def create_all_dags(args, neural_factory):
logger = neural_factory.logger
yaml = YAML(typ="safe")
with open(args.model_config) as f:
jasper_params = yaml.load(f)
vocab = jasper_params['labels']
sample_rate = jasper_params['sample_rate']
# Calculate num_workers for dataloader
total_cpus = os.cpu_count()
cpu_per_traindl = max(int(total_cpus / neural_factory.world_size), 1)
# perturb_config = jasper_params.get('perturb', None)
train_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"])
train_dl_params.update(jasper_params["AudioToTextDataLayer"]["train"])
del train_dl_params["train"]
del train_dl_params["eval"]
# del train_dl_params["normalize_transcripts"]
data_layer = nemo_asr.AudioToTextDataLayer(
manifest_filepath=args.train_dataset,
sample_rate=sample_rate,
labels=vocab,
batch_size=args.batch_size,
num_workers=cpu_per_traindl,
**train_dl_params,
# normalize_transcripts=False
)
N = len(data_layer)
steps_per_epoch = int(
N / (args.batch_size * args.iter_per_step * args.num_gpus))
logger.info('Have {0} examples to train on.'.format(N))
data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor(
sample_rate=sample_rate,
**jasper_params["AudioToMelSpectrogramPreprocessor"])
multiply_batch_config = jasper_params.get('MultiplyBatch', None)
if multiply_batch_config:
multiply_batch = nemo_asr.MultiplyBatch(**multiply_batch_config)
spectr_augment_config = jasper_params.get('SpectrogramAugmentation', None)
if spectr_augment_config:
data_spectr_augmentation = nemo_asr.SpectrogramAugmentation(
**spectr_augment_config)
eval_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"])
eval_dl_params.update(jasper_params["AudioToTextDataLayer"]["eval"])
del eval_dl_params["train"]
del eval_dl_params["eval"]
data_layers_eval = []
if args.eval_datasets:
for eval_datasets in args.eval_datasets:
data_layer_eval = nemo_asr.AudioToTextDataLayer(
manifest_filepath=eval_datasets,
sample_rate=sample_rate,
labels=vocab,
batch_size=args.eval_batch_size,
num_workers=cpu_per_traindl,
**eval_dl_params,
)
data_layers_eval.append(data_layer_eval)
else:
neural_factory.logger.info("There were no val datasets passed")
jasper_encoder = nemo_asr.JasperEncoder(
feat_in=jasper_params["AudioToMelSpectrogramPreprocessor"]["features"],
**jasper_params["JasperEncoder"])
jasper_decoder = nemo_asr.JasperDecoderForCTC(
feat_in=jasper_params["JasperEncoder"]["jasper"][-1]["filters"],
num_classes=len(vocab),
factory=neural_factory)
ctc_loss = nemo_asr.CTCLossNM(
num_classes=len(vocab))
greedy_decoder = nemo_asr.GreedyCTCDecoder()
logger.info('================================')
logger.info(
f"Number of parameters in encoder: {jasper_encoder.num_weights}")
logger.info(
f"Number of parameters in decoder: {jasper_decoder.num_weights}")
logger.info(
f"Total number of parameters in decoder: "
f"{jasper_decoder.num_weights + jasper_encoder.num_weights}")
logger.info('================================')
# Train DAG
audio_signal_t, a_sig_length_t, \
transcript_t, transcript_len_t = data_layer()
processed_signal_t, p_length_t = data_preprocessor(
input_signal=audio_signal_t,
length=a_sig_length_t)
if multiply_batch_config:
processed_signal_t, p_length_t, transcript_t, transcript_len_t = \
multiply_batch(
in_x=processed_signal_t, in_x_len=p_length_t,
in_y=transcript_t,
in_y_len=transcript_len_t)
if spectr_augment_config:
processed_signal_t = data_spectr_augmentation(
input_spec=processed_signal_t)
encoded_t, encoded_len_t = jasper_encoder(
audio_signal=processed_signal_t,
length=p_length_t)
log_probs_t = jasper_decoder(encoder_output=encoded_t)
