deep-voice-conversion
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andabi
How to view the result of the conversion？
After run the convert.py, how can I find the result? How to use tensorboard? Thank you for your reply.
You can use this replace convert.py and you can get test.wav. That is the result. `# -- coding: utf-8 --
/usr/bin/python2
from future import print_function
import argparse
from data_load import get_wav_batch from models import Model import numpy as np from utils import spectrogram2wav, inv_preemphasis from hparams import logdir_path import datetime import tensorflow as tf from hparams import Default as hp_default import hparams as hp
import struct import wave
def out_range(num): if num > 30000: return 30000 elif num < -30000: return -30000 else: return num
def out_wav(result, file_name): n_frames = len(result) outfile = file_name out_wave = wave.open(outfile, 'wb') comp_type = "NONE" comp_name = "not compressed" out_wave.setparams((1, 2, 16000, n_frames, comp_type, comp_name)) MAX_M = np.max(np.abs(result))
for v in result:
out_wave.writeframes(struct.pack('h', out_range(v*10000)))
out_wave.close()
print('save success')
def convert(logdir='logdir/default/train2', queue=False):
# Load graph
model = Model(mode="convert", batch_size=hp.Convert.batch_size, queue=queue)
session_conf = tf.ConfigProto(
allow_soft_placement=True,
device_count={'CPU': 1, 'GPU': 0},
gpu_options=tf.GPUOptions(
allow_growth=True,
per_process_gpu_memory_fraction=0.9
),
)
with tf.Session(config=session_conf) as sess:
# Load trained model
sess.run(tf.global_variables_initializer())
model.load(sess, 'convert', logdir=logdir)
writer = tf.summary.FileWriter(logdir, sess.graph)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
gs = Model.get_global_step(logdir)
if queue:
pred_log_specs, y_log_spec, ppgs = sess.run([model(), model.y_spec, model.ppgs])
else:
mfcc, spec, mel = get_wav_batch(model.mode, model.batch_size)
pred_log_specs, y_log_spec, ppgs = sess.run([model(), model.y_spec, model.ppgs], feed_dict={model.x_mfcc: mfcc, model.y_spec: spec, model.y_mel: mel})
# Denormalizatoin
# pred_log_specs = hp.mean_log_spec + hp.std_log_spec * pred_log_specs
# y_log_spec = hp.mean_log_spec + hp.std_log_spec * y_log_spec
# pred_log_specs = hp.min_log_spec + (hp.max_log_spec - hp.min_log_spec) * pred_log_specs
# y_log_spec = hp.min_log_spec + (hp.max_log_spec - hp.min_log_spec) * y_log_spec
# Convert log of magnitude to magnitude
pred_specs, y_specs = np.e ** pred_log_specs, np.e ** y_log_spec
# Emphasize the magnitude
pred_specs = np.power(pred_specs, hp.Convert.emphasis_magnitude)
y_specs = np.power(y_specs, hp.Convert.emphasis_magnitude)
# Spectrogram to waveform
audio = np.array(map(lambda spec: spectrogram2wav(spec.T, hp_default.n_fft, hp_default.win_length, hp_default.hop_length, hp_default.n_iter), pred_specs))
y_audio = np.array(map(lambda spec: spectrogram2wav(spec.T, hp_default.n_fft, hp_default.win_length, hp_default.hop_length, hp_default.n_iter), y_specs))
# Apply inverse pre-emphasis
audio = inv_preemphasis(audio, coeff=hp_default.preemphasis)
y_audio = inv_preemphasis(y_audio, coeff=hp_default.preemphasis)
if not queue:
# Concatenate to a wav
y_audio = np.reshape(y_audio, (1, y_audio.size), order='C')
audio = np.reshape(audio, (1, audio.size), order='C')
out_wav(audio[0],str('test')+'.wav')
out_wav(y_audio[0],str('test')+'_y.wav')
# Write the result
tf.summary.audio('A', y_audio, hp_default.sr, max_outputs=hp.Convert.batch_size)
tf.summary.audio('B', audio, hp_default.sr, max_outputs=hp.Convert.batch_size)
# Visualize PPGs
heatmap = np.expand_dims(ppgs, 3) # channel=1
tf.summary.image('PPG', heatmap, max_outputs=ppgs.shape[0])
writer.add_summary(sess.run(tf.summary.merge_all()), global_step=gs)
writer.close()
coord.request_stop()
coord.join(threads)
def get_arguments(): parser = argparse.ArgumentParser() parser.add_argument('case', type=str, help='experiment case name') arguments = parser.parse_args() return arguments
if name == 'main': args = get_arguments() case = args.case logdir = '{}/{}/train2'.format(logdir_path, case)
print('case: {}, logdir: {}'.format(case, logdir))
s = datetime.datetime.now()
convert(logdir=logdir)
e = datetime.datetime.now()
diff = e - s
print("Done. elapsed time:{}s".format(diff.seconds))
`
