def train(): save_dir = 'trained_model_t/' input_path = 'training_data/train.txt' checkpoint_path = os.path.join(save_dir, 'model.ckpt') plot_dir = os.path.join(save_dir, 'plots') wav_dir = os.path.join(save_dir, 'wavs') mel_dir = os.path.join(save_dir, 'mel-spectrograms') os.makedirs(plot_dir, exist_ok=True) os.makedirs(wav_dir, exist_ok=True) os.makedirs(mel_dir, exist_ok=True) # Set up data feeder coord = tf.train.Coordinator() with tf.variable_scope('datafeeder') as scope: feeder = Feeder(coord, input_path, hparams) # Set up model: step_count = 0 global_step = tf.Variable(step_count, name='global_step', trainable=False) with tf.variable_scope('model') as scope: model = Tacotron(hparams) model.initialize(feeder.inputs, feeder.input_lengths, feeder.mel_targets, feeder.token_targets) model.add_loss() model.add_optimizer(global_step) stats = add_stats(model) step = 0 saver = tf.train.Saver(max_to_keep=5) config = tf.ConfigProto() # Train with tf.Session(config=config) as sess: try: sess.run(tf.global_variables_initializer()) feeder.start_in_session(sess) # Training loop while not coord.should_stop(): step, loss, opt = sess.run( [global_step, model.loss, model.optimize]) message = 'Step {:7d} , loss={:.5f}'.format(step, loss) print(message) if step % 100 == 0: with open(os.path.join(save_dir, 'step_counter.txt'), 'w') as file: file.write(str(step)) print('Saving checkpoint to: {}-{}'.format( checkpoint_path, step)) saver.save(sess, checkpoint_path, global_step=step) except Exception as e: print('Exception: {}'.format(e)) traceback.print_exc() coord.request_stop(e)
def train(log_dir, args, hparams): save_dir = os.path.join(log_dir, 'taco_pretrained/') checkpoint_path = os.path.join(save_dir, 'tacotron_model.ckpt') input_path = os.path.join(args.base_dir, args.tacotron_input) plot_dir = os.path.join(log_dir, 'plots') wav_dir = os.path.join(log_dir, 'wavs') mel_dir = os.path.join(log_dir, 'mel-spectrograms') eval_dir = os.path.join(log_dir, 'eval-dir') eval_plot_dir = os.path.join(eval_dir, 'plots') eval_wav_dir = os.path.join(eval_dir, 'wavs') os.makedirs(eval_dir, exist_ok=True) os.makedirs(plot_dir, exist_ok=True) os.makedirs(wav_dir, exist_ok=True) os.makedirs(mel_dir, exist_ok=True) os.makedirs(eval_plot_dir, exist_ok=True) os.makedirs(eval_wav_dir, exist_ok=True) if hparams.predict_linear: linear_dir = os.path.join(log_dir, 'linear-spectrograms') os.makedirs(linear_dir, exist_ok=True) log('Checkpoint path: {}'.format(checkpoint_path)) log('Loading training data from: {}'.format(input_path)) log('Using model: {}'.format(args.model)) log(hparams_debug_string()) #Start by setting a seed for repeatability tf.set_random_seed(hparams.tacotron_random_seed) #Set up data feeder coord = tf.train.Coordinator() with tf.variable_scope('datafeeder') as scope: feeder = Feeder(coord, input_path, hparams) #Set up model: global_step = tf.Variable(0, name='global_step', trainable=False) model, stats = model_train_mode(args, feeder, hparams, global_step) eval_model = model_test_mode(args, feeder, hparams, global_step) #Book keeping step = 0 time_window = ValueWindow(100) loss_window = ValueWindow(100) saver = tf.train.Saver(max_to_keep=5) log('Tacotron training set to a maximum of {} steps'.format(args.tacotron_train_steps)) #Memory allocation on the GPU as needed config = tf.ConfigProto() config.gpu_options.allow_growth = True #Train with tf.Session(config=config) as sess: try: summary_writer = tf.summary.FileWriter(log_dir, sess.graph) sess.run(tf.global_variables_initializer()) #saved model restoring if args.restore: #Restore saved model if the user requested it, Default = True. try: checkpoint_state = tf.train.get_checkpoint_state(save_dir) except tf.errors.OutOfRangeError as e: log('Cannot restore checkpoint: {}'.format(e)) if (checkpoint_state and checkpoint_state.model_checkpoint_path): log('Loading checkpoint {}'.format(checkpoint_state.model_checkpoint_path)) saver.restore(sess, checkpoint_state.model_checkpoint_path) else: if not args.restore: log('Starting new training!') else: log('No model to load at {}'.format(save_dir)) #initializing feeder feeder.start_threads(sess) #Training loop while not coord.should_stop() and step < args.tacotron_train_steps: start_time = time.time() step, loss, opt = sess.run([global_step, model.loss, model.optimize]) time_window.append(time.time() - start_time) loss_window.append(loss) message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format( step, time_window.average, loss, loss_window.average) log(message, end='\r') if loss > 1000 or np.isnan(loss): log('Loss exploded to {:.5f} at step {}'.format(loss, step)) raise Exception('Loss exploded') if step % args.summary_interval == 0: log('\nWriting summary at step {}'.format(step)) summary_writer.add_summary(sess.run(stats), step) if step % args.eval_interval == 0: #Run eval and save eval stats log('\nRunning evaluation at step {}'.format(step)) eval_losses = [] before_losses = [] after_losses = [] stop_token_losses = [] linear_losses = [] linear_loss = None if hparams.predict_linear: for i in tqdm(range(feeder.test_steps)): eloss, before_loss, after_loss, stop_token_loss, linear_loss, mel_p, mel_t, t_len, align, lin_p = sess.run( [eval_model.loss, eval_model.before_loss, eval_model.after_loss, eval_model.stop_token_loss, eval_model.linear_loss, eval_model.mel_outputs[0], eval_model.mel_targets[0], eval_model.targets_lengths[0], eval_model.alignments[0], eval_model.linear_outputs[0]]) eval_losses.append(eloss) before_losses.append(before_loss) after_losses.append(after_loss) stop_token_losses.append(stop_token_loss) linear_losses.append(linear_loss) linear_loss = sum(linear_losses) / len(linear_losses) wav = audio.inv_linear_spectrogram(lin_p.T, hparams) audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-waveform-linear.wav'.format(step)), sr=hparams.sample_rate) else: for i in tqdm(range(feeder.test_steps)): eloss, before_loss, after_loss, stop_token_loss, mel_p, mel_t, t_len, align = sess.run( [eval_model.loss, eval_model.before_loss, eval_model.after_loss, eval_model.stop_token_loss, eval_model.mel_outputs[0], eval_model.mel_targets[0], eval_model.targets_lengths[0], eval_model.alignments[0]]) eval_losses.append(eloss) before_losses.append(before_loss) after_losses.append(after_loss) stop_token_losses.append(stop_token_loss) eval_loss = sum(eval_losses) / len(eval_losses) before_loss = sum(before_losses) / len(before_losses) after_loss = sum(after_losses) / len(after_losses) stop_token_loss = sum(stop_token_losses) / len(stop_token_losses) log('Saving eval log to {}..'.format(eval_dir)) #Save some log to monitor model improvement on same unseen sequence wav = audio.inv_mel_spectrogram(mel_p.T, hparams) audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-waveform-mel.wav'.format(step)), sr=hparams.sample_rate) plot.plot_alignment(align, os.path.join(eval_plot_dir, 'step-{}-eval-align.png'.format(step)), info='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, eloss), max_len=t_len // hparams.outputs_per_step) plot.plot_spectrogram(mel_p, os.path.join(eval_plot_dir, 'step-{}-eval-mel-spectrogram.png'.format(step)), info='{}, {}, step={}, loss={:.5}'.format(args.model, time_string(), step, eloss), target_spectrogram=mel_t, max_len=t_len) log('Eval loss for global step {}: {:.3f}'.format(step, eval_loss)) log('Writing eval summary!') add_eval_stats(summary_writer, step, linear_loss, before_loss, after_loss, stop_token_loss, eval_loss) if step % args.checkpoint_interval == 0: #Save model and current global step saver.save(sess, checkpoint_path, global_step=global_step) log('\nSaving alignment, Mel-Spectrograms and griffin-lim inverted waveform..') if hparams.predict_linear: input_seq, mel_prediction, linear_prediction, alignment, target, target_length = sess.run([ model.inputs[0], model.mel_outputs[0], model.linear_outputs[0], model.alignments[0], model.mel_targets[0], model.targets_lengths[0], ]) #save predicted linear spectrogram to disk (debug) linear_filename = 'linear-prediction-step-{}.npy'.format(step) np.save(os.path.join(linear_dir, linear_filename), linear_prediction.T, allow_pickle=False) #save griffin lim inverted wav for debug (linear -> wav) wav = audio.inv_linear_spectrogram(linear_prediction.T, hparams) audio.save_wav(wav, os.path.join(wav_dir, 'step-{}-wave-from-linear.wav'.format(step)), sr=hparams.sample_rate) else: input_seq, mel_prediction, alignment, target, target_length = sess.run([model.inputs[0], model.mel_outputs[0], model.alignments[0], model.mel_targets[0], model.targets_lengths[0], ]) #save predicted mel spectrogram to disk (debug) mel_filename = 'mel-prediction-step-{}.npy'.format(step) np.save(os.path.join(mel_dir, mel_filename), mel_prediction.T, allow_pickle=False) #save griffin lim inverted wav for debug (mel -> wav) wav = audio.inv_mel_spectrogram(mel_prediction.T, hparams) audio.save_wav(wav, os.path.join(wav_dir, 'step-{}-wave-from-mel.wav'.format(step)), sr=hparams.sample_rate) #save alignment plot to disk (control purposes) plot.plot_alignment(alignment, os.path.join(plot_dir, 'step-{}-align.png'.format(step)), info='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, loss), max_len=target_length // hparams.outputs_per_step) #save real and predicted mel-spectrogram plot to disk (control purposes) plot.plot_spectrogram(mel_prediction, os.path.join(plot_dir, 'step-{}-mel-spectrogram.png'.format(step)), info='{}, {}, step={}, loss={:.5}'.format(args.model, time_string(), step, loss), target_spectrogram=target, max_len=target_length) log('Input at step {}: {}'.format(step, sequence_to_text(input_seq))) log('Tacotron training complete after {} global steps!'.format(args.tacotron_train_steps)) return save_dir except Exception as e: log('Exiting due to exception: {}'.format(e)) traceback.print_exc() coord.request_stop(e)
def train(log_dir, args, hparams): save_dir = os.path.join(log_dir, 'taco_pretrained') plot_dir = os.path.join(log_dir, 'plots') wav_dir = os.path.join(log_dir, 'wavs') mel_dir = os.path.join(log_dir, 'mel-spectrograms') tensorboard_dir = os.path.join(log_dir, 'tacotron_events') os.makedirs(save_dir, exist_ok=True) os.makedirs(plot_dir, exist_ok=True) os.makedirs(wav_dir, exist_ok=True) os.makedirs(mel_dir, exist_ok=True) os.makedirs(tensorboard_dir, exist_ok=True) checkpoint_path = os.path.join(save_dir, 'tacotron_model.ckpt') input_path = args.input_dir log('Checkpoint path: {}'.format(checkpoint_path)) log('Loading training data from: {}'.format(input_path)) log(hparams_debug_string()) #Start by setting a seed for repeatability tf.set_random_seed(hparams.tacotron_random_seed) #Set up data feeder coord = tf.train.Coordinator() with tf.variable_scope('datafeeder') as scope: feeder = Feeder(coord, input_path, hparams) #Set up model: global_step = tf.Variable(0, name='global_step', trainable=False) with tf.variable_scope('Tacotron_model', reuse=tf.AUTO_REUSE) as scope: model = Tacotron(hparams) model.initialize(feeder.inputs, feeder.input_lengths, feeder.mel_targets, feeder.token_targets, targets_lengths=feeder.targets_lengths, global_step=global_step, is_training=True, split_infos=feeder.split_infos) model.add_loss() model.add_optimizer(global_step) stats = _add_train_stats(model, hparams) GLGPU_mel_inputs = tf.placeholder(tf.float32, (None, hparams.num_mels), name='GLGPU_mel_inputs') GLGPU_mel_outputs = audio.inv_mel_spectrogram_tensorflow(GLGPU_mel_inputs, hparams) #Book keeping step = 0 time_window = ValueWindow(100) loss_window = ValueWindow(100) saver = tf.train.Saver(max_to_keep=20) log('Tacotron training set to a