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train.py
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train.py
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import os
import sys
import torch
import logging
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from mpd_eval_v3 import MpdStats
import librosa
import json
logger = logging.getLogger(__name__)
def make_attn_mask(wavs, wav_lens):
"""
wav_lens: relative lengths(i.e. 0-1) of a batch. shape: (bs, )
return a tensor of shape (bs, seq_len), representing mask on allowed positions.
1 for regular tokens, 0 for padded tokens
"""
abs_lens = (wav_lens*wavs.shape[1]).long()
attn_mask = wavs.new(wavs.shape).zero_().long()
for i in range(len(abs_lens)):
attn_mask[i, :abs_lens[i]] = 1
return attn_mask
# Define training procedure
class ASR(sb.Brain):
def compute_forward(self, batch, stage):
"Given an input batch it computes the phoneme probabilities."
batch = batch.to(self.device)
wavs, wav_lens = batch.sig
# phns_bos, _ = batch.phn_encoded_bos
if stage == sb.Stage.TRAIN:
if hasattr(self.hparams, "augmentation"):
wavs = self.hparams.augmentation(wavs, wav_lens)
# some wav2vec models (e.g. large-lv60) needs attention_mask
if self.modules.wav2vec2.feature_extractor.return_attention_mask:
attn_mask = make_attn_mask(wavs, wav_lens)
else:
attn_mask = None
feats = self.modules.wav2vec2(wavs, attention_mask=attn_mask)
x = self.modules.enc(feats)
# output layer for ctc log-probabilities
logits = self.modules.ctc_lin(x)
p_ctc = self.hparams.log_softmax(logits)
return p_ctc, wav_lens
def compute_objectives(self, predictions, batch, stage):
"Given the network predictions and targets computed the NLL loss."
p_ctc, wav_lens = predictions
ids = batch.id
targets, target_lens = batch.phn_encoded_target
if stage != sb.Stage.TRAIN:
canonicals, canonical_lens = batch.phn_encoded_canonical
perceiveds, perceived_lens = batch.phn_encoded_perceived
loss_ctc = self.hparams.ctc_cost(p_ctc, targets, wav_lens, target_lens)
loss = loss_ctc
# Record losses for posterity
if stage != sb.Stage.TRAIN:
# Note: sb.decoders.ctc_greedy_decode will also remove padded tokens
# that is, it return a list of list with different lengths
sequence = sb.decoders.ctc_greedy_decode(
p_ctc, wav_lens, blank_id=self.hparams.blank_index
)
self.ctc_metrics.append(ids, p_ctc, targets, wav_lens, target_lens)
self.per_metrics.append(
ids=ids,
predict=sequence,
target=targets,
predict_len=None,
target_len=target_lens,
ind2lab=self.label_encoder.decode_ndim,
)
self.mpd_metrics.append(
ids=ids,
predict=sequence,
canonical=canonicals,
perceived=perceiveds,
predict_len=None,
canonical_len=canonical_lens,
perceived_len=perceived_lens,
ind2lab=self.label_encoder.decode_ndim,
)
return loss
def evaluate_batch(self, batch, stage):
"""Computations needed for validation/test batches"""
predictions = self.compute_forward(batch, stage=stage)
loss = self.compute_objectives(predictions, batch, stage=stage)
return loss.detach()
def on_stage_start(self, stage, epoch):
"Gets called when a stage (either training, validation, test) starts."
self.ctc_metrics = self.hparams.ctc_stats()
if self.hparams.wav2vec2_specaug:
self.modules.wav2vec2.model.config.apply_spec_augment = True
if stage != sb.Stage.TRAIN:
self.modules.wav2vec2.model.config.apply_spec_augment = False
self.per_metrics = self.hparams.per_stats()
self.mpd_metrics = MpdStats()
def on_stage_end(self, stage, stage_loss, epoch):
"""Gets called at the end of a epoch."""
if stage == sb.Stage.TRAIN:
self.train_loss = stage_loss
else:
per = self.per_metrics.summarize("error_rate")
mpd_f1 = self.mpd_metrics.summarize("mpd_f1")
if stage == sb.Stage.VALID:
self.hparams.train_logger.log_stats(
stats_meta={
"epoch": epoch,
"lr_adam": self.adam_optimizer.param_groups[0]["lr"],
"lr_wav2vec": self.wav2vec_optimizer.param_groups[0]["lr"],
},
train_stats={"loss": self.train_loss},
valid_stats={
"loss": stage_loss,
"ctc_loss": self.ctc_metrics.summarize("average"),
"PER": per,
"mpd_f1": mpd_f1
},
)
self.checkpointer.save_and_keep_only(
meta={"PER": per, "mpd_f1": mpd_f1}, min_keys=["PER"], max_keys=["mpd_f1"]
)
if stage == sb.Stage.TEST:
self.hparams.train_logger.log_stats(
stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
test_stats={"loss": stage_loss, "PER": per, "mpd_f1": mpd_f1},
)
with open(self.hparams.wer_file, "w") as w:
w.write("CTC loss stats:\n")
self.ctc_metrics.write_stats(w)
w.write("\nPER stats:\n")
self.per_metrics.write_stats(w)
print(
"CTC and PER stats written to file",
self.hparams.wer_file,
)
with open(self.hparams.mpd_file, "w") as m:
m.write("MPD results and stats:\n")
self.mpd_metrics.write_stats(m)
print(
"MPD results and stats written to file",
self.hparams.mpd_file,
)
def fit_batch(self, batch):
"""Fit one batch, override to do multiple updates.
