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train_aligner.py
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train_aligner.py
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import tensorflow as tf
import numpy as np
from tqdm import trange
from utils.training_config_manager import TrainingConfigManager
from data.datasets import AlignerDataset, AlignerPreprocessor
from utils.decorators import ignore_exception, time_it
from utils.scheduling import piecewise_linear_schedule, reduction_schedule
from utils.logging_utils import SummaryManager
from utils.scripts_utils import dynamic_memory_allocation, basic_train_parser
from utils.metrics import attention_score
from utils.spectrogram_ops import mel_lengths, phoneme_lengths
from utils.alignments import get_durations_from_alignment
np.random.seed(42)
tf.random.set_seed(42)
dynamic_memory_allocation()
parser = basic_train_parser()
args = parser.parse_args()
def cut_with_durations(durations, mel, phonemes, snippet_len=10):
phon_dur = np.pad(durations, (1, 0))
starts = np.cumsum(phon_dur)[:-1]
ends = np.cumsum(phon_dur)[1:]
cut_mels = []
cut_texts = []
for end_idx in range(snippet_len, len(phon_dur), snippet_len):
start_idx = end_idx - snippet_len
cut_mels.append(mel[starts[start_idx]: ends[end_idx - 1], :])
cut_texts.append(phonemes[start_idx: end_idx])
return cut_mels, cut_texts
@ignore_exception
@time_it
def validate(model,
val_dataset,
summary_manager,
weighted_durations):
val_loss = {'loss': 0.}
norm = 0.
current_r = model.r
model.set_constants(reduction_factor=1)
for val_mel, val_text, val_stop, fname in val_dataset.all_batches():
model_out = model.val_step(inp=val_text,
tar=val_mel,
stop_prob=val_stop)
norm += 1
val_loss['loss'] += model_out['loss']
val_loss['loss'] /= norm
summary_manager.display_loss(model_out, tag='Validation', plot_all=True)
summary_manager.display_last_attention(model_out, tag='ValidationAttentionHeads', fname=fname)
attention_values = model_out['decoder_attention']['Decoder_LastBlock_CrossAttention'].numpy()
text = val_text.numpy()
mel = val_mel.numpy()
model.set_constants(reduction_factor=current_r)
modes = list({False, weighted_durations})
for mode in modes:
durations, final_align, jumpiness, peakiness, diag_measure = get_durations_from_alignment(
batch_alignments=attention_values,
mels=mel,
phonemes=text,
weighted=mode)
for k in range(len(durations)):
phon_dur = durations[k]
imel = mel[k][1:] # remove start token (is padded so end token can't be removed/not an issue)
itext = text[k][1:] # remove start token (is padded so end token can't be removed/not an issue)
iphon = model.text_pipeline.tokenizer.decode(itext).replace('/', '')
cut_mels, cut_texts = cut_with_durations(durations=phon_dur, mel=imel, phonemes=iphon)
for cut_idx, cut_text in enumerate(cut_texts):
weighted_label = 'weighted_' * mode
summary_manager.display_audio(
tag=f'CutAudio {weighted_label}{fname[k].numpy().decode("utf-8")}/{cut_idx}/{cut_text}',
mel=cut_mels[cut_idx], description=iphon)
return val_loss['loss']
config_manager = TrainingConfigManager(config_path=args.config, aligner=True)
config = config_manager.config
config_manager.create_remove_dirs(clear_dir=args.clear_dir,
clear_logs=args.clear_logs,
clear_weights=args.clear_weights)
config_manager.dump_config()
config_manager.print_config()
# get model, prepare data for model, create datasets
model = config_manager.get_model()
config_manager.compile_model(model)
data_prep = AlignerPreprocessor.from_config(config_manager,
tokenizer=model.text_pipeline.tokenizer) # TODO: tokenizer is now static
train_data_handler = AlignerDataset.from_config(config_manager,
preprocessor=data_prep,
kind='train')
valid_data_handler = AlignerDataset.from_config(config_manager,
preprocessor=data_prep,
kind='valid')
train_dataset = train_data_handler.get_dataset(bucket_batch_sizes=config['bucket_batch_sizes'],
bucket_boundaries=config['bucket_boundaries'],
shuffle=True)
valid_dataset = valid_data_handler.get_dataset(bucket_batch_sizes=config['val_bucket_batch_size'],
bucket_boundaries=config['bucket_boundaries'],
shuffle=False, drop_remainder=True)
# create logger and checkpointer and restore latest model
summary_manager = SummaryManager(model=model, log_dir=config_manager.log_dir, config=config)
checkpoint = tf.train.Checkpoint(step=tf.Variable(1),
optimizer=model.optimizer,
net=model)
manager = tf.train.CheckpointManager(checkpoint, str(config_manager.weights_dir),
max_to_keep=config['keep_n_weights'],
keep_checkpoint_every_n_hours=config['keep_checkpoint_every_n_hours'])
manager_training = tf.train.CheckpointManager(checkpoint, str(config_manager.weights_dir / 'latest'),
max_to_keep=1, checkpoint_name='latest')
checkpoint.restore(manager_training.latest_checkpoint)
if manager_training.latest_checkpoint:
print(f'\nresuming training from step {model.step} ({manager_training.latest_checkpoint})')
else:
print(f'\nstarting training from scratch')
if config['debug'] is True:
print('\nWARNING: DEBUG is set to True. Training in eager mode.')
