, maxlen = [], 0
files = glob(hp.ref_audio)
for f in files:
_, mel, _= load_spectrograms(f)
mel = np.reshape(mel, (-1, hp.n_mels))
maxlen = max(maxlen, mel.shape[0])
mels.append(mel)
ref = np.zeros((len(mels), maxlen, hp.n_mels), np.float32)
for i, m in enumerate(mels):
ref[i, :m.shape[0], :] = m
# Load graph
g = Graph(mode="synthesize"); print("Graph loaded")
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(hp.logdir)); print("Restored!")
# Feed Forward
## mel
y_hat = np.zeros((texts.shape[0], 200, hp.n_mels*hp.r), np.float32) # hp.n_mels*hp.r
for j in tqdm.tqdm(range(200)):
_y_hat = sess.run(g.y_hat, {g.x: texts, g.y: y_hat, g.ref: ref})
y_hat[:, j, :] = _y_hat[:, j, :]
## mag
mags = sess.run(g.z_hat, {g.y_hat: y_hat})
for i, mag in enumerate(mags):
print("File {}.wav is being generated ...".format(i+1))
audio = spectrogram2wav(mag)
write(os.path.join(hp.sampledir, '{}.wav'.format(i+1)), hp.sr, audio)
import pygame
from os import listdir
from os.path import isfile, join
existing_samples = [f for f in listdir("LJSpeech-1.1/wavs") if isfile(join("LJSpeech-1.1/wavs", f))]
existing_identifiers = [sample.replace(".wav", "") for sample in existing_samples]
print("{} existing samples found. Preloading transcript...".format(len(existing_samples)))
with open("LJSpeech-1.1/metadata_all.csv", "r") as all_metadata_file:
with open("LJSpeech-1.1/transcript.csv", "w") as transcript_file:
for row in all_metadata_file.readlines():
splitted_row = row.split("|")
identifier = splitted_row[0]
if identifier in existing_identifiers:
transcript_file.write(row)
print("Done with preloading transcript.")
# Load data
fpaths, _, _ = load_data() # list
for fpath in tqdm.tqdm(fpaths):
fname, mel, mag = load_spectrograms(fpath)
if not os.path.exists("mels"): os.mkdir("mels")
if not os.path.exists("mags"): os.mkdir("mags")
np.save("mels/{}".format(fname.replace("wav", "npy")), mel)
np.save("mags/{}".format(fname.replace("wav", "npy")), mag)
def f(fpath):
fname, mel, mag = load_spectrograms(fpath)
np.save("mels/{}".format(fname.replace("wav", "npy")), mel)
np.save("mags/{}".format(fname.replace("wav", "npy")), mag)
return None
import numpy as np
from hyperparams import Hyperparams as hp
import tqdm
if not os.path.exists("mels"): os.mkdir("mels")
if not os.path.exists("worlds"): os.mkdir("worlds")
X = []
Y = []
XTest = []
YTest = []
# data directory
data_list = os.listdir(hp.data_dir)
data_lenght = len(data_list)
for i in tqdm.tqdm(range(0, data_lenght)):
try:
#print(i,'de: ',data_lenght)
mel = np.array(load_spectrograms(hp.data_dir + '/' +
data_list[i])) # mel spectrogram
world = wav2world(hp.data_dir + '/' + data_list[i])
num_padding = mel.shape[0] * 8 - world.shape[0]
world = np.pad(world, [[0, num_padding], [0, 0]], mode="constant")
np.save("mels/{}".format(data_list[i].replace("wav", "npy")), mel)
np.save("worlds/{}".format(data_list[i].replace("wav", "npy")), world)
except:
continue
print('preprocessing ok !!!')
def evaluate():
# Load graph
g = Graph(mode="evaluate")
print("Graph loaded")
# Load data
fpaths, _, texts = load_data(mode="evaluate")
lengths = [len(t) for t in texts]
maxlen = sorted(lengths, reverse=True)[0]
new_texts = np.zeros((len(texts), maxlen), np.int32)
for i, text in enumerate(texts):
new_texts[i, :len(text)] = [idx for idx in text]
#new_texts = np.split(new_texts, 2)
new_texts = new_texts[:evaluate_wav_num]
half_size = int(len(fpaths) / 2)
print(half_size)
#new_fpaths = [fpaths[:half_size], fpaths[half_size:]]
fpaths = fpaths[:evaluate_wav_num]
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(hp.logdir))
print("Evaluate Model Restored!")
