本文整理汇总了Python中utils.load_spectrograms方法的典型用法代码示例。如果您正苦于以下问题：Python utils.load_spectrograms方法的具体用法？Python utils.load_spectrograms怎么用？Python utils.load_spectrograms使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类utils的用法示例。
在下文中一共展示了utils.load_spectrograms方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: eval
# 需要导入模块: import utils [as 别名]
# 或者: from utils import load_spectrograms [as 别名]
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() 示例2: proc
# 需要导入模块: import utils [as 别名]
# 或者: from utils import load_spectrograms [as 别名]
def proc(fpath, hp):
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("wav", "npy")), mel)
np.save("{}/{}".format(hp.full_audio_dir, fname.replace("wav", "npy")), mag)
np.save("{}/{}".format(hp.full_mel_dir, fname.replace("wav", "npy")), full_mel) 示例3: evaluate
# 需要导入模块: import utils [as 别名]
# 或者: from utils import load_spectrograms [as 别名]
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")