def create_NMs(tacotron2_params, logger=None, decoder_infer=False):
data_preprocessor = nemo_asr.AudioPreprocessing(
**tacotron2_params["AudioPreprocessing"])
text_embedding = nemo_tts.TextEmbedding(
len(tacotron2_params["labels"]), **tacotron2_params["TextEmbedding"])
t2_enc = nemo_tts.Tacotron2Encoder(**tacotron2_params["Tacotron2Encoder"])
if decoder_infer:
t2_dec = nemo_tts.Tacotron2DecoderInfer(
**tacotron2_params["Tacotron2Decoder"])
else:
t2_dec = nemo_tts.Tacotron2Decoder(
**tacotron2_params["Tacotron2Decoder"])
t2_postnet = nemo_tts.Tacotron2Postnet(
**tacotron2_params["Tacotron2Postnet"])
t2_loss = nemo_tts.Tacotron2Loss()
makegatetarget = nemo_tts.MakeGate()
if logger:
total_weights = (text_embedding.num_weights + t2_enc.num_weights +
t2_dec.num_weights + t2_postnet.num_weights)
logger.info('================================')
logger.info(f"Total number of parameters: {total_weights}")
logger.info('================================')
return (data_preprocessor, text_embedding, t2_enc, t2_dec, t2_postnet,
t2_loss, makegatetarget)
def create_NMs(waveglow_params, logger=None):
data_preprocessor = nemo_asr.AudioPreprocessing(
**waveglow_params["AudioPreprocessing"])
waveglow = nemo_tts.WaveGlowNM(**waveglow_params["WaveGlowNM"])
waveglow_loss = nemo_tts.WaveGlowLoss()
if logger:
logger.info('================================')
logger.info(f"Total number of parameters: {waveglow.num_weights}")
logger.info('================================')
return (data_preprocessor, waveglow, waveglow_loss)
def main():
parser = argparse.ArgumentParser(description='Jasper')
parser.add_argument("--local_rank", default=None, type=int)
parser.add_argument("--batch_size", default=32, type=int)
parser.add_argument("--model_config", type=str, required=True)
parser.add_argument("--eval_datasets", type=str, required=True)
parser.add_argument("--load_dir", type=str, required=True)
parser.add_argument("--vocab_file", type=str, required=True)
parser.add_argument("--save_logprob", default=None, type=str)
parser.add_argument("--lm_path", default=None, type=str)
parser.add_argument("--beam_width", default=50, type=int)
parser.add_argument("--alpha", default=2.0, type=float)
parser.add_argument("--beta", default=1.0, type=float)
parser.add_argument("--cutoff_prob", default=0.99, type=float)
parser.add_argument("--cutoff_top_n", default=40, type=int)
args = parser.parse_args()
batch_size = args.batch_size
load_dir = args.load_dir
if args.local_rank is not None:
if args.lm_path:
raise NotImplementedError(
"Beam search decoder with LM does not currently support "
"evaluation on multi-gpu.")
device = nemo.core.DeviceType.AllGpu
else:
device = nemo.core.DeviceType.GPU
# Instantiate Neural Factory with supported backend
neural_factory = nemo.core.NeuralModuleFactory(
backend=nemo.core.Backend.PyTorch,
local_rank=args.local_rank,
optimization_level=nemo.core.Optimization.mxprO1,
placement=device)
logger = neural_factory.logger
if args.local_rank is not None:
logger.info('Doing ALL GPU')
yaml = YAML(typ="safe")
with open(args.model_config) as f:
jasper_params = yaml.load(f)
vocab = load_vocab(args.vocab_file)
sample_rate = jasper_params['sample_rate']
eval_datasets = args.eval_datasets
eval_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"])
eval_dl_params.update(jasper_params["AudioToTextDataLayer"]["eval"])
eval_dl_params["normalize_transcripts"] = False
del eval_dl_params["train"]
del eval_dl_params["eval"]
data_layer = nemo_asr.AudioToTextDataLayer(
manifest_filepath=eval_datasets,
sample_rate=sample_rate,
labels=vocab,
batch_size=batch_size,
**eval_dl_params)
n = len(data_layer)
logger.info('Evaluating {0} examples'.format(n))
data_preprocessor = nemo_asr.AudioPreprocessing(