predictions_t = greedy_decoder(log_probs=log_probs_t)
loss_t = ctc_loss(
log_probs=log_probs_t,
targets=transcript_t,
input_length=encoded_len_t,
target_length=transcript_len_t)
# Callbacks needed to print info to console and Tensorboard
train_callback = nemo.core.SimpleLossLoggerCallback(
tensors=[loss_t, predictions_t, transcript_t, transcript_len_t],
print_func=partial(
monitor_asr_train_progress,
labels=vocab,
logger=logger),
get_tb_values=lambda x: [("loss", x[0])],
tb_writer=neural_factory.tb_writer,
)
chpt_callback = nemo.core.CheckpointCallback(
folder=neural_factory.checkpoint_dir,
step_freq=args.checkpoint_save_freq)
callbacks = [train_callback, chpt_callback]
# assemble eval DAGs
for i, eval_dl in enumerate(data_layers_eval):
audio_signal_e, a_sig_length_e, transcript_e, transcript_len_e = \
eval_dl()
processed_signal_e, p_length_e = data_preprocessor(
input_signal=audio_signal_e,
length=a_sig_length_e)
encoded_e, encoded_len_e = jasper_encoder(
audio_signal=processed_signal_e,
length=p_length_e)
log_probs_e = jasper_decoder(encoder_output=encoded_e)
predictions_e = greedy_decoder(log_probs=log_probs_e)
loss_e = ctc_loss(
log_probs=log_probs_e,
targets=transcript_e,
input_length=encoded_len_e,
target_length=transcript_len_e)
# create corresponding eval callback
tagname = os.path.basename(args.eval_datasets[i]).split(".")[0]
eval_callback = nemo.core.EvaluatorCallback(
eval_tensors=[loss_e, predictions_e,
transcript_e, transcript_len_e],
user_iter_callback=partial(
process_evaluation_batch,
labels=vocab),
user_epochs_done_callback=partial(
process_evaluation_epoch,
tag=tagname,
logger=logger),
eval_step=args.eval_freq,
tb_writer=neural_factory.tb_writer)
callbacks.append(eval_callback)
return loss_t, callbacks, steps_per_epoch
def create_all_dags(args, neural_factory):
'''
creates train and eval dags as well as their callbacks
returns train loss tensor and callbacks'''
# parse the config files
yaml = YAML(typ="safe")
with open(args.model_config) as f:
quartz_params = yaml.load(f)
vocab = quartz_params['labels']
sample_rate = quartz_params['sample_rate']
# Calculate num_workers for dataloader
total_cpus = os.cpu_count()
cpu_per_traindl = max(int(total_cpus / neural_factory.world_size), 1)
# create data layer for training
train_dl_params = copy.deepcopy(quartz_params["AudioToTextDataLayer"])
train_dl_params.update(quartz_params["AudioToTextDataLayer"]["train"])
del train_dl_params["train"]
del train_dl_params["eval"]
# del train_dl_params["normalize_transcripts"]
data_layer_train = nemo_asr.AudioToTextDataLayer(
manifest_filepath=args.train_dataset,
sample_rate=sample_rate,
labels=vocab,
batch_size=args.batch_size,
num_workers=cpu_per_traindl,
**train_dl_params,
# normalize_transcripts=False
)
N = len(data_layer_train)
steps_per_epoch = int(
N / (args.batch_size * args.iter_per_step * args.num_gpus))
# create separate data layers for eval
# we need separate eval dags for separate eval datasets
# but all other modules in these dags will be shared
eval_dl_params = copy.deepcopy(quartz_params["AudioToTextDataLayer"])
eval_dl_params.update(quartz_params["AudioToTextDataLayer"]["eval"])
del eval_dl_params["train"]
del eval_dl_params["eval"]
data_layers_eval = []
if args.eval_datasets:
for eval_dataset in args.eval_datasets:
data_layer_eval = nemo_asr.AudioToTextDataLayer(
manifest_filepath=eval_dataset,
sample_rate=sample_rate,
labels=vocab,
batch_size=args.eval_batch_size,
num_workers=cpu_per_traindl,
**eval_dl_params,
)