maximum of {} steps'.format(args.tacotron_train_steps)) #Memory allocation on the GPU as needed config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True #Train with tf.Session(config=config) as sess: try: summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph) sess.run(tf.global_variables_initializer()) #saved model restoring if args.restore: # Restore saved model if the user requested it, default = True try: checkpoint_state = tf.train.get_checkpoint_state(save_dir) if (checkpoint_state and checkpoint_state.model_checkpoint_path): log('Loading checkpoint {}'.format(checkpoint_state.model_checkpoint_path), slack=True) saver.restore(sess, checkpoint_state.model_checkpoint_path) else: log('No model to load at {}'.format(save_dir), slack=True) saver.save(sess, checkpoint_path, global_step=global_step) except tf.errors.OutOfRangeError as e: log('Cannot restore checkpoint: {}'.format(e), slack=True) else: log('Starting new training!', slack=True) saver.save(sess, checkpoint_path, global_step=global_step) #initializing feeder feeder.start_threads(sess) #Training loop while not coord.should_stop() and step < args.tacotron_train_steps: start_time = time.time() step, loss, opt = sess.run([global_step, model.loss, model.optimize]) time_window.append(time.time() - start_time) loss_window.append(loss) message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format( step, time_window.average, loss, loss_window.average) log(message, end='\r', slack=(step % args.checkpoint_interval == 0)) if np.isnan(loss) or loss > 100.: log('Loss exploded to {:.5f} at step {}'.format(loss, step)) raise Exception('Loss exploded') if step % args.summary_interval == 0: log('\nWriting summary at step: {}'.format(step)) summary_writer.add_summary(sess.run(stats), step) if step % args.checkpoint_interval == 0 or step == args.tacotron_train_steps or step == 300: #Save model and current global step saver.save(sess, checkpoint_path, global_step=global_step) log('\nSaving alignment, Mel-Spectrograms and griffin-lim inverted waveform..') input_seq, mel_prediction = sess.run([ model.tower_inputs[0][0], model.tower_mel_outputs[0][0], ]) #save predicted mel spectrogram to disk (debug) mel_filename = 'mel-prediction-step-{}.npy'.format(step) np.save(os.path.join(mel_dir, mel_filename), mel_prediction.T, allow_pickle=False) #save griffin lim inverted wav for debug (mel -> wav) wav = sess.run(GLGPU_mel_outputs, feed_dict={GLGPU_mel_inputs: mel_prediction}) wav = audio.inv_preemphasis(wav, hparams.preemphasis, hparams.preemphasize) audio.save_wav(wav, os.path.join(wav_dir, 'step-{}-wave-from-mel.wav'.format(step)), sr=hparams.sample_rate) log('Input at step {}: {}'.format(step, sequence_to_text(input_seq))) log('Tacotron training complete after {} global steps!'.format(args.tacotron_train_steps), slack=True) return save_dir except Exception as e: log('Exiting due to exception: {}'.format(e), slack=True) traceback.print_exc() coord.request_stop(e)
def train(log_dir, args, hparams): save_dir = os.path.join(log_dir, 'taco_pretrained') plot_dir = os.path.join(log_dir, 'plots') wav_dir = os.path.join(log_dir, 'wavs') mel_dir = os.path.join(log_dir, 'mel-spectrograms') eval_dir = os.path.join(log_dir, 'eval-dir') eval_plot_dir = os.path.join(eval_dir, 'plots') eval_wav_dir = os.path.join(eval_dir, 'wavs') tensorboard_dir = os.path.join(log_dir, 'tacotron_events') meta_folder = os.path.join(log_dir, 'metas') os.makedirs(save_dir, exist_ok=True) os.makedirs(plot_dir, exist_ok=True) os.makedirs(wav_dir, exist_ok=True) os.makedirs(mel_dir, exist_ok=True) os.makedirs(eval_dir, exist_ok=True) os.makedirs(eval_plot_dir, exist_ok=True) os.makedirs(eval_wav_dir, exist_ok=True) os.makedirs(tensorboard_dir, exist_ok=True) os.makedirs(meta_folder, exist_ok=True) checkpoint_path = os.path.join(save_dir, 'tacotron_model.ckpt') input_path = os.path.join(args.base_dir, args.tacotron_input) if hparams.predict_linear: linear_dir = os.path.join(log_dir, 'linear-spectrograms') os.makedirs(linear_dir, exist_ok=True) log('Checkpoint path: {}'.format(checkpoint_path)) log('Loading training data from: {}'.format(input_path)) log('Using model: {}'.format(args.model)) log(hparams_debug_string()) #Start by setting a seed for repeatability tf.set_random_seed(hparams.tacotron_random_seed) #Set up data feeder coord = tf.train.Coordinator() with tf.variable_scope('datafeeder') as scope: feeder = Feeder(coord, input_path, hparams) #Set up model: global_step = tf.Variable(0, name='global_step', trainable=False) model, stats = model_train_mode(args, feeder, hparams, global_step) eval_model = model_test_mode(args, feeder, hparams, global_step) #Embeddings metadata char_embedding_meta = os.path.join(meta_folder, 'CharacterEmbeddings.tsv') if not os.path.isfile(char_embedding_meta): with open(char_embedding_meta, 'w', encoding='utf-8') as f: for symbol in symbols: if symbol == ' ': symbol = '\\s' #For visual purposes, swap space with \s f.write('{}\n'.format(symbol)) char_embedding_meta = char_embedding_meta.replace(log_dir, '..') #Potential Griffin-Lim GPU setup if hparams.GL_on_GPU: GLGPU_mel_inputs = tf.placeholder(tf.float32, (None, hparams.num_mels), name='GLGPU_mel_inputs') GLGPU_lin_inputs = tf.placeholder(tf.float32, (None, hparams.num_freq), name='GLGPU_lin_inputs') GLGPU_mel_outputs = audio.inv_mel_spectrogram_tensorflow( GLGPU_mel_inputs, hparams) GLGPU_lin_outputs = audio.inv_linear_spectrogram_tensorflow( GLGPU_lin_inputs, hparams) #Book keeping step = 0 time_window = ValueWindow(100) loss_window = ValueWindow(100) saver = tf.train.Saver(max_to_keep=20) log('Tacotron training set to a maximum of {} steps'.format( args.tacotron_train_steps)) #Memory allocation on the GPU as needed config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True #Train with tf.Session(config=config) as sess: try: summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph) sess.run(tf.global_variables_initializer()) #saved model restoring if args.restore: # Restore saved model if the user requested it, default = True try: checkpoint_state = tf.train.get_checkpoint_state(save_dir) if (checkpoint_state and checkpoint_state.model_checkpoint_path): log('Loading checkpoint {}'.format( checkpoint_state.model_checkpoint_path), slack=True) ckpt = tf.train.load_checkpoint( checkpoint_state.model_checkpoint_path) variables = list( ckpt.get_variable_to_shape_map().keys()) #print('=====================PRINTING VARS===============================') #print(variables) #drop_source_layers = ['Tacotron_model/inference/inputs_embedding','Tacotron_model/Tacotron_model/inference/inputs_embedding/Adam_1','Tacotron_model/Tacotron_model/inference/inputs_embedding/Adam'] #for v in tf.global_variables(): # if not any(layer in v.op.name for layer in drop_source_layers): # print('Loading', v.op.name) # v.load(ckpt.get_tensor(v.op.name), session=sess) # Initialize all variables needed for DS, but not loaded from ckpt #init_op = tf.variables_initializer([v for v in tf.global_variables() if any(layer in v.op.name for layer in drop_source_layers)]) #sess.run(init_op) saver.restore(sess, checkpoint_state.model_checkpoint_path) else: log('No model to load at {}'.format(save_dir), slack=True) saver.save(sess, checkpoint_path, global_step=global_step) except tf.errors.OutOfRangeError as e: log('Cannot restore checkpoint: {}'.format(e), slack=True) else: log('Starting new training!', slack=True) saver.save(sess, checkpoint_path, global_step=global_step) #initializing feeder feeder.start_threads(sess) #Training loop while not coord.should_stop() and step < args.tacotron_train_steps: start_time = time.time() step, loss, opt = sess.run( [global_step, model.loss, model.optimize]) time_window.append(time.time() - start_time) loss_window.append(loss) message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format( step, time_window.average, loss, loss_window.average) log(message, end='\r', slack=(step % args.checkpoint_interval == 0)) if np.isnan(loss): log('Loss exploded to {:.5f} at step {}'.format( loss, step)) raise Exception('Loss exploded') if step % args.summary_interval == 0: log('\nWriting summary at step {}'.format(step)) summary_writer.add_summary(sess.run(stats), step) if step % args.eval_interval == 0: #Run eval and save eval stats log('\nRunning evaluation at step {}'.format(step)) eval_losses = [] before_losses = [] after_losses = [] stop_token_losses = [] linear_losses = [] linear_loss = None if hparams.predict_linear: for i in tqdm(range(feeder.test_steps)): eloss, before_loss, after_loss, stop_token_loss, linear_loss, mel_p, mel_t, t_len, align, lin_p, lin_t = sess.run( [ eval_model.tower_loss[0], eval_model.tower_before_loss[0], eval_model.tower_after_loss[0], eval_model.tower_stop_token_loss[0], eval_model.tower_linear_loss[0], eval_model.tower_mel_outputs[0][0], eval_model.tower_mel_targets[0][0], eval_model.tower_targets_lengths[0][0], eval_model.tower_alignments[0][0], eval_model.tower_linear_outputs[0][0], eval_model.tower_linear_targets[0][0], ]) eval_losses.append(eloss) before_losses.append(before_loss) after_losses.append(after_loss) stop_token_losses.append(stop_token_loss) linear_losses.append(linear_loss) linear_loss = sum(linear_losses) / len(linear_losses) if hparams.GL_on_GPU: wav = sess.run(GLGPU_lin_outputs, feed_dict={GLGPU_lin_inputs: lin_p}) wav = audio.inv_preemphasis( wav, hparams.preemphasis, hparams.preemphasize) else: wav = audio.inv_linear_spectrogram( lin_p.T, hparams) audio.save_wav( wav, os.path.join( eval_wav_dir, 'step-{}-eval-wave-from-linear.wav'.format( step)), sr=hparams.sample_rate) else: for i in tqdm(range(feeder.test_steps)): eloss, before_loss, after_loss, stop_token_loss, mel_p, mel_t, t_len, align = sess.run( [ eval_model.tower_loss[0], eval_model.tower_before_loss[0], eval_model.tower_after_loss[0], eval_model.tower_stop_token_loss[0], eval_model.tower_mel_outputs[0][0], eval_model.tower_mel_targets[0][0], eval_model.tower_targets_lengths[0][0], eval_model.tower_alignments[0][0] ]) eval_losses.append(eloss) before_losses.append(before_loss) after_losses.append(after_loss) stop_token_losses.append(stop_token_loss) eval_loss = sum(eval_losses) / len(eval_losses) before_loss = sum(before_losses) / len(before_losses) after_loss = sum(after_losses) / len(after_losses) stop_token_loss = sum(stop_token_losses) / len( stop_token_losses) log('Saving eval log to {}..'.format(eval_dir)) #Save some log to monitor model improvement on same unseen sequence if hparams.GL_on_GPU: wav = sess.run(GLGPU_mel_outputs, feed_dict={GLGPU_mel_inputs: mel_p}) wav = audio.inv_preemphasis(wav, hparams.preemphasis, hparams.preemphasize) else: wav = audio.inv_mel_spectrogram(mel_p.T, hparams) audio.save_wav( wav, os.path.join( eval_wav_dir, 'step-{}-eval-wave-from-mel.wav'.format(step)), sr=hparams.sample_rate) plot.plot_alignment( align, os.path.join(eval_plot_dir, 'step-{}-eval-align.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, eval_loss), max_len=t_len // hparams.outputs_per_step) plot.plot_spectrogram( mel_p, os.path.join( eval_plot_dir, 'step-{}-eval-mel-spectrogram.