sample_rate=sample_rate,
**jasper_params["AudioPreprocessing"])
jasper_encoder = nemo_asr.JasperEncoder(
feat_in=jasper_params["AudioPreprocessing"]["features"],
**jasper_params["JasperEncoder"])
jasper_decoder = nemo_asr.JasperDecoderForCTC(
feat_in=jasper_params["JasperEncoder"]["jasper"][-1]["filters"],
num_classes=len(vocab))
greedy_decoder = nemo_asr.GreedyCTCDecoder()
if args.lm_path:
beam_width = args.beam_width
alpha = args.alpha
beta = args.beta
cutoff_prob = args.cutoff_prob
cutoff_top_n = args.cutoff_top_n
beam_search_with_lm = nemo_asr.BeamSearchDecoderWithLM(
vocab=vocab,
beam_width=beam_width,
alpha=alpha,
beta=beta,
cutoff_prob=cutoff_prob,
cutoff_top_n=cutoff_top_n,
lm_path=args.lm_path,
num_cpus=max(os.cpu_count(), 1))
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('================================')
audio_signal_e1, a_sig_length_e1, transcript_e1, transcript_len_e1 = \
data_layer()
processed_signal_e1, p_length_e1 = data_preprocessor(
input_signal=audio_signal_e1,
length=a_sig_length_e1)
encoded_e1, encoded_len_e1 = jasper_encoder(
audio_signal=processed_signal_e1,
length=p_length_e1)
log_probs_e1 = jasper_decoder(encoder_output=encoded_e1)
predictions_e1 = greedy_decoder(log_probs=log_probs_e1)
eval_tensors = [log_probs_e1, predictions_e1,
transcript_e1, transcript_len_e1, encoded_len_e1]
if args.lm_path:
beam_predictions_e1 = beam_search_with_lm(
log_probs=log_probs_e1, log_probs_length=encoded_len_e1)
eval_tensors.append(beam_predictions_e1)
evaluated_tensors = neural_factory.infer(
tensors=eval_tensors,
checkpoint_dir=load_dir,
)
greedy_hypotheses = post_process_predictions(evaluated_tensors[1], vocab)
references = post_process_transcripts(
evaluated_tensors[2], evaluated_tensors[3], vocab)
cer = word_error_rate(hypotheses=greedy_hypotheses,
references=references,
use_cer=True)
logger.info("Greedy CER {:.2f}%".format(cer * 100))
if args.lm_path:
beam_hypotheses = []
# Over mini-batch
for i in evaluated_tensors[-1]:
# Over samples
for j in i:
beam_hypotheses.append(j[0][1])
cer = word_error_rate(
hypotheses=beam_hypotheses, references=references, use_cer=True)
logger.info("Beam CER {:.2f}".format(cer * 100))
if args.save_logprob:
# Convert logits to list of numpy arrays
logprob = []
for i, batch in enumerate(evaluated_tensors[0]):
for j in range(batch.shape[0]):
logprob.append(
batch[j][:evaluated_tensors[4][i][j], :].cpu().numpy())
with open(args.save_logprob, 'wb') as f:
pickle.dump(logprob, f, protocol=pickle.HIGHEST_PROTOCOL)
def main():
parser = argparse.ArgumentParser(parents=[nm_argparse.NemoArgParser()],
description='AN4 ASR',
conflict_handler='resolve')
# Overwrite default args
parser.add_argument("--train_dataset",
type=str,
help="training dataset path")
parser.add_argument("--eval_datasets",
type=str,
nargs=1,
help="validation dataset path")
# Create new args
parser.add_argument("--lm", default="./an4-lm.3gram.binary", type=str)
parser.add_argument("--test_after_training", action='store_true')
parser.add_argument("--momentum", type=float)
parser.add_argument("--beta1", default=0.95, type=float)
parser.add_argument("--beta2", default=0.25, type=float)
parser.set_defaults(
model_config="./configs/jasper_an4.yaml",
train_dataset="/home/mrjenkins/TestData/an4_dataset/an4_train.json",
eval_datasets="/home/mrjenkins/TestData/an4_dataset/an4_val.json",
work_dir="./tmp",
checkpoint_dir="./tmp",
optimizer="novograd",
num_epochs=50,
batch_size=32,
eval_batch_size=16,
lr=0.02,
weight_decay=0.005,
checkpoint_save_freq=1000,
eval_freq=100,
amp_opt_level="O1")
args = parser.parse_args()
betas = (args.beta1, args.beta2)
wer_thr = 0.20
beam_wer_thr = 0.15
nf = nemo.core.NeuralModuleFactory(local_rank=args.local_rank,