nice and tidy now
# -- coding: utf-8 --
#/usr/bin/python2
from __future__ import print_function
import argparse
from data_load import get_wav_batch
from models import Model
import numpy as np
from utils import spectrogram2wav, inv_preemphasis
from hparams import logdir_path
import datetime
import tensorflow as tf
from hparams import Default as hp_default
import hparams as hp
import struct
import wave
def out_range(num):
if num > 30000:
return 30000
elif num < -30000:
return -30000
else:
return num
def out_wav(result, file_name):
n_frames = len(result)
outfile = file_name
out_wave = wave.open(outfile, 'wb')
comp_type = "NONE"
comp_name = "not compressed"
out_wave.setparams((1, 2, 16000, n_frames, comp_type, comp_name))
MAX_M = np.max(np.abs(result))
for v in result:
out_wave.writeframes(struct.pack('h', out_range(v*10000)))
out_wave.close()
print('save success')
def convert(logdir='logdir/default/train2', queue=False):
# Load graph
model = Model(mode="convert", batch_size=hp.Convert.batch_size, queue=queue)
session_conf = tf.ConfigProto(
allow_soft_placement=True,
device_count={'CPU': 1, 'GPU': 0},
gpu_options=tf.GPUOptions(
allow_growth=True,
per_process_gpu_memory_fraction=0.9
),
)
with tf.Session(config=session_conf) as sess:
# Load trained model
sess.run(tf.global_variables_initializer())
model.load(sess, 'convert', logdir=logdir)
writer = tf.summary.FileWriter(logdir, sess.graph)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
gs = Model.get_global_step(logdir)
if queue:
pred_log_specs, y_log_spec, ppgs = sess.run([model(), model.y_spec, model.ppgs])
else:
mfcc, spec, mel = get_wav_batch(model.mode, model.batch_size)
pred_log_specs, y_log_spec, ppgs = sess.run([model(), model.y_spec, model.ppgs], feed_dict={model.x_mfcc: mfcc, model.y_spec: spec, model.y_mel: mel})
# Denormalizatoin
# pred_log_specs = hp.mean_log_spec + hp.std_log_spec * pred_log_specs
# y_log_spec = hp.mean_log_spec + hp.std_log_spec * y_log_spec
# pred_log_specs = hp.min_log_spec + (hp.max_log_spec - hp.min_log_spec) * pred_log_specs
# y_log_spec = hp.min_log_spec + (hp.max_log_spec - hp.min_log_spec) * y_log_spec
# Convert log of magnitude to magnitude
pred_specs, y_specs = np.e ** pred_log_specs, np.e ** y_log_spec
# Emphasize the magnitude
pred_specs = np.power(pred_specs, hp.Convert.emphasis_magnitude)
y_specs = np.power(y_specs, hp.Convert.emphasis_magnitude)
# Spectrogram to waveform
audio = np.array(map(lambda spec: spectrogram2wav(spec.T, hp_default.n_fft, hp_default.win_length, hp_default.hop_length, hp_default.n_iter), pred_specs))
y_audio = np.array(map(lambda spec: spectrogram2wav(spec.T, hp_default.n_fft, hp_default.win_length, hp_default.hop_length, hp_default.n_iter), y_specs))
# Apply inverse pre-emphasis
audio = inv_preemphasis(audio, coeff=hp_default.preemphasis)
y_audio = inv_preemphasis(y_audio, coeff=hp_default.preemphasis)
if not queue:
# Concatenate to a wav
y_audio = np.reshape(y_audio, (1, y_audio.size), order='C')
audio = np.reshape(audio, (1, audio.size), order='C')
out_wav(audio[0],str('test')+'.wav')
out_wav(y_audio[0],str('test')+'_y.wav')
# Write the result
tf.summary.audio('A', y_audio, hp_default.sr, max_outputs=hp.Convert.batch_size)
tf.summary.audio('B', audio, hp_default.sr, max_outputs=hp.Convert.batch_size)
# Visualize PPGs
heatmap = np.expand_dims(ppgs, 3) # channel=1
tf.summary.image('PPG', heatmap, max_outputs=ppgs.shape[0])
writer.add_summary(sess.run(tf.summary.merge_all()), global_step=gs)
writer.close()
coord.request_stop()
coord.join(threads)
def get_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('case', type=str, help='experiment case name')
arguments = parser.parse_args()
return arguments
if __name__ == '__main__':
args = get_arguments()
case = args.case
logdir = '{}/{}/train2'.format(logdir_path, case)
print('case: {}, logdir: {}'.format(case, logdir))
s = datetime.datetime.now()
convert(logdir=logdir)
e = datetime.datetime.now()
diff = e - s
print("Done. elapsed time:{}s".format(diff.seconds))
"get_wav_batch" seems note in data_load.py, anyone run this successfully?
I have the same question. I run cmd python convert.py, and no wav file is generate.
it seems that you can use tensorboard and see the result in audio partition
Can someone please explain how to use tensorboard to view the final wav file.