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, eval_loss), target_spectrogram=mel_t, max_len=t_len) if hparams.predict_linear: plot.plot_spectrogram( lin_p, os.path.join( eval_plot_dir, 'step-{}-eval-linear-spectrogram.png'.format( step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, eval_loss), target_spectrogram=lin_t, max_len=t_len, auto_aspect=True) log('Eval loss for global step {}: {:.3f}'.format( step, eval_loss)) log('Writing eval summary!') add_eval_stats(summary_writer, step, linear_loss, before_loss, after_loss, stop_token_loss, eval_loss) if step % args.checkpoint_interval == 0 or step == args.tacotron_train_steps or step == 300: #Save model and current global step saver.save(sess, checkpoint_path, global_step=global_step) log('\nSaving alignment, Mel-Spectrograms and griffin-lim inverted waveform..' ) if hparams.predict_linear: input_seq, mel_prediction, linear_prediction, alignment, target, target_length, linear_target = sess.run( [ model.tower_inputs[0][0], model.tower_mel_outputs[0][0], model.tower_linear_outputs[0][0], model.tower_alignments[0][0], model.tower_mel_targets[0][0], model.tower_targets_lengths[0][0], model.tower_linear_targets[0][0], ]) #save predicted linear spectrogram to disk (debug) linear_filename = 'linear-prediction-step-{}.npy'.format( step) np.save(os.path.join(linear_dir, linear_filename), linear_prediction.T, allow_pickle=False) #save griffin lim inverted wav for debug (linear -> wav) if hparams.GL_on_GPU: wav = sess.run(GLGPU_lin_outputs, feed_dict={ GLGPU_lin_inputs: linear_prediction }) wav = audio.inv_preemphasis( wav, hparams.preemphasis, hparams.preemphasize) else: wav = audio.inv_linear_spectrogram( linear_prediction.T, hparams) audio.save_wav( wav, os.path.join( wav_dir, 'step-{}-wave-from-linear.wav'.format(step)), sr=hparams.sample_rate) #Save real and predicted linear-spectrogram plot to disk (control purposes) plot.plot_spectrogram( linear_prediction, os.path.join( plot_dir, 'step-{}-linear-spectrogram.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, loss), target_spectrogram=linear_target, max_len=target_length, auto_aspect=True) else: input_seq, mel_prediction, alignment, target, target_length = sess.run( [ model.tower_inputs[0][0], model.tower_mel_outputs[0][0], model.tower_alignments[0][0], model.tower_mel_targets[0][0], model.tower_targets_lengths[0][0], ]) #save predicted mel spectrogram to disk (debug) mel_filename = 'mel-prediction-step-{}.npy'.format(step) np.save(os.path.join(mel_dir, mel_filename), mel_prediction.T, allow_pickle=False) #save griffin lim inverted wav for debug (mel -> wav) if hparams.GL_on_GPU: wav = sess.run( GLGPU_mel_outputs, feed_dict={GLGPU_mel_inputs: mel_prediction}) wav = audio.inv_preemphasis(wav, hparams.preemphasis, hparams.preemphasize) else: wav = audio.inv_mel_spectrogram( mel_prediction.T, hparams) audio.save_wav( wav, os.path.join(wav_dir, 'step-{}-wave-from-mel.wav'.format(step)), sr=hparams.sample_rate) #save alignment plot to disk (control purposes) plot.plot_alignment( alignment, os.path.join(plot_dir, 'step-{}-align.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, loss), max_len=target_length // hparams.outputs_per_step) #save real and predicted mel-spectrogram plot to disk (control purposes) plot.plot_spectrogram( mel_prediction, os.path.join( plot_dir, 'step-{}-mel-spectrogram.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, loss), target_spectrogram=target, max_len=target_length) log('Input at step {}: {}'.format( step, sequence_to_text(input_seq))) if step % args.embedding_interval == 0 or step == args.tacotron_train_steps or step == 1: #Get current checkpoint state checkpoint_state = tf.train.get_checkpoint_state(save_dir) #Update Projector log('\nSaving Model Character Embeddings visualization..') add_embedding_stats(summary_writer, [model.embedding_table.name], [char_embedding_meta], checkpoint_state.model_checkpoint_path) log('Tacotron Character embeddings have been updated on tensorboard!' ) log('Tacotron training complete after {} global steps!'.format( args.tacotron_train_steps), slack=True) return save_dir except Exception as e: log('Exiting due to exception: {}'.format(e), slack=True) traceback.print_exc() coord.request_stop(e)
def train(log_dir, args): save_dir = os.path.join(log_dir, 'pretrained/') checkpoint_path = os.path.join(save_dir, 'model.ckpt') input_path = os.path.join(args.base_dir, args.input) plot_dir = os.path.join(log_dir, 'plots') wav_dir = os.path.join(log_dir, 'wavs') mel_dir = os.path.join(log_dir, 'mel-spectrograms') os.makedirs(plot_dir, exist_ok=True) os.makedirs(wav_dir, exist_ok=True) os.makedirs(mel_dir, exist_ok=True) if hparams.predict_linear: linear_dir = os.path.join(log_dir, 'linear-spectrograms') os.makedirs(linear_dir, exist_ok=True) log('Checkpoint path: {}'.format(checkpoint_path)) log('Loading training data from: {}'.format(input_path)) log('Using model: {}'.format(args.model)) log(hparams_debug_string()) #Set up data feeder coord = tf.train.Coordinator() with tf.variable_scope('datafeeder') as scope: feeder = Feeder(coord, input_path, hparams) #Set up model: step_count = 0 try: #simple text file to keep count of global step with open(os.path.join(log_dir, 'step_counter.txt'), 'r') as file: step_count = int(file.read()) except: print( 'no step_counter file found, assuming there is no saved checkpoint' ) global_step = tf.Variable(step_count, name='global_step', trainable=False) with tf.variable_scope('model') as scope: model = create_model(args.model, hparams) if hparams.predict_linear: model.initialize(feeder.inputs, feeder.input_lengths, feeder.mel_targets, feeder.token_targets, feeder.linear_targets) else: model.initialize(feeder.inputs, feeder.input_lengths, feeder.mel_targets, feeder.token_targets) model.add_loss() model.add_optimizer(global_step) stats = add_stats(model) #Book keeping step = 0 save_step = 0 time_window = ValueWindow(100) loss_window = ValueWindow(100) saver = tf.train.Saver(max_to_keep=5) #Memory allocation on the GPU as needed config = tf.ConfigProto() config.gpu_options.allow_growth = True #Train with tf.Session(config=config) as sess: try: summary_writer = tf.summary.FileWriter(log_dir, sess.graph) sess.run(tf.global_variables_initializer()) #saved model restoring if args.restore: #Restore saved model if the user requested it, Default = True. try: checkpoint_state = tf.train.get_checkpoint_state(save_dir) except tf.errors.OutOfRangeError as e: log('Cannot restore checkpoint: {}'.format(e)) if (checkpoint_state and checkpoint_state.model_checkpoint_path): log('Loading checkpoint {}'.format( checkpoint_state.model_checkpoint_path)) saver.restore(sess, checkpoint_state.model_checkpoint_path) else: if not args.restore: log('Starting new training!') else: log('No model to load at {}'.format(save_dir)) #initiating feeder feeder.start_in_session(sess) #Training loop while not coord.should_stop(): start_time = time.time() step, loss, opt = sess.run( [global_step, model.loss, model.optimize]) time_window.append(time.time() - start_time) loss_window.append(loss) message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format( step, time_window.average, loss, loss_window.average) log(message, end='\r') if loss > 100 or np.isnan(loss): log('Loss exploded to {:.5f} at step {}'.format( loss, step)) raise Exception('Loss exploded') if step % args.summary_interval == 0: log('\nWriting summary at step: {}'.format(step)) summary_writer.add_summary(sess.run(stats), step) if step % args.checkpoint_interval == 0: with open(os.path.join(log_dir, 'step_counter.txt'), 'w') as file: file.write(str(step)) log('Saving checkpoint to: {}-{}'.format( checkpoint_path, step)) saver.save(sess, checkpoint_path, global_step=step) save_step = step log('Saving alignment, Mel-Spectrograms and griffin-lim inverted waveform..' ) if hparams.predict_linear: input_seq, mel_prediction, linear_prediction, alignment, target = sess.run( [ model.inputs[0], model.mel_outputs[0], model.linear_outputs[0], model.alignments[0], model.mel_targets[0], ]) #save predicted linear spectrogram to disk (debug) linear_filename = 'linear-prediction-step-{}.npy'.format( step) np.save(os.path.join(linear_dir, linear_filename), linear_prediction.T, allow_pickle=False) #save griffin lim inverted wav for debug (linear -> wav) wav = audio.inv_linear_spectrogram(linear_prediction.T) audio.save_wav( wav, os.path.join( wav_dir, 'step-{}-waveform-linear.wav'.format(step))) else: input_seq, mel_prediction, alignment, target = sess.run( [ model.inputs[0], model.mel_outputs[0], model.alignments[0], model.mel_targets[0], ]) #save predicted mel spectrogram to disk (debug) mel_filename = 'mel-prediction-step-{}.npy'.format(step) np.save(os.path.join(mel_dir, mel_filename), mel_prediction.T, allow_pickle=False) #save griffin lim inverted wav for debug (mel -> wav) wav = audio.inv_mel_spectrogram(mel_prediction.T) audio.save_wav( wav, os.path.join(wav_dir, 'step-{}-waveform-mel.wav'.format(step))) #save alignment plot to disk (control purposes) plot.plot_alignment( alignment, os.path.join(plot_dir, 'step-{}-align.png'.format(step)), info='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, loss)) #save real mel-spectrogram plot to disk (control purposes) plot.plot_spectrogram( target, os.path.join( plot_dir, 'step-{}-real-mel-spectrogram.png'.format(step)), info='{}, {}, step={}, Real'.format( args.model, time_string(), step, loss)) #save predicted mel-spectrogram plot to disk (control purposes) plot.plot_spectrogram( mel_prediction, os.path.join( plot_dir, 'step-{}-pred-mel-spectrogram.png'.format(step)), info='{}, {}, step={}, loss={:.5}'.format( args.model, time_string(), step, loss)) log('Input at step {}: {}'.format( step, sequence_to_text(input_seq))) except Exception as e: log('Exiting due to exception: {}'.format(e), slack=True) traceback.print_exc() coord.request_stop(e)
def train(log_dir, args, hparams): save_dir = os.path.join(log_dir, 'taco_pretrained') plot_dir = os.path.join(log_dir, 'plots') wav_dir = os.path.join(log_dir, 'wavs') mel_dir = os.path.join(log_dir, 'mel-spectrograms') eval_dir = os.path.join(log_dir, 'eval-dir') eval_plot_dir = os.path.join(eval_dir, 'plots') eval_wav_dir = os.path.join(eval_dir, 'wavs') tensorboard_dir = os.path.join(log_dir, 'tacotron_events') meta_folder = os.path.join(log_dir, 'metas') os.makedirs(save_dir, exist_ok=True) os.makedirs(plot_dir, exist_ok=True) os.makedirs(wav_dir, exist_ok=True) os.makedirs(mel_dir, exist_ok=True) os.makedirs(eval_dir, exist_ok=True) os.makedirs(eval_plot_dir, exist_ok=True) os.makedirs(eval_wav_dir, exist_ok=True) os.makedirs(tensorboard_dir, exist_ok=True) os.makedirs(meta_folder, exist_ok=True) checkpoint_path = os.path.join(save_dir, 'tacotron_model.ckpt') input_path = os.path.join(args.base_dir, args.tacotron_input) if hparams.predict_linear: linear_dir = os.path.join(log_dir, 'linear-spectrograms') os.makedirs(linear_dir, exist_ok=True) log('Checkpoint path: {}'.format(checkpoint_path)) log('Loading training data from: {}'.format(input_path)) log('Using model: {}'.format(args.model)) log(hparams_debug_string()) # Start by setting a seed for repeatability tf.set_random_seed(hparams.tacotron_random_seed) # Set up data feeder coord = tf.train.Coordinator() with tf.variable_scope('datafeeder') as scope: feeder = Feeder(coord, input_path, hparams) # with open("split_train.txt", "w") as file: # for line in feeder._train_meta: # for k in range(len(line)-1): # file.write(line[k]+"|") # file.write(line[-1]+"\n") # with open("split_validation.txt", "w") as file: # for line in feeder._test_meta: # for k in range(len(line)-1): # file.write(line[k]+"|") # file.write(line[-1]+"\n") # print("Feeder init done !") # assert False # Set up model: global_step = tf.Variable(0, name='global_step', trainable=False) model, stats = model_train_mode(args, feeder, hparams, global_step) eval_model = model_test_mode(args, feeder, hparams, global_step) # TODO Visualize embeddings # Embeddings inputs metadata char_embedding_meta = os.path.join(meta_folder, 