data_layers_eval.append(data_layer_eval)
else:
nemo.logging.warning("There were no val datasets passed")
# create shared modules
data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor(
sample_rate=sample_rate,
**quartz_params["AudioToMelSpectrogramPreprocessor"])
# (QuartzNet uses the Jasper baseline encoder and decoder)
encoder = nemo_asr.JasperEncoder(
feat_in=quartz_params["AudioToMelSpectrogramPreprocessor"]["features"],
**quartz_params["JasperEncoder"])
decoder = nemo_asr.JasperDecoderForCTC(
feat_in=quartz_params["JasperEncoder"]["jasper"][-1]["filters"],
num_classes=len(vocab))
ctc_loss = nemo_asr.CTCLossNM(num_classes=len(vocab))
greedy_decoder = nemo_asr.GreedyCTCDecoder()
# create augmentation modules (only used for training) if their configs
# are present
multiply_batch_config = quartz_params.get('MultiplyBatch', None)
if multiply_batch_config:
multiply_batch = nemo_asr.MultiplyBatch(**multiply_batch_config)
spectr_augment_config = quartz_params.get('SpectrogramAugmentation', None)
if spectr_augment_config:
data_spectr_augmentation = nemo_asr.SpectrogramAugmentation(
**spectr_augment_config)
# assemble train DAG
audio_signal_t, a_sig_length_t, \
transcript_t, transcript_len_t = data_layer_train()
processed_signal_t, p_length_t = data_preprocessor(
input_signal=audio_signal_t, length=a_sig_length_t)
if multiply_batch_config:
processed_signal_t, p_length_t, transcript_t, transcript_len_t = \
multiply_batch(
in_x=processed_signal_t, in_x_len=p_length_t,
in_y=transcript_t,
in_y_len=transcript_len_t)
if spectr_augment_config:
processed_signal_t = data_spectr_augmentation(
input_spec=processed_signal_t)
encoded_t, encoded_len_t = encoder(audio_signal=processed_signal_t,
length=p_length_t)
log_probs_t = decoder(encoder_output=encoded_t)
predictions_t = greedy_decoder(log_probs=log_probs_t)
loss_t = ctc_loss(log_probs=log_probs_t,
targets=transcript_t,
input_length=encoded_len_t,
target_length=transcript_len_t)
# create train callbacks
train_callback = nemo.core.SimpleLossLoggerCallback(
tensors=[loss_t, predictions_t, transcript_t, transcript_len_t],
print_func=partial(monitor_asr_train_progress, labels=vocab),
get_tb_values=lambda x: [["loss", x[0]]],
tb_writer=neural_factory.tb_writer)
callbacks = [train_callback]
if args.checkpoint_dir or args.load_dir:
chpt_callback = nemo.core.CheckpointCallback(
folder=args.checkpoint_dir,
load_from_folder=args.load_dir,
step_freq=args.checkpoint_save_freq)
callbacks.append(chpt_callback)
# assemble eval DAGs
for i, eval_dl in enumerate(data_layers_eval):
audio_signal_e, a_sig_length_e, transcript_e, transcript_len_e = \
eval_dl()
processed_signal_e, p_length_e = data_preprocessor(
input_signal=audio_signal_e, length=a_sig_length_e)
encoded_e, encoded_len_e = encoder(audio_signal=processed_signal_e,
length=p_length_e)
log_probs_e = decoder(encoder_output=encoded_e)
predictions_e = greedy_decoder(log_probs=log_probs_e)
loss_e = ctc_loss(log_probs=log_probs_e,
targets=transcript_e,
input_length=encoded_len_e,
target_length=transcript_len_e)
# create corresponding eval callback
tagname = os.path.basename(args.eval_datasets[i]).split(".")[0]
eval_callback = nemo.core.EvaluatorCallback(
eval_tensors=[
loss_e, predictions_e, transcript_e, transcript_len_e
],
user_iter_callback=partial(process_evaluation_batch, labels=vocab),
user_epochs_done_callback=partial(process_evaluation_epoch,
tag=tagname),
eval_step=args.eval_freq,
tb_writer=neural_factory.tb_writer)
callbacks.append(eval_callback)
return loss_t, callbacks, steps_per_epoch