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sygst_train.py
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sygst_train.py
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import os
import math
import time
import argparse
import traceback
import numpy as np
import tensorflow as tf
from datetime import datetime
from tfr_dset import TFDataSet
from text import sequence_to_text
from utils import audio, plot, infolog, ValueWindow, debug
from sygst_hparams import hp
from models.sygst_tacotron2 import Tacotron2SYGST
log = infolog.log
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
_max_step = 500000
hdfs_ckpts='hdfs://haruna/home/byte_speech_sv/user/caixiong/ckpts'
# spec_length max = 1116
# text length max = 99
def time_string():
return datetime.now().strftime('%Y-%m-%d %H:%M')
def debug_data(batch=32, time_in=100, time_out=500):
text_x = np.random.randint(0, 150, size=(batch, time_in), dtype=np.int32)
mel = np.random.randn(batch, time_out, 80).astype(np.float32)
spec = np.random.randn(batch, time_out, 1025).astype(np.float32)
spec_len = np.random.randint(time_out // 2, time_out, size=batch, dtype=np.int32)
aro_label = np.random.rand(batch, 2).astype(np.float32)
val_label = np.random.rand(batch, 2).astype(np.float32)
print('text_input:', text_x[0], 'spec_len:', spec_len, sep='\n')
return text_x, mel, spec, spec_len, aro_label, val_label
def train(log_dir, args):
checkpoint_path = os.path.join(hdfs_ckpts, log_dir, 'model.ckpt')
log(hp.to_string(), is_print=False)
log('Loading training data from: %s' % args.tfr_dir)
log('Checkpoint path: %s' % checkpoint_path)
log('Using model: sygst tacotron2')
tf_dset = TFDataSet(hp, args.tfr_dir)
feats = tf_dset.get_train_next()
# Set up model:
global_step = tf.Variable(0, name='global_step', trainable=False)
training = tf.placeholder_with_default(True, shape=(), name='training')
with tf.name_scope('model'):
model = Tacotron2SYGST(hp)
model(feats['inputs'],
mel_inputs=feats['mel_targets'],
spec_inputs=feats['linear_targets'],
spec_lengths=feats['spec_lengths'],
ref_inputs=feats['mel_targets'],
ref_lengths=feats['spec_lengths'],
arousal_labels=feats['soft_arousal_labels'],
valence_labels=feats['soft_valance_labels'],
training=training)
"""
text_x, mel_x, spec_x, spec_len, aro, val = debug_data(2, 5, 10)
model(text_x, mel_x, spec_x, spec_len, mel_x, spec_len, aro, val, training=training)
"""
model.add_loss()
model.add_optimizer(global_step)
stats = model.add_stats()
# Bookkeeping:
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=50, keep_checkpoint_every_n_hours=2)
# Train!
config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=tf.GPUOptions(allow_growth=True))
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
if args.restore_step:
# Restore from a checkpoint if the user requested it.
restore_path = '%s-%s' % (checkpoint_path, args.restore_step)
saver.restore(sess, restore_path)
log('Resuming from checkpoint: %s' % restore_path, slack=True)
else:
log('Starting a new training run ...', slack=True)
"""
fetches = [global_step, model.optimize, model.loss, model.mel_loss, model.spec_loss,
model.stop_loss, model.arousal_loss, model.valence_loss, model.mel_grad_norms_max,
model.spec_grad_norms_max, model.stop_grad_norms_max, model.aro_grad_norms_max, model.val_grad_norms_max]
"""
fetches = [global_step, model.optimize, model.loss, model.mel_loss, model.spec_loss,
model.stop_loss, model.arousal_loss, model.valence_loss]
for _ in range(_max_step):
start_time = time.time()
sess.run(debug.get_ops())
# step, _, loss, mel_loss, spec_loss, stop_loss, aro_loss, val_loss, mel_g, spec_g, stop_g, aro_g, val_g = sess.run(fetches)
step, _, loss, mel_loss, spec_loss, stop_loss, aro_loss, val_loss = sess.run(fetches)
time_window.append(time.time() - start_time)
loss_window.append(loss)
"""
message = 'Step %-7d [%.3f sec/step,ml=%.3f,spl=%.3f,sl=%.3f,al=%.3f,vl=%.3f,mg=%.4f,spg=%.4f,sg=%.4f,ag=%.4f,vg=%.4f]' % (
step, time_window.average, mel_loss, spec_loss, stop_loss, aro_loss, val_loss, mel_g, spec_g, stop_g, aro_g, val_g)
"""
message = 'Step %-7d [%.3f sec/step,ml=%.3f,spl=%.3f,sl=%.3f,al=%.3f,vl=%.3f]' % (
step, time_window.average, mel_loss, spec_loss, stop_loss, aro_loss, val_loss)
log(message, slack=(step % args.checkpoint_interval == 0))
if loss > 100 or math.isnan(loss):
log('Loss exploded to %.5f at step %d!' % (loss, step), slack=True)
raise Exception('Loss Exploded')
if step % args.summary_interval == 0:
log('Writing summary at step: %d' % step)
try:
summary_writer.add_summary(sess.run(stats), step)
except Exception as e:
log(f'summary failed and ignored: {str(e)}')
if step % args.checkpoint_interval == 0:
log('Saving checkpoint to: %s-%d' % (checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
log('Saving audio and alignment...')
gt_mel, gt_spec, seq, mel, spec, align = sess.run([model.mel_targets[0], model.spec_targets[0],
model.text_targets[0], model.mel_outputs[0],
model.spec_outputs[0], model.alignment_outputs[0]])
text = sequence_to_text(seq)
wav = audio.inv_spectrogram(hp, spec.T)
wav_path = os.path.join(log_dir, 'step-%d-audio.wav' % step)
mel_path = os.path.join(log_dir, 'step-%d-mel.png' % step)
spec_path = os.path.join(log_dir, 'step-%d-spec.png' % step)
align_path = os.path.join(log_dir, 'step-%d-align.png' % step)
info = '%s, %s, step=%d, loss=%.5f\n %s' % (args.model, time_string(), step, loss, text)
plot.plot_alignment(align, align_path, info=info)
plot.plot_mel(mel, mel_path, info=info, gt_mel=gt_mel)
plot.plot_mel(spec, spec_path, info=info, gt_mel=gt_spec)
audio.save_wav(hp, wav, wav_path)
log('Input: %s' % text)
except Exception as e:
log('Exiting due to exception: %s' % e, slack=True)
traceback.print_exc()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', default='0')
parser.add_argument('--log', '-l', default='')
parser.add_argument('--restore_step', '-r', default=None)
parser.add_argument('--tfr_dir', default='bc2013/training/tfrs_with_emo_feature')
args = parser.parse_args()
args.model = 'sygst_taco2'
args.summary_interval = 200
args.checkpoint_interval = 5000
# args.summary_interval = 2
# args.checkpoint_interval = 5
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
log_dir = 'sygst_logs' + ('_' + args.log if args.log else '')
os.makedirs(log_dir, exist_ok=True)
tf.set_random_seed(hp.random_seed)
infolog.init(os.path.join(log_dir, 'train.log'), args.model)
train(log_dir, args)
if __name__ == '__main__':
main()