'CharacterEmbeddings.tsv') if not os.path.isfile(char_embedding_meta): with open(char_embedding_meta, 'w', encoding='utf-8') as f: for symbol in symbols: if symbol == ' ': symbol = '\\s' # For visual purposes, swap space with \s f.write('{}\n'.format(symbol)) char_embedding_meta = char_embedding_meta.replace(log_dir, '..') # # Embeddings speaker metadata # speaker_embedding_meta = os.path.join(meta_folder, 'SpeakerEmbeddings.tsv') # if not os.path.isfile(speaker_embedding_meta): # with open(speaker_embedding_meta, 'w', encoding='utf-8') as f: # f.write("Filename\tSpeaker\n") # for description in feeder._metadata: # f.write('{}\t{}\n'.format(description[1], description[-1])) # speaker_embedding_meta = speaker_embedding_meta.replace(log_dir, '..') # Book keeping step = 0 time_window = ValueWindow(100) loss_window = ValueWindow(100) saver = tf.train.Saver(max_to_keep=5) log('Tacotron training set to a maximum of {} steps'.format( args.tacotron_train_steps)) # Memory allocation on the GPU as needed config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True # Train with tf.Session(config=config) as sess: try: summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph) sess.run(tf.global_variables_initializer()) # saved model restoring if args.restore: # Restore saved model if the user requested it, default = True try: checkpoint_state = tf.train.get_checkpoint_state(save_dir) if (checkpoint_state and checkpoint_state.model_checkpoint_path): log('Loading checkpoint {}'.format( checkpoint_state.model_checkpoint_path), slack=True) saver.restore(sess, checkpoint_state.model_checkpoint_path) else: log('No model to load at {}'.format(save_dir), slack=True) saver.save(sess, checkpoint_path, global_step=global_step) except tf.errors.OutOfRangeError as e: log('Cannot restore checkpoint: {}'.format(e), slack=True) else: log('Starting new training!', slack=True) saver.save(sess, checkpoint_path, global_step=global_step) # initializing feeder feeder.start_threads(sess) # Training loop while not coord.should_stop() and step < args.tacotron_train_steps: start_time = time.time() step, loss, opt = sess.run( [global_step, model.loss, model.optimize]) time_window.append(time.time() - start_time) loss_window.append(loss) message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format( step, time_window.average, loss, loss_window.average) log(message, end='\r', slack=(step % args.checkpoint_interval == 0)) if loss > 100 or np.isnan(loss): log('Loss exploded to {:.5f} at step {}'.format( loss, step)) raise Exception('Loss exploded') if step % args.summary_interval == 0: log('\nWriting summary at step {}'.format(step)) summary_writer.add_summary(sess.run(stats), step) if step % args.eval_interval == 0: # Run eval and save eval stats log('\nRunning evaluation at step {}'.format(step)) eval_losses = [] before_losses = [] after_losses = [] stop_token_losses = [] linear_losses = [] linear_loss = None speaker_losses = [] speaker_loss = None eval_run = [ eval_model.tower_loss[0], eval_model.tower_before_loss[0], eval_model.tower_after_loss[0], eval_model.tower_stop_token_loss[0], eval_model.tower_mel_outputs[0][0], eval_model.tower_mel_targets[0][0], eval_model.tower_targets_lengths[0][0], eval_model.tower_alignments[0][0] ] if hparams.predict_linear: eval_run.append(eval_model.tower_linear_loss[0]) eval_run.append(eval_model.tower_linear_outputs[0][0]) eval_run.append(eval_model.tower_linear_targets[0][0]) if hparams.tacotron_multi_speaker: eval_run.append(eval_model.tower_speaker_loss[0]) for i in tqdm(range(feeder.test_steps)): blob = sess.run(eval_run) eloss = blob[0] before_loss = blob[1] after_loss = blob[2] stop_token_loss = blob[3] mel_p = blob[4] mel_t = blob[5] t_len = blob[6] align = blob[7] if hparams.predict_linear: linear_loss = blob[8] lin_p = blob[9] lin_t = blob[10] if hparams.tacotron_multi_speaker: speaker_p = blob[11] eval_losses.append(eloss) before_losses.append(before_loss) after_losses.append(after_loss) stop_token_losses.append(stop_token_loss) if hparams.predict_linear: linear_losses.append(linear_loss) if hparams.tacotron_multi_speaker: speaker_losses.append(speaker_p) if hparams.predict_linear: linear_loss = sum(linear_losses) / len(linear_losses) wav = audio.inv_linear_spectrogram(lin_p.T, hparams) audio.save_wav( wav, os.path.join( eval_wav_dir, 'step-{}-eval-wave-from-linear.wav'.format( step)), sr=hparams.sample_rate) if hparams.tacotron_multi_speaker: speaker_loss = sum(speaker_losses) / len( speaker_losses) # if hparams.predict_linear: # for i in tqdm(range(feeder.test_steps)): # eloss, before_loss, after_loss, stop_token_loss, linear_loss, mel_p, mel_t, t_len, align, lin_p, lin_t = sess.run( # [ # eval_model.tower_loss[0], eval_model.tower_before_loss[0], # eval_model.tower_after_loss[0], # eval_model.tower_stop_token_loss[0], eval_model.tower_linear_loss[0], # eval_model.tower_mel_outputs[0][0], # eval_model.tower_mel_targets[0][0], eval_model.tower_targets_lengths[0][0], # eval_model.tower_alignments[0][0], eval_model.tower_linear_outputs[0][0], # eval_model.tower_linear_targets[0][0], # ]) # eval_losses.append(eloss) # before_losses.append(before_loss) # after_losses.append(after_loss) # stop_token_losses.append(stop_token_loss) # linear_losses.append(linear_loss) # # print("len(eval_loss) : {}".format(len(eval_loss))) # # print("len(before_losses) : {}".format(len(before_losses))) # # print("len(after_losses) : {}".format(len(after_losses))) # # print("len(stop_token_losses) : {}".format(len(stop_token_losses))) # # print("len(linear_losses) : {}".format(len(linear_losses))) # # print("division par : {}, dans hparams.predict_linear".format(len(linear_losses))) # linear_loss = sum(linear_losses) / len(linear_losses) # # wav = audio.inv_linear_spectrogram(lin_p.T, hparams) # audio.save_wav(wav, # os.path.join(eval_wav_dir, 'step-{}-eval-wave-from-linear.wav'.format(step)), # sr=hparams.sample_rate) # # else: # for i in tqdm(range(feeder.test_steps)): # eloss, before_loss, after_loss, stop_token_loss, mel_p, mel_t, t_len, align = sess.run([ # eval_model.tower_loss[0], eval_model.tower_before_loss[0], # eval_model.tower_after_loss[0], # eval_model.tower_stop_token_loss[0], eval_model.tower_mel_outputs[0][0], # eval_model.tower_mel_targets[0][0], # eval_model.tower_targets_lengths[0][0], eval_model.tower_alignments[0][0] # ]) # eval_losses.append(eloss) # before_losses.append(before_loss) # after_losses.append(after_loss) # stop_token_losses.append(stop_token_loss) # print("len(eval_loss) : {}".format(len(eval_loss))) # print("len(before_losses) : {}".format(len(before_losses))) # print("len(after_losses) : {}".format(len(after_losses))) # print("len(stop_token_losses) : {}".format(len(stop_token_losses))) eval_loss = sum(eval_losses) / len(eval_losses) before_loss = sum(before_losses) / len(before_losses) after_loss = sum(after_losses) / len(after_losses) stop_token_loss = sum(stop_token_losses) / len( stop_token_losses) log('Saving eval log to {}..'.format(eval_dir)) # Save some log to monitor model improvement on same unseen sequence wav = audio.inv_mel_spectrogram(mel_p.T, hparams) audio.save_wav( wav, os.path.join( eval_wav_dir, 'step-{}-eval-wave-from-mel.wav'.format(step)), sr=hparams.sample_rate) plot.plot_alignment( align, os.path.join(eval_plot_dir, 'step-{}-eval-align.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, eval_loss), max_len=t_len // hparams.outputs_per_step) plot.plot_spectrogram( mel_p, os.path.join( eval_plot_dir, 'step-{}-eval-mel-spectrogram.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, eval_loss), target_spectrogram=mel_t, max_len=t_len) if hparams.predict_linear: plot.plot_spectrogram( lin_p, os.path.join( eval_plot_dir, 'step-{}-eval-linear-spectrogram.png'.format( step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, eval_loss), target_spectrogram=lin_t, max_len=t_len, auto_aspect=True) log('Eval loss for global step {}: {:.3f}'.format( step, eval_loss)) log('Writing eval summary!') add_eval_stats(summary_writer, step, linear_loss, before_loss, after_loss, stop_token_loss, eval_loss, speaker_loss) if step % args.checkpoint_interval == 0 or step == args.tacotron_train_steps or step == 300: # Save model and current global step saver.save(sess, checkpoint_path, global_step=global_step) log('\nSaving alignment, Mel-Spectrograms and griffin-lim inverted waveform..' ) if hparams.predict_linear: input_seq, mel_prediction, linear_prediction, alignment, target, target_length, linear_target = sess.run( [ model.tower_inputs[0][0], model.tower_mel_outputs[0][0], model.tower_linear_outputs[0][0], model.tower_alignments[0][0], model.tower_mel_targets[0][0], model.tower_targets_lengths[0][0], model.tower_linear_targets[0][0], ]) # save predicted linear spectrogram to disk (debug) linear_filename = 'linear-prediction-step-{}.npy'.format( step) np.save(os.path.join(linear_dir, linear_filename), linear_prediction.T, allow_pickle=False) # save griffin lim inverted wav for debug (linear -> wav) wav = audio.inv_linear_spectrogram( linear_prediction.T, hparams) audio.save_wav( wav, os.path.join( wav_dir, 'step-{}-wave-from-linear.wav'.format(step)), sr=hparams.sample_rate) # Save real and predicted linear-spectrogram plot to disk (control purposes) plot.plot_spectrogram( linear_prediction, os.path.join( plot_dir, 'step-{}-linear-spectrogram.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, loss), target_spectrogram=linear_target, max_len=target_length, auto_aspect=True) else: input_seq, mel_prediction, alignment, target, target_length = sess.run( [ model.tower_inputs[0][0], model.tower_mel_outputs[0][0], model.tower_alignments[0][0], model.tower_mel_targets[0][0], model.tower_targets_lengths[0][0], ]) # save predicted mel spectrogram to disk (debug) mel_filename = 'mel-prediction-step-{}.npy'.format(step) np.save(os.path.join(mel_dir, mel_filename), mel_prediction.T, allow_pickle=False) # save griffin lim inverted wav for debug (mel -> wav) wav = audio.inv_mel_spectrogram(mel_prediction.T, hparams) audio.save_wav( wav, os.path.join(wav_dir, 'step-{}-wave-from-mel.wav'.format(step)), sr=hparams.sample_rate) # save alignment plot to disk (control purposes) plot.plot_alignment( alignment, os.path.join(plot_dir, 'step-{}-align.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, loss), max_len=target_length // hparams.outputs_per_step) # save real and predicted mel-spectrogram plot to disk (control purposes) plot.plot_spectrogram( mel_prediction, os.path.join( plot_dir, 'step-{}-mel-spectrogram.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, loss), target_spectrogram=target, max_len=target_length) # TODO Find a way to revert encoded IPA to original IPA or original text # log('Input at step {}: {}'.format(step, sequence_to_text(input_seq))) if step % args.embedding_interval == 0 or step == args.tacotron_train_steps or step == 1: #Get current checkpoint_backup state # checkpoint_state = tf.train.get_checkpoint_state(save_dir) checkpoint_state = tf.train.get_checkpoint_state(save_dir) # TODO Visualize embeddings #Update Projector log('\nSaving Model Character Embeddings visualization..') # add_embedding_stats(summary_writer, [model.embedding_table.name], [char_embedding_meta], checkpoint_state.model_checkpoint_path) # add_embedding_stats(summary_writer, [model.embedding_speaker.name], [char_embedding_meta], checkpoint_state.model_checkpoint_path) log('Tacotron Character embeddings have been updated on tensorboard!' ) log('Tacotron training complete after {} global steps!'