optimization_level=args.amp_opt_level,
random_seed=0,
log_dir=args.work_dir,
checkpoint_dir=args.checkpoint_dir,
create_tb_writer=True,
cudnn_benchmark=args.cudnn_benchmark)
tb_writer = nf.tb_writer
checkpoint_dir = nf.checkpoint_dir
args.checkpoint_dir = nf.checkpoint_dir
# Load model definition
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']
# build train and eval model
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"]
data_layer = nemo_asr.AudioToTextDataLayer(
manifest_filepath=args.train_dataset,
sample_rate=sample_rate,
labels=vocab,
batch_size=args.batch_size,
**train_dl_params)
num_samples = len(data_layer)
total_steps = int(num_samples * args.num_epochs / args.batch_size)
print("Train samples=", num_samples, "num_steps=", total_steps)
data_preprocessor = nemo_asr.AudioPreprocessing(
sample_rate=sample_rate, **jasper_params["AudioPreprocessing"])
# data_augmentation = nemo_asr.SpectrogramAugmentation(
# **jasper_params['SpectrogramAugmentation']
# )
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_layer_eval = nemo_asr.AudioToTextDataLayer(
manifest_filepath=args.eval_datasets,
sample_rate=sample_rate,
labels=vocab,
batch_size=args.eval_batch_size,
**eval_dl_params)
num_samples = len(data_layer_eval)
nf.logger.info(f"Eval samples={num_samples}")
jasper_encoder = nemo_asr.JasperEncoder(
feat_in=jasper_params["AudioPreprocessing"]["features"],
**jasper_params["JasperEncoder"])
jasper_decoder = nemo_asr.JasperDecoderForCTC(
feat_in=jasper_params["JasperEncoder"]["jasper"][-1]["filters"],
num_classes=len(vocab))
ctc_loss = nemo_asr.CTCLossNM(num_classes=len(vocab))
greedy_decoder = nemo_asr.GreedyCTCDecoder()
# Training model
audio, audio_len, transcript, transcript_len = data_layer()
processed, processed_len = data_preprocessor(input_signal=audio,
length=audio_len)
encoded, encoded_len = jasper_encoder(audio_signal=processed,
length=processed_len)
log_probs = jasper_decoder(encoder_output=encoded)
predictions = greedy_decoder(log_probs=log_probs)
loss = ctc_loss(log_probs=log_probs,
targets=transcript,
input_length=encoded_len,
target_length=transcript_len)
# Evaluation model
audio_e, audio_len_e, transcript_e, transcript_len_e = data_layer_eval()
processed_e, processed_len_e = data_preprocessor(input_signal=audio_e,
length=audio_len_e)
encoded_e, encoded_len_e = jasper_encoder(audio_signal=processed_e,
length=processed_len_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)
nf.logger.info("Num of params in encoder: {0}".format(
jasper_encoder.num_weights))
# Callbacks to print info to console and Tensorboard
train_callback = nemo.core.SimpleLossLoggerCallback(
tensors=[loss, predictions, transcript, transcript_len],
print_func=lambda x: monitor_asr_train_progress(x, labels=vocab),
get_tb_values=lambda x: [["loss", x[0]]],
tb_writer=tb_writer,
)
checkpointer_callback = nemo.core.CheckpointCallback(
folder=checkpoint_dir, step_freq=args.checkpoint_save_freq)
eval_tensors = [loss_e, predictions_e, transcript_e, transcript_len_e]
eval_callback = nemo.core.EvaluatorCallback(
eval_tensors=eval_tensors,
user_iter_callback=lambda x, y: process_evaluation_batch(
x, y, labels=vocab),
user_epochs_done_callback=process_evaluation_epoch,
eval_step=args.eval_freq,
tb_writer=tb_writer)
nf.train(tensors_to_optimize=[loss],
callbacks=[train_callback, eval_callback, checkpointer_callback],
optimizer=args.optimizer,
lr_policy=CosineAnnealing(total_steps=total_steps),
optimization_params={
"num_epochs": args.num_epochs,
"max_steps": args.max_steps,
"lr": args.lr,
"momentum": args.momentum,
"betas": betas,
"weight_decay": args.weight_decay,
"grad_norm_clip": None
},
batches_per_step=args.iter_per_step)
if args.test_after_training:
# Create BeamSearch NM
beam_search_with_lm = nemo_asr.BeamSearchDecoderWithLM(
vocab=vocab,
beam_width=64,
alpha=2.,
beta=1.5,
lm_path=args.lm,
num_cpus=max(os.cpu_count(), 1))
beam_predictions = beam_search_with_lm(log_probs=log_probs_e,
log_probs_length=encoded_len_e)
eval_tensors.append(beam_predictions)
evaluated_tensors = nf.infer(eval_tensors)
greedy_hypotheses = post_process_predictions(evaluated_tensors[1],
vocab)
references = post_process_transcripts(evaluated_tensors[2],
evaluated_tensors[3], vocab)
wer = word_error_rate(hypotheses=greedy_hypotheses,
references=references)
nf.logger.info("Greedy WER: {:.2f}".format(wer * 100))
assert wer <= wer_thr, (
"Final eval greedy WER {:.2f}% > than {:.2f}%".format(
wer * 100, wer_thr * 100))
beam_hypotheses = []
# Over mini-batch
for i in evaluated_tensors[-1]:
# Over samples
for j in i:
beam_hypotheses.append(j[0][1])
beam_wer = word_error_rate(hypotheses=beam_hypotheses,
references=references)
nf.logger.info("Beam WER {:.2f}%".format(beam_wer * 100))
assert beam_wer <= beam_wer_thr, (
"Final eval beam WER {:.2f}% > than {:.2f}%".format(
beam_wer * 100, beam_wer_thr * 100))
assert beam_wer <= wer, ("Final eval beam WER > than the greedy WER.")
# Reload model weights and train for extra 10 epochs
checkpointer_callback = nemo.core.CheckpointCallback(
folder=checkpoint_dir,
step_freq=args.checkpoint_save_freq,
force_load=True)
nf.reset_trainer()
nf.train(tensors_to_optimize=[loss],
callbacks=[train_callback, checkpointer_callback],
optimizer=args.optimizer,
optimization_params={
"num_epochs": args.num_epochs + 10,
"lr": args.lr,
"momentum": args.momentum,
"betas": betas,
"weight_decay": args.weight_decay,
"grad_norm_clip": None
},
reset=True)
evaluated_tensors = nf.infer(eval_tensors[:-1])
greedy_hypotheses = post_process_predictions(evaluated_tensors[1],
vocab)
references = post_process_transcripts(evaluated_tensors[2],
evaluated_tensors[3], vocab)
wer_new = word_error_rate(hypotheses=greedy_hypotheses,
references=references)
nf.logger.info("New greedy WER: {:.2f}%".format(wer_new * 100))
assert wer_new <= wer * 1.1, (
f"Fine tuning: new WER {wer * 100:.2f}% > than the previous WER "
f"{wer_new * 100:.2f}%")
def main():
parser = argparse.ArgumentParser(description='Jasper')
parser.add_argument("--local_rank", default=None, type=int)
parser.add_argument("--batch_size", default=64, type=int)
parser.add_argument("--model_config", type=str, required=True)
parser.add_argument("--eval_datasets", type=str, required=True)
parser.add_argument("--load_dir", type=str, required=True)
parser.add_argument("--save_logprob", default=None, type=str)
parser.add_argument("--lm_path", default=None, type=str)
parser.add_argument('--alpha',
default=2.,
type=float,
help='value of LM weight',
required=False)
parser.add_argument(
'--alpha_max',
type=float,
help='maximum value of LM weight (for a grid search in \'eval\' mode)',
required=False)
parser.add_argument('--alpha_step',
type=float,
help='step for LM weight\'s tuning in \'eval\' mode',
required=False,
default=0.1)
parser.add_argument('--beta',
default=1.5,
type=float,
help='value of word count weight',
required=False)
parser.add_argument(
'--beta_max',
type=float,
help='maximum value of word count weight (for a grid search in \
\'eval\' mode',
required=False)
parser.add_argument(
'--beta_step',
type=float,
help='step for word count weight\'s tuning in \'eval\' mode',
required=False,
default=0.1)
parser.add_argument("--beam_width", default=128, type=int)
args = parser.parse_args()
batch_size = args.batch_size
load_dir = args.load_dir
if args.local_rank is not None:
if args.lm_path:
raise NotImplementedError(
"Beam search decoder with LM does not currently support "
"evaluation on multi-gpu.")