"""
err = 0.0
for i, t_split in enumerate(new_texts):
y_hat = np.zeros((t_split.shape[0], 200, hp.n_mels*hp.r), np.float32) # hp.n_mels*hp.r
for j in tqdm.tqdm(range(200)):
_y_hat = sess.run(g.y_hat, {g.x: t_split, g.y: y_hat})
y_hat[:, j, :] = _y_hat[:, j, :]
mags = sess.run(g.z_hat, {g.y_hat: y_hat})
for k, mag in enumerate(mags):
fname, mel_ans, mag_ans = load_spectrograms(new_fpaths[i][k])
print("File {} is being evaluated ...".format(fname))
audio = spectrogram2wav(mag)
audio_ans = spectrogram2wav(mag_ans)
err += calculate_mse(audio, audio_ans)
err = err/float(len(fpaths))
print(err)
"""
# Feed Forward
## mel
y_hat = np.zeros((new_texts.shape[0], 200, hp.n_mels * hp.r),
np.float32) # hp.n_mels*hp.r
for j in tqdm.tqdm(range(200)):
_y_hat = sess.run(g.y_hat, {g.x: new_texts, g.y: y_hat})
y_hat[:, j, :] = _y_hat[:, j, :]
## mag
mags = sess.run(g.z_hat, {g.y_hat: y_hat})
err = 0.0
for i, mag in enumerate(mags):
fname, mel_ans, mag_ans = load_spectrograms(fpaths[i])
print("File {} is being evaluated ...".format(fname))
#audio = spectrogram2wav(mag)
#audio_ans = spectrogram2wav(mag_ans)
#err += calculate_mse(audio, audio_ans)
err += calculate_mse(mag, mag_ans)
err = err / float(len(fpaths))
print(err)
opf.write(hp.logdir + " spectrogram mse: " + str(err) + "\n")
for i in range(lenx):
mse = mse + RMSE(X[i], Y[i])
return mse / lenx
esperado_dir = '../avaliacao-subjetiva/Esperado/'
exp_dir = '../avaliacao-subjetiva/Preditos/'
exp_list = list(os.listdir(exp_dir))
esperado_list = list(os.listdir(esperado_dir))
esp_mag = []
esp_db = []
for i in esperado_list:
if i[-4:] == '.wav':
file_id = i[:-4]
_, _, mag = load_spectrograms(os.path.join(esperado_dir, i))
db = librosa.amplitude_to_db(mag, ref=np.max)
display.specshow(db, y_axis='log', x_axis='time')
save_img_dir = os.path.join(esperado_dir, i.replace('.wav', '.png'))
esp_mag.append([file_id, mag])
esp_db.append([file_id, db])
plt.title('Espectrograma STFT')
plt.colorbar(format='%+2.0f dB')
plt.tight_layout()
plt.savefig(save_img_dir)
plt.cla() # Clear axis
plt.clf()
from hyperparams import Hyperparams as hp
# import the necessary packages
from skimage.measure import structural_similarity as ssim
import matplotlib.pyplot as plt
import numpy as np
import cv2
cinco_org = np.load('../savedir/synthesized-audios/RTISI-LA/5-org.npy')
savedir = '../savedir/spectogramas/'
vocoders = ['RTISI-LA', 'Griff-Lim']
for voco in vocoders:
diretory = os.path.join('../savedir/synthesized-audios/', voco)
arquivos = ['1.wav', '5.wav', '5-org.wav', '3.wav', '3-org.wav']
for i in arquivos:
_, _, mag = load_spectrograms(os.path.join(diretory, i))
print(os.path.join(diretory, i), ' :', mag.shape)
if i == '5.wav':
mag = mag[:cinco_org.shape[0]][:]
np.save(os.path.join(diretory, i.replace('.wav', '')), mag)
# transpose
mag = mag.T
# de-noramlize
mag = (np.clip(mag, 0, 1) * hp.max_db) - hp.max_db + hp.ref_db
# to amplitude
mag = np.power(10.0, mag * 0.05)
mag = mag**hp.power
display.specshow(librosa.amplitude_to_db(mag, ref=np.max),
y_axis='log',
x_axis='time')
def synthesize():
if not os.path.exists(hp.sampledir):
os.mkdir(hp.sampledir)
# Load data
texts = load_data(mode="synthesize")
# pad texts to multiple of batch_size
texts_len = texts.shape[0]
num_batches = int(ceil(float(texts_len) / hp.batch_size))
padding_len = num_batches * hp.batch_size - texts_len
texts = np.pad(texts, ((0, padding_len), (0, 0)),
'constant',
constant_values=0)
# reference audio
mels, maxlen = [], 0
files = glob(hp.ref_audio)
for f in files:
_, mel, _ = load_spectrograms(f)
mel = np.reshape(mel, (-1, hp.n_mels))
maxlen = max(maxlen, mel.shape[0])
mels.append(mel)
ref = np.zeros((len(mels), maxlen, hp.n_mels), np.float32)
for i, m in enumerate(mels):
ref[i, :m.shape[0], :] = m
# Load graph
g = Graph(mode="synthesize")
print("Graph loaded")
saver = tf.train.Saver()
with tf.Session() as sess:
if len(sys.argv) == 1:
saver.restore(sess, tf.train.latest_checkpoint(hp.logdir))
print("Restored latest checkpoint")
else:
saver.restore(sess, sys.argv[1])
print("Restored checkpoint: %s" % sys.argv[1])
batches = [
texts[i:i + hp.batch_size]
for i in range(0, texts.shape[0], hp.batch_size)
]
start = 0
batch_index = 0
# Feed Forward
for batch in batches:
ref_batch, start = looper(ref, start, hp.batch_size)
## mel
y_hat = np.zeros((batch.shape[0], 200, hp.n_mels * hp.r),
np.float32) # hp.n_mels*hp.r
for j in tqdm.tqdm(range(200)):
_y_hat = sess.run(g.y_hat, {
g.x: batch,
g.y: y_hat,
g.ref: ref_batch
})
y_hat[:, j, :] = _y_hat[:, j, :]
## mag
mags = sess.run(g.z_hat, {g.y_hat: y_hat})
for i, mag in enumerate(mags):
index_label = batch_index * hp.batch_size + i + 1
if index_label > texts_len:
break
print("File {}.wav is being generated ...".format(index_label))
audio = spectrogram2wav(mag)
write(os.path.join(hp.sampledir, '{}.wav'.format(index_label)),
hp.sr, audio)
batch_index += 1