.format( args.tacotron_train_steps), slack=True) return save_dir except Exception as e: log('Exiting due to exception: {}'.format(e), slack=True) traceback.print_exc() coord.request_stop(e)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--base_dir', default='') parser.add_argument( '--hparams', default='', help= 'Hyperparameter overrides as a comma-separated list of name=value pairs' ) parser.add_argument('--model', default='Tacotron-2') parser.add_argument('--tacotron_input', default='training_data/train.txt') parser.add_argument('--nb_speaker', default=96, help='Number of speaker during training.') parser.add_argument('--embedding_dir', default="logs-speaker_embeddings", help='Directory to save the speaker embeddings.') parser.add_argument('--debug', default=False, help='Print debugging information') parser.add_argument('--verbose', default=True, help='Print progress information') args = parser.parse_args() debug_flag = args.debug verbose_flag = args.verbose run_name = args.model log_dir = os.path.join(args.base_dir, 'logs-{}'.format(run_name)) input_path = os.path.join(args.base_dir, args.tacotron_input) hparams = hparamspy.parse(args.hparams) tensorboard_dir = os.path.join(log_dir, 'tacotron_events') save_dir = os.path.join(log_dir, 'taco_pretrained') # Set up data feeder coord = tf.train.Coordinator() with tf.variable_scope('datafeeder') as scope: feeder = Feeder(coord, input_path, hparams, split=False) # Create model global_step = tf.Variable(0, name='global_step', trainable=False) with tf.variable_scope('Tacotron_model', reuse=tf.AUTO_REUSE) as scope: model = create_model("Tacotron", hparams) initialize_args = { "inputs": feeder.inputs, "input_lengths": feeder.input_lengths, "mel_targets": feeder.mel_targets, "stop_token_targets": feeder.token_targets, "targets_lengths": feeder.targets_lengths, "global_step": global_step, "is_training": False, "split_infos": feeder.split_infos } if hparams.predict_linear: initialize_args["linear_targets"] = feeder.linear_targets if hparams.tacotron_reference_waveform: initialize_args["mel_references"] = feeder.mel_references initialize_args["nb_sample"] = len(feeder._metadata) if hparams.tacotron_multi_speaker: initialize_args["speaker_id_target"] = feeder.speaker_id_target initialize_args["nb_speaker"] = args.nb_speaker model.initialize(**initialize_args) saver = tf.train.Saver(max_to_keep=5) config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True with tf.Session(config=config) as sess: summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph) sess.run(tf.global_variables_initializer()) # Restore saved model checkpoint_state = tf.train.get_checkpoint_state(save_dir) saver.restore(sess, checkpoint_state.model_checkpoint_path) # Embeddings speaker metadata os.makedirs(args.embedding_dir, exist_ok=False) speaker_embedding_meta = os.path.join(args.embedding_dir, 'SpeakerEmbeddings.tsv') with open(speaker_embedding_meta, 'w', encoding='utf-8') as f: f.write("Filename\tSpeaker\n") # Header n = feeder._hparams.tacotron_batch_size r = feeder._hparams.outputs_per_step speaker_embeddings = [] examples = [] if debug_flag: print(len(feeder._train_meta)) print(len(feeder._train_meta[0])) print(n * _batches_per_group) # Extract speaker label and embedding for i in range(n * _batches_per_group): # if i<10: if i < len(feeder._train_meta): example = feeder._get_next_example() metadata = feeder._train_meta[i] f.write('{}\t{}\n'.format(metadata[1], metadata[-1])) examples.append(example) batch = [example] feed_dict = dict( zip(feeder._placeholders, feeder._prepare_batch(batch, r))) sess.run(feeder._enqueue_op, feed_dict=feed_dict) speaker_embedding = sess.run([model.embedding_speaker]) speaker_embeddings.append(speaker_embedding) if verbose_flag: print("\r\r\r\r\r\r\r\r{}/{}".format( i, len(feeder._train_meta)), end=" ") if verbose_flag: print(" ") # Reshape the embeddings data speaker_embeddings = np.array(speaker_embeddings) if debug_flag: print(speaker_embeddings.shape) speaker_embeddings = speaker_embeddings.reshape((-1, 64)) if debug_flag: print(speaker_embeddings.shape) # Save embeddings data for Tensorboard spk_emb = tf.Variable(speaker_embeddings, name='speaker_embeddings') with tf.Session(config=config) as sess: saver = tf.train.Saver([spk_emb]) sess.run(spk_emb.initializer) saver.save( sess, os.path.join(args.embedding_dir, 'speaker_embeddings.ckpt')) config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig() # One can add multiple embeddings. embedding = config.embeddings.add() embedding.tensor_name = spk_emb.name # Link this tensor to its metadata file (e.g. labels). embedding.metadata_path = 'SpeakerEmbeddings.tsv' # Saves a config file that TensorBoard will read during startup. tf.contrib.tensorboard.plugins.projector.visualize_embeddings( tf.summary.FileWriter("logs-speaker_embeddings"), config)
def train(log_dir, args, hparams): save_dir = os.path.join(log_dir, 'taco_pretrained') plot_dir = os.path.join(log_dir, 'plots') wav_dir = os.path.join(log_dir, 'wavs') feat_dir = os.path.join(log_dir, 'features') eval_dir = os.path.join(log_dir, 'eval-dir') eval_plot_dir = os.path.join(eval_dir, 'plots') eval_wav_dir = os.path.join(eval_dir, 'wavs') tensorboard_dir = os.path.join(log_dir, 'tacotron_events') os.makedirs(save_dir, exist_ok=True) os.makedirs(plot_dir, exist_ok=True) os.makedirs(wav_dir, exist_ok=True) os.makedirs(feat_dir, exist_ok=True) os.makedirs(eval_dir, exist_ok=True) os.makedirs(eval_plot_dir, exist_ok=True) os.makedirs(eval_wav_dir, exist_ok=True) os.makedirs(tensorboard_dir, exist_ok=True) checkpoint_path = os.path.join(save_dir, 'tacotron_model.ckpt') input_path = os.path.join(args.base_dir, args.tacotron_input) log('Checkpoint path: {}'.format(checkpoint_path)) log('Loading training data from: {}'.format(input_path)) log('Using model: {}'.format(args.model)) log(hparams_debug_string()) #Start by setting a seed for repeatability tf.set_random_seed(hparams.tacotron_random_seed) #Set up data feeder coord = tf.train.Coordinator() with tf.variable_scope('datafeeder') as scope: feeder = Feeder(coord, input_path, hparams) #Set up model: global_step = tf.Variable(0, name='global_step', trainable=False) model, stats = model_train_mode(args, feeder, hparams, global_step) eval_model = model_test_mode(args, feeder, hparams, global_step) #Book keeping step = 0 time_window = ValueWindow(100) loss_window = ValueWindow(100) saver = tf.train.Saver(max_to_keep=1) log('Tacotron training set to a maximum of {} steps'.format( args.tacotron_train_steps)) #Memory allocation on the GPU as needed config = tf.ConfigProto() config.gpu_options.allow_growth = True #Train with tf.Session(config=config) as sess: try: summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph) sess.run(tf.global_variables_initializer()) #saved model restoring if args.restore: # Restore saved model if the user requested it, default = True try: checkpoint_state = tf.train.get_checkpoint_state(save_dir) if (checkpoint_state and checkpoint_state.model_checkpoint_path): log('Loading checkpoint {}'.format( checkpoint_state.model_checkpoint_path), slack=True) saver.restore(sess, checkpoint_state.model_checkpoint_path) else: log('No model to load at {}'.format(save_dir), slack=True) except tf.errors.OutOfRangeError as e: log('Cannot restore checkpoint: {}'.format(e), slack=True) else: log('Starting new training!', slack=True) #initializing feeder feeder.start_threads(sess) #Training loop while not coord.should_stop() and step < args.tacotron_train_steps: start_time = time.time() step, loss, opt = sess.run( [global_step, model.loss, model.optimize]) time_window.append(time.time() - start_time) loss_window.append(loss) message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format( step, time_window.average, loss, loss_window.average) log(message, end='\r', slack=(step % args.checkpoint_interval == 0)) if loss > 100 or np.isnan(loss): log('Loss exploded to {:.5f} at step {}'.format( loss, step)) raise Exception('Loss exploded') if step % args.summary_interval == 0: log('\nWriting summary at step {}'.format(step)) summary_writer.add_summary(sess.run(stats), step) if step % args.eval_interval == 0: #Run eval and save eval stats log('\nRunning evaluation at step {}'.format(step)) eval_losses = [] before_losses = [] after_losses = [] stop_token_losses = [] attention_losses = [] for i in tqdm(range(feeder.test_steps)): eloss, before_loss, after_loss, stop_token_loss, attention_loss, feature_prediction, target_len, align = sess.run( [ eval_model.loss, eval_model.before_loss, eval_model.after_loss, eval_model.stop_token_loss, eval_model.attention_loss, eval_model.final_outputs[0], eval_model.targets_lengths[0], eval_model.alignments[0] ]) eval_losses.append(eloss) before_losses.append(before_loss) after_losses.append(after_loss) stop_token_losses.append(stop_token_loss) attention_losses.append(attention_loss) eval_loss = sum(eval_losses) / len(eval_losses) before_loss = sum(before_losses) / len(before_losses) after_loss = sum(after_losses) / len(after_losses) stop_token_loss = sum(stop_token_losses) / len( stop_token_losses) attention_loss = sum(attention_losses) / len( attention_losses) log('Saving eval log to {}..'.format(eval_dir)) #Save some log to monitor model improvement on same unseen sequence wav = audio.synthesize(feature_prediction, hparams) audio.save_wav( wav, os.path.join(eval_wav_dir, 'step-{}-eval-waveform.wav'.format(step)), hparams) plot.plot_alignment( align, os.path.join(eval_plot_dir, 'step-{}-eval-align.png'.format(step)), info='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, eval_loss), max_len=target_len // hparams.outputs_per_step) log('Eval loss for global step {}: {:.3f}'.format( step, eval_loss)) log('Writing eval summary!') add_eval_stats(summary_writer, step, before_loss, after_loss, stop_token_loss, attention_loss, eval_loss) if step % args.checkpoint_interval == 0 or step == args.tacotron_train_steps: #Save model and current global step saver.save(sess, checkpoint_path, global_step=global_step) graph_def = tf.graph_util.convert_variables_to_constants( sess, sess.graph_def, ['model/inference/add']) tf.train.write_graph(sess.graph_def, save_dir, 'graph.pb', as_text=False) log('\nSaving alignment and World vocoder synthesized waveform..' ) input_seq, feature_prediction, alignment, target_length = sess.run( [ model.inputs[0], model.final_outputs[0], model.alignments[0], model.targets_lengths[0] ]) #save World vocoder waveform for debug wav = audio.synthesize(feature_prediction, hparams) audio.save_wav( wav, os.path.join(wav_dir, 'step-{}.wav'.format(step)), hparams) #save alignment plot to disk (control purposes) plot.plot_alignment( alignment, os.path.join(plot_dir, 'step-{}-align.png'.format(step)), info='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, loss), max_len=target_length // hparams.outputs_per_step) log('Input at step {}: {}'.format( step, sequence_to_text(input_seq))) log('Tacotron training complete after {} global steps!'.format( args.tacotron_train_steps), slack=True) return save_dir except Exception as e: log('Exiting due to exception: {}'.format(e), slack=True) traceback.print_exc() coord.request_stop(e)
