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convert.py
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convert.py
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import tensorflow as tf
from hyperparams import Hyperparams as hp
from net import *
import time
from data_load import *
from utils import normalize_0_1, denormalize_0_1
from audio import spec2wav, inv_preemphasis, db2amp, denormalize_db, amp2db
import os
from net import *
os.environ["CUDA_VISIBLE_DEVICES"]=""
def sumspecimage(spec, spec_name):
spec = denormalize_db(spec, hp.max_db, hp.min_db)
spec = db2amp(spec)
spec_image = spec.transpose(0,2,1)
heatmap = np.expand_dims(spec_image, 3)
tf.summary.image(spec_name, heatmap, max_outputs=spec_image.shape[0])
out_spec = np.power(np.maximum(spec, 0), 1) #hp.emphasis_magnitude)
out_audio = np.array(list(map(lambda spec: spec2wav(spec.T, hp.n_fft, hp.win_length, hp.hop_length,
hp.n_iter), out_spec)))
out_audio = inv_preemphasis(out_audio, coeff=hp.preemphasis)
tf.summary.audio(spec_name, out_audio, hp.sr, max_outputs=hp.batch_size)
def sumimage(mel, mel_name):
mel = mel #+ 0.001 * np.random.standard_normal([hp.batch_size, hp.duration * hp.n_mels, hp.n_mels])
mel_image = mel.transpose(0,2,1)
heatmap = np.expand_dims(mel_image, 3)
tf.summary.image(mel_name, heatmap, max_outputs=mel_image.shape[0])
mel_basis = librosa.filters.mel(hp.sr, hp.n_fft, hp.n_mels)
mel_basis = np.mat(mel_basis)
mel_basis_I = mel_basis.I
mel_spec = []
for i in range(len(mel)):
print(mel_name)
print(np.max(mel[i]))
print(np.min(mel[i]))
print(np.mean(mel[i]))
#mel[i] = mel[i] * (0.6 / np.max(mel[i]))
mel_db_item = np.transpose(mel[i])
mel_db_item = denormalize_0_1(mel_db_item, hp.max_db, hp.min_db)
#mel_db_item = np.maximum(mel_db_item, 0)
# = normalize_0_1(mel_db_item, hp.default.max_db, hp.default.min_db)
print(np.max(mel_db_item))
print(np.mean(mel_db_item))
mel_item = db2amp(mel_db_item)
print(np.max(mel_item))
mag_item = np.dot(mel_basis_I, mel_item)
print(np.max(mel_item))
mag_item = np.maximum(mag_item, 0)
spec_item = np.transpose(mag_item)
#mag_db_item = amp2db(mag_item)
#mag_db_item = normalize_0_1(mag_db_item, hp.default.max_db, hp.default.min_db)
#mag_db_item = np.transpose(mag_db_item)
#specitem = np.transpose(magitem)
#mel_complex = mel_D_abs + np.complex(0, 0)
#specitem = librosa.istft(stft_matrix=mel_complex, hop_length=hp.default.hop_length, win_length=hp.default.win_length)
mel_spec.append(spec_item.getA())
mel_spec = np.power(mel_spec, hp.emphasis_magnitude)
mel_audio = np.array(list(map(lambda spec: spec2wav(spec.T, hp.n_fft, hp.win_length, hp.hop_length,
hp.n_iter), mel_spec)))
mel_audio = inv_preemphasis(mel_audio, coeff=hp.preemphasis)
tf.summary.audio(mel_name, mel_audio, hp.sr, max_outputs=hp.batch_size)
def main():
g = S2SNet(is_training = False)
g.loadg()
test_path = "../data_thchs30/test-mini"
# init_op = tf.global_variables_initializer()
# g.sess.run(init_op)
d = NetDataFlow(test_path)
x_mels, x_specs, x_labels = d.get_data(4)
sumimage(np.array(x_mels), 'x_mel')
sumspecimage(np.array(x_specs), 'x_spec')
decoded, ppgs, mel, spec = g.sess.run([g.decoded, g.ppgs, g.g_mel, g.g_spec], \
feed_dict={ g.x_mel: x_mels, g.x_spec : x_specs, g.x_label : x_labels })
print(x_labels)
print(decoded)
f = open(test_path + "/phoneme.txt")
words = []
lines = f.readlines()
for w in lines:
words.append(w.replace('\n', ""))
words_size = len(words)
word_num_map = dict(zip(words, range(len(words)))) # 当字符不在已经收集的words中时，赋予其应当的num，这是一个动态的结果
num_word_map = {}
for k in word_num_map:
num_word_map[word_num_map[k]] = k
pingyin = ""
for i in x_labels[0]:
if i > 0 and i < hp.len_chinese_ppgs - 1:
pingyin += num_word_map[i] + " "
print(pingyin)
pingyin = ""
for i in decoded[0]:
if i < hp.len_chinese_ppgs - 1:
pingyin += num_word_map[i] + " "
print(pingyin)
pingyin = ""
for i in range(len(ppgs[0])):
pos = np.argmax(ppgs[0][i])
if pos > 0 and pos < hp.len_chinese_ppgs -1 : # and ppgs[0][i][pos] > 0.1:
pingyin += num_word_map[pos] + " "
else:
pingyin += "-"
print(pingyin)
sumimage(np.array(mel), 'g_mel')
sumspecimage(np.array(spec), 'g_spec')
summ = g.sess.run(tf.summary.merge_all(), feed_dict={ g.x_mel: x_mels, g.x_spec : x_specs, g.x_label : x_labels })
g.writer.add_summary(summ)
g.writer.close()
print('done')
if __name__ == "__main__":
main()