device = nemo.core.DeviceType.AllGpu
else:
device = nemo.core.DeviceType.GPU
# Instantiate Neural Factory with supported backend
neural_factory = nemo.core.NeuralModuleFactory(
backend=nemo.core.Backend.PyTorch,
local_rank=args.local_rank,
optimization_level=nemo.core.Optimization.mxprO1,
placement=device)
logger = neural_factory.logger
if args.local_rank is not None:
logger.info('Doing ALL GPU')
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']
eval_datasets = args.eval_datasets
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_layer = nemo_asr.AudioToTextDataLayer(manifest_filepath=eval_datasets,
sample_rate=sample_rate,
labels=vocab,
batch_size=batch_size,
**eval_dl_params)
N = len(data_layer)
logger.info('Evaluating {0} examples'.format(N))
data_preprocessor = nemo_asr.AudioPreprocessing(
sample_rate=sample_rate, **jasper_params["AudioPreprocessing"])
jasper_encoder = nemo_asr.JasperEncoder(
feat_in=jasper_params["AudioPreprocessing"]["features"],
**jasper_params["JasperEncoder"])
jasper_decoder = nemo_asr.JasperDecoderForCTC(
feat_in=jasper_params["JasperEncoder"]["jasper"][-1]["filters"],
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('================================')
audio_signal_e1, a_sig_length_e1, transcript_e1, transcript_len_e1 =\
data_layer()
processed_signal_e1, p_length_e1 = data_preprocessor(
input_signal=audio_signal_e1, length=a_sig_length_e1)
encoded_e1, encoded_len_e1 = jasper_encoder(
audio_signal=processed_signal_e1, length=p_length_e1)
log_probs_e1 = jasper_decoder(encoder_output=encoded_e1)
predictions_e1 = greedy_decoder(log_probs=log_probs_e1)
eval_tensors = [
log_probs_e1, predictions_e1, transcript_e1, transcript_len_e1,
encoded_len_e1
]
evaluated_tensors = neural_factory.infer(tensors=eval_tensors,
checkpoint_dir=load_dir,
cache=True)
greedy_hypotheses = post_process_predictions(evaluated_tensors[1], vocab)
references = post_process_transcripts(evaluated_tensors[2],
evaluated_tensors[3], vocab)
wer = word_error_rate(hypotheses=greedy_hypotheses, references=references)
logger.info("Greedy WER {:.2f}%".format(wer * 100))
if args.lm_path:
if args.alpha_max is None:
args.alpha_max = args.alpha
# include alpha_max in tuning range
args.alpha_max += args.alpha_step / 10.0
if args.beta_max is None:
args.beta_max = args.beta
# include beta_max in tuning range
args.beta_max += args.beta_step / 10.0
beam_wers = []
for alpha in np.arange(args.alpha, args.alpha_max, args.alpha_step):
for beta in np.arange(args.beta, args.beta_max, args.beta_step):
logger.info('================================')
logger.info(f'Infering with (alpha, beta): ({alpha}, {beta})')
beam_search_with_lm = nemo_asr.BeamSearchDecoderWithLM(
vocab=vocab,
beam_width=args.beam_width,
alpha=alpha,
beta=beta,
lm_path=args.lm_path,
num_cpus=max(os.cpu_count(), 1))
beam_predictions_e1 = beam_search_with_lm(
log_probs=log_probs_e1, log_probs_length=encoded_len_e1)
evaluated_tensors = neural_factory.infer(
tensors=[beam_predictions_e1],
use_cache=True,
verbose=False)
beam_hypotheses = []
# Over mini-batch
for i in evaluated_tensors[-1]:
# Over samples
for j in i:
beam_hypotheses.append(j[0][1])
wer = word_error_rate(hypotheses=beam_hypotheses,
references=references)
logger.info("Beam WER {:.2f}%".format(wer * 100))
beam_wers.append(((alpha, beta), wer * 100))
logger.info('Beam WER for (alpha, beta)')
logger.info('================================')
logger.info('\n' + '\n'.join([str(e) for e in beam_wers]))
logger.info('================================')
best_beam_wer = min(beam_wers, key=lambda x: x[1])
logger.info('Best (alpha, beta): '
f'{best_beam_wer[0]}, '