def train(log_dir, args, hparams): save_dir = os.path.join(log_dir, 'taco_pretrained') wav_plot = os.path.join(log_dir, 'wav_plot') tensorboard_dir = os.path.join(log_dir, 'tacotron_events') meta_folder = os.path.join(log_dir, 'metas') os.makedirs(save_dir, exist_ok=True) os.makedirs(tensorboard_dir, exist_ok=True) os.makedirs(meta_folder, exist_ok=True) os.makedirs(wav_plot, exist_ok=True) checkpoint_path = os.path.join(save_dir, 'tacotron_model.ckpt') input_path = os.path.join(args.data_dir, args.tacotron_input) log('Checkpoint path: {}'.format(checkpoint_path)) log('Loading training data from: {}'.format(input_path)) log('Using model: {}'.format(args.model)) log(hparams_debug_string()) #Start by setting a seed for repeatability tf.set_random_seed(hparams.tacotron_random_seed) #Set up data feeder coord = tf.train.Coordinator() with tf.variable_scope('datafeeder') as scope: feeder = Feeder(coord, input_path, hparams) #Set up model: global_step = tf.Variable(0, name='global_step', trainable=False) model, stats = model_train_mode(args, feeder, hparams, global_step) #Embeddings metadata char_embedding_meta = os.path.join(meta_folder, 'CharacterEmbeddings.tsv') if not os.path.isfile(char_embedding_meta): with open(char_embedding_meta, 'w', encoding='utf-8') as f: for symbol in symbols: if symbol == ' ': symbol = '\\s' #For visual purposes, swap space with \s f.write('{}\n'.format(symbol)) char_embedding_meta = char_embedding_meta.replace(log_dir, '..') #Book keeping step = 0 time_window = ValueWindow(100) loss_window = ValueWindow(100) saver = tf.train.Saver(max_to_keep=20) log('Tacotron training set to a maximum of {} steps'.format( args.tacotron_train_steps)) #Memory allocation on the GPU as needed ''' config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True ''' #Train with tf.Session() as sess: #config=config) as sess: try: summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph) sess.run(tf.global_variables_initializer()) #saved model restoring if args.restore: # Restore saved model if the user requested it, default = True try: checkpoint_state = tf.train.get_checkpoint_state(save_dir) if (checkpoint_state and checkpoint_state.model_checkpoint_path): saver.restore(sess, checkpoint_state.model_checkpoint_path) #initial_global_step = global_step.assign(0) #sess.run(initial_global_step) else: log('No model to load at {}'.format(save_dir), slack=True) saver.save(sess, checkpoint_path, global_step=global_step) except tf.errors.OutOfRangeError as e: log('Cannot restore checkpoint: {}'.format(e), slack=True) else: log('Starting new training!', slack=True) saver.save(sess, checkpoint_path, global_step=global_step) #initializing feeder feeder.start_threads(sess) #Training loop while not coord.should_stop() and step < args.tacotron_train_steps: start_time = time.time() step, loss, opt, before_loss, after_loss, token_loss, reg_loss = sess.run( [ global_step, model.loss, model.optimize, model.before_loss, model.after_loss, model.stop_token_loss, model.regularization_loss ]) time_window.append(time.time() - start_time) loss_window.append(loss) message = 'Step{:6d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}, mel_before={:.5f}, mel_after={:.5f}, token_loss={:.5f}, reg_loss={:.5f}]'.format( step, time_window.average, loss, loss_window.average, before_loss, after_loss, token_loss, reg_loss) log(message, end='\r', slack=(step % args.checkpoint_interval == 0)) if np.isnan(loss) or loss > 100.: log('Loss exploded to {:.5f} at step {}'.format( loss, step)) raise Exception('Loss exploded') if step % args.summary_interval == 0: log('\nWriting summary at step {}'.format(step)) summary_writer.add_summary(sess.run(stats), step) if step % args.checkpoint_interval == 0 or step == args.tacotron_train_steps or step == 300: #Save model and current global step saver.save(sess, checkpoint_path, global_step=global_step) log('\nSaving alignment, Mel-Spectrograms and griffin-lim inverted waveform..' ) input_seq, mel_prediction, alignment, target, target_length = sess.run( [ model.inputs[0], model.mel_outputs[0], model.alignments[0], model.mel_targets[0], model.targets_lengths[0], ]) wav = audio.inv_mel_spectrogram(mel_prediction.T, hparams) audio.save_wav( wav, os.path.join(wav_plot, 'step-{}-wave-from-mel.wav'.format(step)), sr=hparams.sample_rate) #save alignment plot to disk (control purposes) plot.plot_alignment( alignment, os.path.join(wav_plot, 'step-{}-align.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, loss), max_len=target_length // hparams.outputs_per_step) #save real and predicted mel-spectrogram plot to disk (control purposes) plot.plot_spectrogram( mel_prediction, os.path.join( wav_plot, 'step-{}-mel-spectrogram.png'.format(step)), title='{}, {}, step={}, loss={:.5f}'.format( args.model, time_string(), step, loss), target_spectrogram=target, max_len=target_length) print(', '.join(map(str, input_seq.tolist()))) log('Input at step {}: {}'.format( step, sequence_to_text(input_seq))) if step % args.embedding_interval == 0 or step == args.tacotron_train_steps or step == 1: #Get current checkpoint state checkpoint_state = tf.train.get_checkpoint_state(save_dir) #Update Projector log('\nSaving Model Character Embeddings visualization..') add_embedding_stats(summary_writer, [model.embedding_table.name], [char_embedding_meta], checkpoint_state.model_checkpoint_path) log('Tacotron Character embeddings have been updated on tensorboard!' ) log('Tacotron training complete after {} global steps!'.format( args.tacotron_train_steps), slack=True) return save_dir except Exception as e: log('Exiting due to exception: {}'.format(e), slack=True) traceback.print_exc() coord.request_stop(e)
if hparams.predict_linear: linear_dir = os.path.join(log_dir, 'linear-spectrograms') os.makedirs(linear_dir, exist_ok=True) log('Checkpoint path: {}'.format(checkpoint_path)) log('Loading training data from: {}'.format(input_path)) log('Using model: {}'.format(args.model)) log(hparams_debug_string()) #Start by setting a seed for repeatability tf.set_random_seed(hparams.tacotron_random_seed) #Set up data feeder coord = tf.train.Coordinator() with tf.variable_scope('datafeeder') as scope: feeder = Feeder(coord, input_path, hparams) #Set up model: global_step = tf.Variable(0, name='global_step', trainable=False) model, stats = model_train_mode(args, feeder, hparams, global_step) eval_model = model_test_mode(args, feeder, hparams, global_step) #Embeddings metadata char_embedding_meta = os.path.join(meta_folder, 'CharacterEmbeddings.tsv') if not os.path.isfile(char_embedding_meta): with open(char_embedding_meta, 'w', encoding='utf-8') as f: for symbol in symbols: if symbol == ' ': symbol = '\\s' #For visual purposes, swap space with \s f.write('{}\n'.format(symbol))
def train(log_dir, args, hparams): save_dir = os.path.join(log_dir, 'taco_pretrained') plot_dir = os.path.join(log_dir, 'plots') wav_dir = os.path.join(log_dir, 'wavs') mel_dir = os.path.join(log_dir, 'mel-spectrograms') eval_dir = os.path.join(log_dir, 'eval-dir') eval_plot_dir = os.path.join(eval_dir, 'plots') eval_wav_dir = os.path.join(eval_dir, 'wavs') tensorboard_dir = os.path.join(log_dir, 'tacotron_events') meta_folder = os.path.join(log_dir, 'metas') os.makedirs(save_dir, exist_ok=True) os.makedirs(plot_dir, exist_ok=True) os.makedirs(wav_dir, exist_ok=True) os.makedirs(mel_dir, exist_ok=True) os.makedirs(eval_dir, exist_ok=True) os.makedirs(eval_plot_dir, exist_ok=True) os.makedirs(eval_wav_dir, exist_ok=True) os.makedirs(tensorboard_dir, exist_ok=True) os.makedirs(meta_folder, exist_ok=True) checkpoint_path = os.path.join(save_dir, 'tacotron_model.ckpt') input_path = os.path.join(args.base_dir, args.tacotron_input) if hparams.predict_linear: linear_dir = os.path.join(log_dir, 'linear-spectrograms') os.makedirs(linear_dir, exist_ok=True) log('Checkpoint path: {}'.format(checkpoint_path)) log('Loading training data from: {}'.format(input_path)) log('Using model: {}'.format(args.model)) log(hparams_debug_string()) #Start by setting a seed for repeatability tf.set_random_seed(hparams.tacotron_random_seed) #Set up data feeder coord = tf.train.Coordinator() with tf.variable_scope('datafeeder') as scope: feeder = Feeder(coord, input_path, hparams, args) #Set up model: global_step = tf.Variable(0, name='global_step', trainable=False) model, stats = model_train_mode(args, feeder, hparams, global_step) eval_model = model_test_mode(args, hparams, model) # if args.TEST: # for v in tf.global_variables(): # print(v) #Embeddings metadata char_embedding_meta = os.path.join(meta_folder, 'CharacterEmbeddings.tsv') if not os.path.isfile(char_embedding_meta): with open(char_embedding_meta, 'w', encoding='utf-8') as f: for symbol in symbols: if symbol == ' ': symbol = '\\s' #For visual purposes, swap space with \s f.write('{}\n'.format(symbol)) char_embedding_meta = char_embedding_meta.replace(log_dir, '..') #Potential Griffin-Lim GPU setup if hparams.GL_on_GPU: GLGPU_mel_inputs = tf.placeholder(tf.float32, (None, hparams.num_mels), name='GLGPU_mel_inputs') GLGPU_lin_inputs = tf.placeholder(tf.float32, (None, hparams.num_freq), name='GLGPU_lin_inputs') GLGPU_mel_outputs = audio.inv_mel_spectrogram_tensorflow( GLGPU_mel_inputs, hparams) GLGPU_lin_outputs = audio.inv_linear_spectrogram_tensorflow( GLGPU_lin_inputs, hparams) #Book keeping step = 0 time_window = ValueWindow(100) loss_window = ValueWindow(100) loss_bef_window = ValueWindow(100) loss_aft_window = ValueWindow(100) loss_stop_window = ValueWindow(100) loss_reg_window = ValueWindow(100) loss_emt_window = ValueWindow(100) loss_spk_window = ValueWindow(100) loss_orthog_window = ValueWindow(100) loss_up_emt_window = ValueWindow(100) loss_up_spk_window = ValueWindow(100) loss_mo_up_emt_window = ValueWindow(100) loss_mo_up_spk_window = ValueWindow(100) if args.nat_gan: d_loss_t_window = ValueWindow(100) d_loss_p_window = ValueWindow(100) d_loss_up_window = ValueWindow(100) g_loss_p_window = ValueWindow(100) g_loss_up_window = ValueWindow(100) saver = tf.train.Saver(max_to_keep=args.max_to_keep) if args.opt_ref_no_mo and not (args.restart_optimizer_r): print( "WILL ATTEMPT TO RESTORE OPTIMIZER R - SET ARGS.RESTART_OPTIMIZER_R IF RETRAINING A MODEL THAT DIDN'T HAVE THE OPTIMIZER R" ) assert (not (args.restart_nat_gan_d and args.restore_nat_gan_d_sep)) var_list = tf.global_variables() var_list = [v for v in var_list if not ('pretrained' in v.name)] var_list = [ v for v in var_list if not ('nat_gan' in v.name or 'optimizer_n' in v.name) ] if (args.restart_nat_gan_d or args.restore_nat_gan_d_sep) else var_list var_list = [ v for v in var_list if not ('optimizer_r' in v.name or 'optimizer_3' in v.name) ] if args.restart_optimizer_r else var_list saver_restore = tf.train.Saver(var_list=var_list) if args.unpaired and args.pretrained_emb_disc: saver_restore_emt_disc = tf.train.Saver(var_list=[ v for v in tf.global_variables() if ('pretrained_ref_enc_emt' in v.name) ]) saver_restore_spk_disc = tf.train.Saver(var_list=[ v for v in tf.global_variables() if ('pretrained_ref_enc_spk' in v.name) ]) elif args.unpaired and args.pretrained_emb_disc_all: saver_restore_emt_disc = tf.train.Saver(var_list=[ v for v in tf.global_variables() if ('refnet_emt' in v.name) ]) saver_restore_spk_disc = tf.train.Saver(var_list=[ v for v in tf.global_variables() if ('refnet_spk' in v.name) ]) if args.nat_gan: saver_nat_gan = tf.train.Saver(var_list=[ v for v in tf.global_variables() if ('nat_gan' in v.name or 'optimizer_n' in v.name) ]) save_dir_nat_gan = r'nat_gan/pretrained_model' log('Tacotron training set to a maximum of {} steps'.format( args.tacotron_train_steps)) if hparams.tacotron_fine_tuning: print('FINE TUNING SET TO TRUE - MAKE SURE THIS IS WHAT YOU WANT!') #Memory allocation on the GPU as needed config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True eval_feed_dict, emt_labels, spk_labels, \ basenames, basenames_refs = get_eval_feed_dict(hparams, args.synth_metadata_filename, eval_model, args.input_dir, args.flip_spk_emt) #Train with