# main event
print('\nTRAINING')
texts = []
for text_file in config['test_stencences']:
with open(text_file, 'r') as file:
text = file.readlines()
texts.append(text)
losses = []
test_mel, test_phonemes, _, test_fname = valid_dataset.next_batch()
val_test_sample, val_test_fname, val_test_mel = test_phonemes[0], test_fname[0], test_mel[0]
val_test_sample = tf.boolean_mask(val_test_sample, val_test_sample!=0)
_ = train_dataset.next_batch()
t = trange(model.step, config['max_steps'], leave=True)
for _ in t:
t.set_description(f'step {model.step}')
mel, phonemes, stop, sample_name = train_dataset.next_batch()
learning_rate = piecewise_linear_schedule(model.step, config['learning_rate_schedule'])
reduction_factor = reduction_schedule(model.step, config['reduction_factor_schedule'])
t.display(f'reduction factor {reduction_factor}', pos=10)
force_encoder_diagonal = model.step < config['force_encoder_diagonal_steps']
force_decoder_diagonal = model.step < config['force_decoder_diagonal_steps']
model.set_constants(learning_rate=learning_rate,
reduction_factor=reduction_factor,
force_encoder_diagonal=force_encoder_diagonal,
force_decoder_diagonal=force_decoder_diagonal)
output = model.train_step(inp=phonemes,
tar=mel,
stop_prob=stop)
losses.append(float(output['loss']))
t.display(f'step loss: {losses[-1]}', pos=1)
for pos, n_steps in enumerate(config['n_steps_avg_losses']):
if len(losses) > n_steps:
t.display(f'{n_steps}-steps average loss: {sum(losses[-n_steps:]) / n_steps}', pos=pos + 2)
summary_manager.display_loss(output, tag='Train')
summary_manager.display_scalar(tag='Meta/learning_rate', scalar_value=model.optimizer.lr)
summary_manager.display_scalar(tag='Meta/reduction_factor', scalar_value=model.r)
summary_manager.display_scalar(scalar_value=t.avg_time, tag='Meta/iter_time')
summary_manager.display_scalar(scalar_value=tf.shape(sample_name)[0], tag='Meta/batch_size')
if model.step % config['train_images_plotting_frequency'] == 0:
summary_manager.display_attention_heads(output, tag='TrainAttentionHeads')
summary_manager.display_mel(mel=output['mel'][0], tag=f'Train/predicted_mel')
for layer, k in enumerate(output['decoder_attention'].keys()):
mel_lens = mel_lengths(mel_batch=mel, padding_value=0) // model.r # [N]
phon_len = phoneme_lengths(phonemes)
loc_score, peak_score, diag_measure = attention_score(att=output['decoder_attention'][k],
mel_len=mel_lens,
phon_len=phon_len,
r=model.r)
loc_score = tf.reduce_mean(loc_score, axis=0)
peak_score = tf.reduce_mean(peak_score, axis=0)
diag_measure = tf.reduce_mean(diag_measure, axis=0)
for i in range(tf.shape(loc_score)[0]):
summary_manager.display_scalar(tag=f'TrainDecoderAttentionJumpiness/layer{layer}_head{i}',
scalar_value=tf.reduce_mean(loc_score[i]))
summary_manager.display_scalar(tag=f'TrainDecoderAttentionPeakiness/layer{layer}_head{i}',
scalar_value=tf.reduce_mean(peak_score[i]))
summary_manager.display_scalar(tag=f'TrainDecoderAttentionDiagonality/layer{layer}_head{i}',
scalar_value=tf.reduce_mean(diag_measure[i]))
if model.step % 1000 == 0:
save_path = manager_training.save()
if model.step % config['weights_save_frequency'] == 0:
save_path = manager.save()
t.display(f'checkpoint at step {model.step}: {save_path}', pos=len(config['n_steps_avg_losses']) + 2)
if model.step % config['validation_frequency'] == 0 and (model.step >= config['prediction_start_step']):
val_loss, time_taken = validate(model=model,
val_dataset=valid_dataset,
summary_manager=summary_manager,
weighted_durations=config['extract_attention_weighted'])
t.display(f'validation loss at step {model.step}: {val_loss} (took {time_taken}s)',
pos=len(config['n_steps_avg_losses']) + 3)
if model.step % config['prediction_frequency'] == 0 and (model.step >= config['prediction_start_step']):
for j, text in enumerate(texts):
for i, text_line in enumerate(text):
out = model.predict(text_line, encode=True)
wav = summary_manager.audio.reconstruct_waveform(out['mel'].numpy().T)
wav = tf.expand_dims(wav, 0)
wav = tf.expand_dims(wav, -1)
summary_manager.add_audio(f'Predictions/{text_line}', wav.numpy(), sr=summary_manager.config['sampling_rate'],
step=summary_manager.global_step)
out = model.predict(val_test_sample, encode=False)#, max_length=tf.shape(val_test_mel)[-2])
wav = summary_manager.audio.reconstruct_waveform(out['mel'].numpy().T)
wav = tf.expand_dims(wav, 0)
wav = tf.expand_dims(wav, -1)
summary_manager.add_audio(f'Predictions/val_sample {val_test_fname.numpy().decode("utf-8")}', wav.numpy(), sr=summary_manager.config['sampling_rate'],
step=summary_manager.global_step)
print('Done.')