f'WER: {best_beam_wer[1]:.2f}%')
# Set this to True to enable beam search decoder
ENABLE_NGRAM = False
# This is only necessary if ENABLE_NGRAM = True. Otherwise, set to empty string
LM_PATH = "<PATH_TO_KENLM_BINARY>"
# Read model YAML
yaml = YAML(typ="safe")
with open(MODEL_YAML) as f:
jasper_model_definition = yaml.load(f)
labels = jasper_model_definition['labels']
# Instantiate necessary Neural Modules
# Note that data layer is missing from here
neural_factory = nemo.core.NeuralModuleFactory(
placement=nemo.core.DeviceType.GPU, backend=nemo.core.Backend.PyTorch)
data_preprocessor = nemo_asr.AudioPreprocessing(factory=neural_factory)
jasper_encoder = nemo_asr.JasperEncoder(
jasper=jasper_model_definition['JasperEncoder']['jasper'],
activation=jasper_model_definition['JasperEncoder']['activation'],
feat_in=jasper_model_definition['AudioPreprocessing']['features'])
jasper_encoder.restore_from(CHECKPOINT_ENCODER, local_rank=0)
jasper_decoder = nemo_asr.JasperDecoderForCTC(feat_in=1024,
num_classes=len(labels))
jasper_decoder.restore_from(CHECKPOINT_DECODER, local_rank=0)
greedy_decoder = nemo_asr.GreedyCTCDecoder()
if ENABLE_NGRAM and os.path.isfile(LM_PATH):
beam_search_with_lm = nemo_asr.BeamSearchDecoderWithLM(vocab=labels,
beam_width=64,
alpha=2.0,
beta=1.0,
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.num_gpus))
logger.info('Have {0} examples to train on.'.format(N))
data_preprocessor = nemo_asr.AudioPreprocessing(
sample_rate=sample_rate, **jasper_params["AudioPreprocessing"])
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["AudioPreprocessing"]["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_dag(args, cfg, logger, num_gpus):
# Defining nodes
data = nemo_asr.AudioToTextDataLayer(
manifest_filepath=args.train_dataset,
labels=cfg['target']['labels'],
batch_size=cfg['optimization']['batch_size'],
eos_id=cfg['target']['eos_id'],
**cfg['AudioToTextDataLayer']['train']
)
data_evals = []
if args.eval_datasets:
for val_path in args.eval_datasets:
data_evals.append(nemo_asr.AudioToTextDataLayer(
manifest_filepath=val_path,
labels=cfg['target']['labels'],
batch_size=cfg['inference']['batch_size'],
eos_id=cfg['target']['eos_id'],
**cfg['AudioToTextDataLayer']['eval']
))
else:
logger.info("There were no val datasets passed")
data_preprocessor = nemo_asr.AudioPreprocessing(
**cfg['AudioPreprocessing']
)
data_augmentation = nemo_asr.SpectrogramAugmentation(
**cfg['SpectrogramAugmentation']
)
encoder = nemo_asr.JasperEncoder(
feat_in=cfg["AudioPreprocessing"]["features"],
**cfg['JasperEncoder']
)
if args.encoder_checkpoint is not None \
and os.path.exists(args.encoder_checkpoint):
if cfg['JasperEncoder']['load']:
encoder.restore_from(args.encoder_checkpoint, args.local_rank)
logger.info(f'Loaded weights for encoder'
f' from {args.encoder_checkpoint}')
if cfg['JasperEncoder']['freeze']:
encoder.freeze()
logger.info(f'Freeze encoder weights')
connector = nemo_asr.JasperRNNConnector(
in_channels=cfg['JasperEncoder']['jasper'][-1]['filters'],
out_channels=cfg['DecoderRNN']['hidden_size']
)
decoder = nemo.backends.pytorch.DecoderRNN(
voc_size=len(cfg['target']['labels']),
bos_id=cfg['target']['bos_id'],
**cfg['DecoderRNN']
)
if args.decoder_checkpoint is not None \
and os.path.exists(args.decoder_checkpoint):
if cfg['DecoderRNN']['load']:
decoder.restore_from(args.decoder_checkpoint, args.local_rank)
logger.info(f'Loaded weights for decoder'
f' from {args.decoder_checkpoint}')
if cfg['DecoderRNN']['freeze']:
decoder.freeze()
logger.info(f'Freeze decoder weights')
if cfg['decoder']['unfreeze_attn']:
for name, param in decoder.attention.named_parameters():