tf.Session(config=config) as sess: try: summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph) # for x in tf.global_variables(): # print(x) sess.run(tf.global_variables_initializer()) #saved model restoring if args.restore: # Restore saved model if the user requested it, default = True try: checkpoint_state = tf.train.get_checkpoint_state(save_dir) if (checkpoint_state and checkpoint_state.model_checkpoint_path): log('Loading checkpoint {}'.format( checkpoint_state.model_checkpoint_path), slack=True) saver_restore.restore( sess, checkpoint_state.model_checkpoint_path) else: raise ValueError( 'No model to load at {}'.format(save_dir)) except tf.errors.OutOfRangeError as e: log('Cannot restore checkpoint: {}'.format(e), slack=True) else: log('Starting new training!', slack=True) saver.save(sess, checkpoint_path, global_step=global_step) if args.unpaired and (args.pretrained_emb_disc or args.pretrained_emb_disc_all): save_dir_emt = r'spk_disc/pretrained_model_emt_disc' checkpoint_state_emt = tf.train.get_checkpoint_state( save_dir_emt) saver_restore_emt_disc.restore( sess, checkpoint_state_emt.model_checkpoint_path) log('Loaded Emotion Discriminator from checkpoint {}'.format( checkpoint_state_emt.model_checkpoint_path), slack=True) save_dir_spk = r'spk_disc/pretrained_model_spk_disc' checkpoint_state_spk = tf.train.get_checkpoint_state( save_dir_spk) saver_restore_spk_disc.restore( sess, checkpoint_state_spk.model_checkpoint_path) log('Loaded Speaker Discriminator from checkpoint {}'.format( checkpoint_state_spk.model_checkpoint_path), slack=True) if args.nat_gan and args.restore_nat_gan_d_sep: checkpoint_state_nat_gan = tf.train.get_checkpoint_state( save_dir_nat_gan) saver_nat_gan.restore( sess, checkpoint_state_nat_gan.model_checkpoint_path) log('Loaded Nat Gan Discriminator from checkpoint {}'.format( checkpoint_state_nat_gan.model_checkpoint_path), slack=True) #initializing feeder feeder.start_threads(sess) #Training loop while not coord.should_stop() and step < args.tacotron_train_steps: start_time = time.time() # vars = [global_step, model.loss, model.optimize,model.before_loss, model.after_loss,model.stop_token_loss, # model.regularization_loss,model.style_emb_loss_emt, model.style_emb_loss_spk, model.style_emb_orthog_loss] # out = [step, loss, opt, bef, aft, stop, reg, loss_emt, loss_spk, loss_orthog] # message = 'Step {:7d} {:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}, bef={:.5f}, aft={:.5f}, stop={:.5f},' \ # 'reg={:.5f}, emt={:.5f}, spk={:.5f}, orthog={:.5f}'.format(step, time_window.average, loss, loss_window.average, # loss_bef_window.average, loss_aft_window.average, # loss_stop_window.average, loss_reg_window.average, # loss_emt_window.average, loss_spk_window.average, # loss_orthog_window.average) # if args.unpaired: # vars += [model.style_emb_loss_up_emt, model.style_emb_loss_up_spk,model.style_emb_loss_mel_out_up_emt, model.style_emb_loss_mel_out_up_spk] # out += [loss_up_emt, loss_up_spk, loss_mo_up_emt, loss_mo_up_spk] # message += ' up_emt={:.5f}, up_spk={:.5f}, mo_up_emt={:.5f}, mo_up_spk={:.5f}]'.format(loss_up_emt_window.average, # loss_up_spk_window.average, # loss_mo_up_emt_window.average, # loss_mo_up_spk_window.average) # if False: # vars += [model.tower_style_emb_logit_emt[0], model.tower_emt_labels[0],model.tower_style_emb_logit_up_emt[0], # model.tower_emt_up_labels[0],model.tower_spk_labels[0]] # out += [emt_logit, emt_labels, emt_up_logit, emt_up_labels, spk_labels] # # out = sess.run([vars]) if args.nat_gan and (args.restart_nat_gan_d or not (args.restore)) and step == 0: log("Will start with Training Nat GAN Discriminator", end='\r') disc_epochs = 300 if args.unpaired else 200 disc_epochs = 0 if args.TEST else disc_epochs for i in range(disc_epochs + 1): d_loss_t, d_loss_p, d_loss_up,\ d_loss_t_emt, d_loss_p_emt, d_loss_up_emt, \ d_loss_t_spk, d_loss_p_spk, d_loss_up_spk, \ opt_n = sess.run([model.d_loss_targ, model.d_loss_p, model.d_loss_up, model.d_loss_targ_emt, model.d_loss_p_emt, model.d_loss_up_emt, model.d_loss_targ_spk, model.d_loss_p_spk, model.d_loss_up_spk, model.optimize_n]) message = 'step: {}, d_loss_t={:.5f}, d_loss_p ={:.5f}, d_loss_up ={:.5f},' \ ' d_loss_t_emt={:.5f}, d_loss_p_emt ={:.5f}, d_loss_up_emt ={:.5f},' \ ' d_loss_t_spk={:.5f}, d_loss_p_spk ={:.5f}, d_loss_up_spk ={:.5f}'.format(i, d_loss_t, d_loss_p, d_loss_up, d_loss_t_emt, d_loss_p_emt, d_loss_up_emt, d_loss_t_spk, d_loss_p_spk, d_loss_up_spk) log(message, end='\r') os.makedirs(r'nat_gan', exist_ok=True) os.makedirs(r'nat_gan/pretrained_model', exist_ok=True) checkpoint_path_nat_gan = os.path.join( save_dir_nat_gan, 'nat_gan_model.ckpt') saver_nat_gan.save(sess, checkpoint_path_nat_gan, global_step=i) if args.nat_gan: d_loss_t, d_loss_p, d_loss_up, opt_n = sess.run([ model.d_loss_targ, model.d_loss_p, model.d_loss_up, model.optimize_n ]) if args.unpaired: step, tfr, loss, opt, bef, aft, stop, reg, loss_emt, loss_spk, loss_orthog, \ loss_up_emt, loss_up_spk, loss_mo_up_emt, loss_mo_up_spk, g_loss_p, g_loss_up, mels, opt_r\ = sess.run([global_step, model.ratio, model.loss, model.optimize,model.before_loss, model.after_loss,model.stop_token_loss, model.regularization_loss, model.style_emb_loss_emt, model.style_emb_loss_spk, model.style_emb_orthog_loss, model.style_emb_loss_up_emt, model.style_emb_loss_up_spk,model.style_emb_loss_mel_out_up_emt, model.style_emb_loss_mel_out_up_spk,model.g_loss_p, model.g_loss_up, model.tower_mel_outputs[0], model.optimize_r]) else: step, tfr, loss, opt, bef, aft, stop, reg, loss_emt, loss_spk, loss_orthog, \ loss_up_emt, loss_up_spk, loss_mo_up_emt, loss_mo_up_spk, g_loss_p, g_loss_up, mels,dec_out,opt_r = sess.run([global_step, model.helper._ratio, model.loss, model.optimize, model.before_loss, model.after_loss, model.stop_token_loss, model.regularization_loss, model.style_emb_loss_emt, model.style_emb_loss_spk, model.style_emb_orthog_loss, model.style_emb_loss_up_emt, model.style_emb_loss_up_spk,model.style_emb_loss_mel_out_up_emt, model.style_emb_loss_mel_out_up_spk, model.g_loss_p, model.g_loss_up, model.tower_mel_outputs[0],model.tower_decoder_output[0],model.optimize_r]) # step, loss, opt, bef, aft, stop, reg, loss_emt, loss_spk, loss_orthog, \ # loss_up_emt, loss_up_spk, loss_mo_up_emt, loss_mo_up_spk, g_loss_p, g_loss_up, mels,ref_emt,ref_spk,ref_up_emt,ref_up_spk,emb,enc_out,enc_out_up,\ # stop_pred, targ, inp, inp_len,targ_len,stop_targ,mels_up,dec_out,dec_out_up,opt_r\ # = sess.run([global_step, model.loss, model.optimize,model.before_loss, model.after_loss,model.stop_token_loss, # model.regularization_loss, model.style_emb_loss_emt, model.style_emb_loss_spk, model.style_emb_orthog_loss, # model.style_emb_loss_up_emt, model.style_emb_loss_up_spk,model.style_emb_loss_mel_out_up_emt, # model.style_emb_loss_mel_out_up_spk,model.g_loss_p, model.g_loss_up, model.tower_mel_outputs[0], # model.tower_refnet_out_emt[0],model.tower_refnet_out_spk[0],model.tower_refnet_out_up_emt[0],model.tower_refnet_out_up_spk[0], # model.tower_embedded_inputs[0], model.tower_encoder_outputs[0],model.tower_encoder_outputs_up[0],model.tower_stop_token_prediction[0], # model.tower_mel_targets[0],model.tower_inputs[0],model.tower_input_lengths[0],model.tower_targets_lengths[0], # model.tower_stop_token_targets[0],model.tower_mel_outputs_up[0],model.tower_decoder_output[0],model.tower_decoder_output_up[0],model.optimize_r]) # # if args.save_output_vars: # import pandas as pd # pd.DataFrame(emb[:, 0, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\emb.csv') # pd.DataFrame(enc_out[:, 0, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\enc_out.csv') # pd.DataFrame(enc_out_up[:, 0, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\enc_out_up.csv') # pd.DataFrame(stop_pred[:, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\stop.csv') # pd.DataFrame(targ[:, 0, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\targ.csv') # pd.DataFrame(inp[:, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\inp.csv') # pd.DataFrame(inp_len[:]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\inp_len.csv') # pd.DataFrame(targ_len[:]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\targ_len.csv') # pd.DataFrame(stop_targ[:, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\stop_targ.csv') # pd.DataFrame(mels_up[:, 0, 0:5]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\mels_up.csv') # pd.DataFrame(dec_out_up[:, 0, 0:5]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\dec_out_up.csv') if args.save_output_vars: import pandas as pd pd.DataFrame(mels[:, 0, 0:5]).to_csv( r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\mels.csv' ) pd.DataFrame(dec_out[:, 0, 0:5]).to_csv( r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\dec_out.csv' ) # import pandas as pd # print(emt_logit.shape, emt_labels.shape) # if len(emt_logit.shape)>2: # emt_logit = emt_logit.squeeze(1) # emt_up_logit = emt_up_logit.squeeze(1) # emt_labels = emt_labels.reshape(-1,1) # emt_up_labels = emt_up_labels.reshape(-1, 1) # spk_labels = spk_labels.reshape(-1, 1) # df = np.concatenate((emt_logit,emt_labels,spk_labels,emt_up_logit,emt_up_labels),axis=1) # print(emt_labels) # print(emt_logit) # print(emt_up_labels) # print(emt_up_logit) # # pd.DataFrame(df).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\emt_logit_.001_up_10k.csv') # raise time_window.append(time.time() - start_time) loss_window.append(loss) loss_bef_window.append(bef) loss_aft_window.append(aft) loss_stop_window.append(stop) loss_reg_window.append(reg) loss_emt_window.append(loss_emt) loss_spk_window.append(loss_spk) loss_orthog_window.append(loss_orthog) loss_up_emt_window.append(loss_up_emt) loss_up_spk_window.append(loss_up_spk) loss_mo_up_emt_window.append(loss_mo_up_emt) loss_mo_up_spk_window.append(loss_mo_up_spk) if args.nat_gan: d_loss_t_window.append(d_loss_t) d_loss_p_window.append(d_loss_p) d_loss_up_window.append(d_loss_up) g_loss_p_window.append(g_loss_p) g_loss_up_window.append(g_loss_up) message = 'Step {:7d} {:.3f} sec/step, tfr={:.3f}, loss={:.5f}, avg_loss={:.5f}, bef={:.5f}, aft={:.5f}, stop={:.5f}, reg={:.5f}'.format( step, time_window.average, tfr, loss, loss_window.average, loss_bef_window.average, loss_aft_window.average, loss_stop_window.average, loss_reg_window.average) if args.emt_attn: message += ' emt={:.5f}, spk={:.5f}, spk_l2={:.5f}'.format( loss_emt_window.average, loss_spk_window.average, loss_orthog_window.average) else: message += ' emt={:.5f}, spk={:.5f}, orthog={:.5f},'.format( loss_emt_window.average, loss_spk_window.average, loss_orthog_window.average) if args.unpaired: message += ' up_emt={:.5f}, up_spk={:.5f}, mo_up_emt={:.5f}, mo_up_spk={:.5f}'.format( loss_up_emt_window.average, loss_up_spk_window.average, loss_mo_up_emt_window.average, loss_mo_up_spk_window.average) if args.nat_gan: message += ' d_loss_t={:.5f}, d_loss_p ={:.5f}, d_loss_up ={:.5f}, g_loss_p ={:.5f}, g_loss_up ={:.5f}'.format( d_loss_t_window.average, d_loss_p_window.average, d_loss_up_window.average, g_loss_p_window.average, g_loss_up_window.average) log(message, end='\r', slack=(step % args.checkpoint_interval == 0)) if np.isnan(loss) or loss > 100.: log('Loss exploded to {:.5f} at step {}'.format( loss, step)) raise Exception('Loss exploded') if step % args.summary_interval == 0: log('\nWriting summary at step {}'.format(step)) summary_writer.add_summary(sess.run(stats), step) # if step % args.eval_interval == 0: # #Run eval and save eval stats # log('\nRunning evaluation and saving model at step {}'.format(step)) # saver.save(sess, checkpoint_path, global_step=global_step) # # eval_losses = [] # before_losses = [] # after_losses = [] # stop_token_losses = [] # linear_losses = [] # linear_loss = None # # if hparams.predict_linear: # for i in tqdm(range(feeder.test_steps)): # eloss, before_loss, after_loss, stop_token_loss, linear_loss, mel_p, mel_t, t_len, align, lin_p, lin_t = sess.run([ # eval_model.tower_loss[0], eval_model.tower_before_loss[0], eval_model.tower_after_loss[0], # eval_model.tower_stop_token_loss[0], eval_model.tower_linear_loss[0], eval_model.tower_mel_outputs[0][0], # eval_model.tower_mel_targets[0][0], eval_model.tower_targets_lengths[0][0], # eval_model.tower_alignments[0][0], eval_model.tower_linear_outputs[0][0], # eval_model.tower_linear_targets[0][0], # ]) # eval_losses.append(eloss) # before_losses.append(before_loss) # after_losses.append(after_loss) # stop_token_losses.append(stop_token_loss) # linear_losses.append(linear_loss) # linear_loss = sum(linear_losses) / len(linear_losses) # # if hparams.GL_on_GPU: # wav = sess.run(GLGPU_lin_outputs, feed_dict={GLGPU_lin_inputs: lin_p}) # wav = audio.inv_preemphasis(wav, hparams.preemphasis, hparams.preemphasize) # else: # wav = audio.inv_linear_spectrogram(lin_p.T, hparams) # audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-wave-from-linear.wav'.format(step)), sr=hparams.sample_rate) # # else: # for i in tqdm(range(feeder.test_steps)): # eloss, before_loss, after_loss, stop_token_loss, input_seq, mel_p, mel_t, t_len, align = sess.run([ # eval_model.tower_loss[0], eval_model.tower_before_loss[0], eval_model.tower_after_loss[0], # eval_model.tower_stop_token_loss[0],eval_model.tower_inputs[0][0], eval_model.tower_mel_outputs[0][0], # eval_model.tower_mel_targets[0][0], # eval_model.tower_targets_lengths[0][0], eval_model.tower_alignments[0][0] # ]) # eval_losses.append(eloss) # before_losses.append(before_loss) # after_losses.append(after_loss) # stop_token_losses.append(stop_token_loss) # # eval_loss = sum(eval_losses) / len(eval_losses) # before_loss = sum(before_losses) / len(before_losses) # after_loss = sum(after_losses) / len(after_losses) # stop_token_loss = sum(stop_token_losses) / len(stop_token_losses) # # # log('Saving eval log to {}..'.format(eval_dir)) # #Save some log to monitor model improvement on same unseen sequence # if hparams.GL_on_GPU: # wav = sess.run(GLGPU_mel_outputs, feed_dict={GLGPU_mel_inputs: mel_p}) # wav = audio.inv_preemphasis(wav, hparams.preemphasis, hparams.preemphasize) # else: # wav = audio.inv_mel_spectrogram(mel_p.T, hparams) # audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-wave-from-mel.wav'.format(step)), sr=hparams.sample_rate) # # input_seq = sequence_to_text(input_seq) # plot.plot_alignment(align, os.path.join(eval_plot_dir, 'step-{}-eval-align.png'.format(step)), # title='{}, {}, step={}, loss={:.5f}\n{}'.format(args.model, time_string(), step, eval_loss, input_seq), # max_len=t_len // hparams.outputs_per_step) # plot.plot_spectrogram(mel_p, os.path.join(eval_plot_dir, 'step-{}-eval-mel-spectrogram.png'.format(step)), # title='{}, {}, step={}, loss={:.5f}\n{}'.format(args.model, time_string(), step, eval_loss,input_seq), target_spectrogram=mel_t, # max_len=t_len) # # if hparams.predict_linear: # plot.plot_spectrogram(lin_p, os.path.join(eval_plot_dir, 'step-{}-eval-linear-spectrogram.png'.format(step)), # title='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, eval_loss), target_spectrogram=lin_t, # max_len=t_len, auto_aspect=True) # # log('Step {:7d} [eval loss: {:.3f}, before loss: {:.3f}, after loss: {:.3f}, stop loss: {:.3f}]'.format(step, eval_loss, before_loss, after_loss, stop_token_loss)) # # log('Writing eval summary!') # add_eval_stats(summary_writer, step, linear_loss, before_loss, after_loss, stop_token_loss, eval_loss) if step % args.checkpoint_interval == 0 or step == args.tacotron_train_steps or step == 300: #Save model and current global step saver.save(sess, checkpoint_path, global_step=global_step) log('\nSaved model at step {}'.format(step)) if step % args.eval_interval == 0: if hparams.predict_linear: raise ValueError('predict linear not implemented') # input_seq, mel_prediction, linear_prediction, alignment, target, target_length, linear_target = sess.run([ # model.tower_inputs[0][0], # model.tower_mel_outputs[0][0], # model.tower_linear_outputs[0][0], # model.tower_alignments[0][0], # model.tower_mel_targets[0][0], # model.tower_targets_lengths[0][0], # model.tower_linear_targets[0][0], # ]) # # #save predicted linear spectrogram to disk (debug) # linear_filename = 'linear-prediction-step-{}.npy'.format(step) # np.save(os.path.join(linear_dir, linear_filename), linear_prediction.T, allow_pickle=False) # # #save griffin lim inverted wav for debug (linear -> wav) # if hparams.GL_on_GPU: # wav = sess.run(GLGPU_lin_outputs, feed_dict={GLGPU_lin_inputs: linear_prediction}) # wav = audio.inv_preemphasis(wav, hparams.preemphasis, hparams.preemphasize) # else: # wav = audio.inv_linear_spectrogram(linear_prediction.T, hparams) # audio.save_wav(wav, os.path.join(wav_dir, 'step-{}-wave-from-linear.wav'.format(step)), sr=hparams.sample_rate) # # #Save real and predicted linear-spectrogram plot to disk (control purposes) # plot.plot_spectrogram(linear_prediction, os.path.join(plot_dir, 'step-{}-linear-spectrogram.png'.format(step)), # title='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, loss), target_spectrogram=linear_target, # max_len=target_length, auto_aspect=True) else: input_seqs, mels, alignments,\ stop_tokens = sess.run([eval_model.tower_inputs, eval_model.tower_mel_outputs, eval_model.tower_alignments, eval_model.tower_stop_token_prediction], feed_dict=eval_feed_dict) # num_evals = len(input_seqs) if False else 1 # for i in range(num_evals): # input_seq = input_seqs[i] # mel_prediction = mel_predictions[i] # alignment = alignments[i] # target = targets[i] # target_length = target_lengths[i] # emt = emts[i] # spk = spks[i] # if args.emt_attn and args.attn=='simple': # alignment_emt = alignments_emt[0][i] # Linearize outputs (n_gpus -> 1D) inp = [ inp for gpu_inp in input_seqs for inp in gpu_inp ] mels = [mel for gpu_mels in mels for mel in gpu_mels] # targets = [target for gpu_targets in targets for target in gpu_targets] alignments = [ align for gpu_aligns in alignments for align in gpu_aligns ] stop_tokens = [ token for gpu_token in stop_tokens for token in gpu_token ] try: target_lengths = get_output_lengths(stop_tokens) # Take off the batch wise padding mels = [ mel[:target_length, :] for mel, target_length in zip( mels, target_lengths) ] T2_output_range = ( -hparams.max_abs_value, hparams.max_abs_value ) if hparams.symmetric_mels else ( 0, hparams.max_abs_value) mels = [ np.clip(m, T2_output_range[0], T2_output_range[1]) for m in mels ] folder_bucket = 'step_{}'.format(step // 500) folder_wavs_save = os.path.join( wav_dir, folder_bucket) folder_plot_save = os.path.join( plot_dir, folder_bucket) os.makedirs(folder_wavs_save, exist_ok=True) os.makedirs(folder_plot_save, exist_ok=True) for i, (mel, align, basename, basename_ref) in enumerate( zip(mels, alignments, basenames, basenames_refs)): #save griffin lim inverted wav for debug (mel -> wav) if hparams.GL_on_GPU: wav = sess.run( GLGPU_mel_outputs, feed_dict={GLGPU_mel_inputs: mel}) wav = audio.inv_preemphasis( wav, hparams.preemphasis, hparams.preemphasize) else: wav = audio.inv_mel_spectrogram( mel.T, hparams) audio.save_wav( wav, os.path.join( folder_wavs_save, 'step_{}_wav_{}_{}_{}.wav'.format( step, i, basename, basename_ref)), sr=hparams.sample_rate) input_seq = sequence_to_text(inp[i]) #save alignment plot to disk (control purposes) try: plot.plot_alignment( align, os.path.join( folder_plot_save, 'step_{}_wav_{}_{}_{}_align.png'. format(step, i, basename, basename_ref)), title='{}, {}, step={}\n{}'.format( args.model, time_string(), step, input_seq), max_len=target_lengths[i] // hparams.outputs_per_step) except: print("failed to plot alignment") try: #save real and predicted mel-spectrogram plot to disk (control purposes) plot.plot_spectrogram( mel, os.path.join( folder_plot_save, 'step-{}-{}-mel-spectrogram.png'. format(step, i)), title='{}, {}, step={}\n{}'.format( args.model, time_string(), step, input_seq)) # target_spectrogram=targets[i], # max_len=target_lengths[i]) except: print("failed to plot spectrogram") log('Saved synthesized samples for step {}'.format( step), end='\r') except: print("Couldn't synthesize samples") # log('Input at step {}: {}'.format(step, input_seq), end='\r') # if step % args.embedding_interval == 0 or step == args.tacotron_train_steps or step == 1: # #Get current checkpoint state # checkpoint_state = tf.train.get_checkpoint_state(save_dir) # # #Update Projector # log('\nSaving Model Character Embeddings visualization..') # add_embedding_stats(summary_writer, [model.embedding_table.name], [char_embedding_meta], checkpoint_state.model_checkpoint_path) # log('Tacotron Character embeddings have been updated on tensorboard!') log('Tacotron training complete after {} global steps!'.format( args.tacotron_train_steps), slack=True) return save_dir except Exception as e: log('Exiting due to exception: {}'.format(e), slack=True) traceback.print_exc() coord.request_stop(e)
def train(path, restore=False, restore_path=''): checkpoint_path, log_path = path metadata_filename = 'C:/Users/t-mawhit/Documents/code/Tacotron-2/data/emt4/train.txt' setup_log(log_path, checkpoint_path, os.path.dirname(metadata_filename)) coord = tf.train.Coordinator() feeder = Feeder(coord, metadata_filename, hparams) labels = tf.one_hot(feeder.emt_labels, 4) labels_eval = tf.one_hot(feeder.eval_emt_labels, 4) training = tf.placeholder(tf.bool) emt_disc = Emt_Disc(feeder.mel_targets, is_training=training, hparams=hparams) emt_disc_eval = Emt_Disc(feeder.eval_mel_targets, is_training=training, hparams=hparams) loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=emt_disc.logit, labels=labels)) acc = tf.reduce_mean( tf.cast(tf.equal(tf.argmax(labels, 1), tf.argmax(emt_disc.logit, 1)), 'float32')) loss_val = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=emt_disc_eval.logit, labels=labels_eval)) acc_val = tf.reduce_mean( tf.cast( tf.equal(tf.argmax(labels_eval, 1), tf.argmax(emt_disc_eval.logit, 1)), 'float32')) update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): training_op = tf.train.AdamOptimizer(1e-4).minimize(loss) saver = tf.train.Saver(max_to_keep=20) with tf.Session() as sess: if restore: saver.restore(sess, restore_path) sess.run(tf.global_variables_initializer()) feeder.start_threads(sess) num_batch_ckpt = 10 #64 num_val_batches = feeder.test_steps num_batch_sav_ckpt = 20 batches = 0 total_loss = 0 total_acc = 0 total_loss_val = 0 total_acc_val = 0 while not coord.should_stop(): cur_loss, cur_acc, _ = sess.run([loss, acc, training_op], feed_dict={training: True}) total_loss += cur_loss total_acc += cur_acc batches += 1 if batches % num_batch_ckpt == 0: for i in range(num_val_batches): cur_loss_val, cur_acc_val = sess.run( [loss_val, acc_val], feed_dict={training: False}) total_loss_val += cur_loss_val total_acc_val += cur_acc_val message = "Batches Processed: {0} | Tr Loss: {1:5.3f} | Val Loss: {2:5.3f} | Tr Acc: {3:4.2f}% | Val Acc: {4:4.2f}%".format( batches, total_loss / num_batch_ckpt, total_loss_val / num_val_batches, total_acc * 100 / num_batch_ckpt, total_acc_val * 100 / num_val_batches) print(message) log(message, end='\r') total_loss = 0 total_acc = 0 total_loss_val = 0 total_acc_val = 0 if batches % num_batch_sav_ckpt == 0: saver.save(sess, checkpoint_path, global_step=batches)