param.requires_grad = True
logger.info(f'Unfreeze decoder attn weights')
num_data = len(data)
batch_size = cfg['optimization']['batch_size']
num_epochs = cfg['optimization']['params']['num_epochs']
steps_per_epoch = int(num_data / (batch_size * num_gpus))
total_steps = num_epochs * steps_per_epoch
vsc = ValueSetterCallback
tf_callback = ValueSetterCallback(
decoder, 'teacher_forcing',
policies=[
vsc.Policy(vsc.Method.Const(1.0), start=0.0, end=1.0)
],
total_steps=total_steps
)
seq_loss = nemo.backends.pytorch.SequenceLoss(
pad_id=cfg['target']['pad_id'],
smoothing_coef=cfg['optimization']['smoothing_coef'],
sample_wise=cfg['optimization']['sample_wise']
)
se_callback = ValueSetterCallback(
seq_loss, 'smoothing_coef',
policies=[
vsc.Policy(
vsc.Method.Const(seq_loss.smoothing_coef),
start=0.0, end=1.0
),
],
total_steps=total_steps
)
beam_search = nemo.backends.pytorch.BeamSearch(
decoder=decoder,
pad_id=cfg['target']['pad_id'],
bos_id=cfg['target']['bos_id'],
eos_id=cfg['target']['eos_id'],
max_len=cfg['target']['max_len'],
beam_size=cfg['inference']['beam_size']
)
uf_callback = UnfreezeCallback(
[encoder, decoder],
start_epoch=cfg['optimization']['start_unfreeze']
)
saver_callback = nemo.core.ModuleSaverCallback(
save_modules_list=[encoder, connector, decoder],
folder=args.checkpoint_dir,
step_freq=args.eval_freq
)
# Creating DAG
audios, audio_lens, transcripts, _ = data()
processed_audios, processed_audio_lens = data_preprocessor(
input_signal=audios,
length=audio_lens
)
augmented_spec = data_augmentation(input_spec=processed_audios)
encoded, _ = encoder(
audio_signal=augmented_spec,
length=processed_audio_lens
)
encoded = connector(tensor=encoded)
log_probs, _ = decoder(
targets=transcripts,
encoder_outputs=encoded
)
train_loss = seq_loss(
log_probs=log_probs,
targets=transcripts
)
evals = []
for i, data_eval in enumerate(data_evals):
audios, audio_lens, transcripts, _ = data_eval()
processed_audios, processed_audio_lens = data_preprocessor(
input_signal=audios,
length=audio_lens
)
encoded, _ = encoder(
audio_signal=processed_audios,
length=processed_audio_lens
)
encoded = connector(tensor=encoded)
log_probs, _ = decoder(
targets=transcripts,
encoder_outputs=encoded
)
loss = seq_loss(
log_probs=log_probs,
targets=transcripts
)
predictions, aw = beam_search(encoder_outputs=encoded)
evals.append((args.eval_datasets[i],
(loss, log_probs, transcripts, predictions, aw)))
# Update config
cfg['num_params'] = {
'encoder': encoder.num_weights,
'connector': connector.num_weights,
'decoder': decoder.num_weights
}
cfg['num_params']['total'] = sum(cfg['num_params'].values())
cfg['input']['train'] = {'num_data': num_data}
cfg['optimization']['steps_per_epoch'] = steps_per_epoch
cfg['optimization']['total_steps'] = total_steps
return (train_loss, evals), cfg, [tf_callback, se_callback,
uf_callback, saver_callback]
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.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:
neural_factory.logger.info("There were no val datasets passed")
# create shared modules
data_preprocessor = nemo_asr.AudioPreprocessing(
sample_rate=sample_rate,
**quartz_params["AudioPreprocessing"])
# (QuartzNet uses the Jasper baseline encoder and decoder)
encoder = nemo_asr.JasperEncoder(
feat_in=quartz_params["AudioPreprocessing"]["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,
logger=neural_factory.logger),
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,
logger=neural_factory.logger),
eval_step=args.eval_freq,
tb_writer=neural_factory.tb_writer)
callbacks.append(eval_callback)
return loss_t, callbacks, steps_per_epoch