The default implementation depends on a few methods being defined
with a particular behavior:
* ``compute_forward()``
* ``compute_objectives()``
Also depends on having optimizers passed at initialization.
Arguments
---------
batch : list of torch.Tensors
Batch of data to use for training. Default implementation assumes
this batch has two elements: inputs and targets.
Returns
-------
detached loss
"""
# Managing automatic mixed precision
if self.auto_mix_prec:
self.wav2vec_optimizer.zero_grad()
self.adam_optimizer.zero_grad()
with torch.cuda.amp.autocast():
outputs = self.compute_forward(batch, sb.Stage.TRAIN)
loss = self.compute_objectives(outputs, batch, sb.Stage.TRAIN)
self.scaler.scale(loss).backward()
self.scaler.unscale_(self.wav2vec_optimizer)
self.scaler.unscale_(self.adam_optimizer)
if self.check_gradients(loss):
self.scaler.step(self.wav2vec_optimizer)
self.scaler.step(self.adam_optimizer)
self.scaler.update()
else:
outputs = self.compute_forward(batch, sb.Stage.TRAIN)
loss = self.compute_objectives(outputs, batch, sb.Stage.TRAIN)
# normalize the loss by gradient_accumulation step
(loss / self.hparams.gradient_accumulation).backward()
if self.step % self.hparams.gradient_accumulation == 0:
# gradient clipping & early stop if loss is not fini
if self.check_gradients(loss):
self.wav2vec_optimizer.step()
self.adam_optimizer.step()
self.wav2vec_optimizer.zero_grad()
self.adam_optimizer.zero_grad()
return loss.detach().cpu()
def init_optimizers(self):
"Initializes the wav2vec2 optimizer and model optimizer"
self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
self.modules.wav2vec2.model.parameters()
)
self.adam_optimizer = self.hparams.adam_opt_class(
self.hparams.model.parameters()
)
if self.checkpointer is not None:
self.checkpointer.add_recoverable(
"wav2vec_opt", self.wav2vec_optimizer
)
self.checkpointer.add_recoverable("adam_opt", self.adam_optimizer)
def on_fit_start(self):
"""Gets called at the beginning of ``fit()``, on multiple processes
if ``distributed_count > 0`` and backend is ddp.
Default implementation compiles the jit modules, initializes
optimizers, and loads the latest checkpoint to resume training.
"""
# Run this *after* starting all processes since jit modules cannot be
# pickled.
self._compile_jit()
# Wrap modules with parallel backend after jit
self._wrap_distributed()
# Initialize optimizers after parameters are configured
self.init_optimizers()
# Load latest checkpoint to resume training if interrupted
## NOTE: make sure to use the "best" model to continual training
## so we set the `min_key` argument
if self.checkpointer is not None:
self.checkpointer.recover_if_possible(
device=torch.device(self.device),
min_key="PER"
)
def dataio_prep(hparams):
"""This function prepares the datasets to be used in the brain class.
It also defines the data processing pipeline through user-defined functions."""
data_folder = hparams["data_folder_save"]
# 1. Declarations:
train_data = sb.dataio.dataset.DynamicItemDataset.from_json(
json_path=hparams["train_annotation"],
replacements={"data_root": data_folder},
)
if hparams["sorting"] == "ascending":
# we sort training data to speed up training and get better results.
train_data = train_data.filtered_sorted(sort_key="duration")
# when sorting do not shuffle in dataloader ! otherwise is pointless
hparams["train_dataloader_opts"]["shuffle"] = False
elif hparams["sorting"] == "descending":
train_data = train_data.filtered_sorted(
sort_key="duration", reverse=True
)
# when sorting do not shuffle in dataloader ! otherwise is pointless
hparams["train_dataloader_opts"]["shuffle"] = False
elif hparams["sorting"] == "random":
pass
else:
raise NotImplementedError(
"sorting must be random, ascending or descending"
)
valid_data = sb.dataio.dataset.DynamicItemDataset.from_json(
json_path=hparams["valid_annotation"],
replacements={"data_root": data_folder},
)
valid_data = valid_data.filtered_sorted(sort_key="duration")
test_data = sb.dataio.dataset.DynamicItemDataset.from_json(
json_path=hparams["test_annotation"],
replacements={"data_root": data_folder},
)
test_data = test_data.filtered_sorted(sort_key="duration")
datasets = [train_data, valid_data, test_data]
label_encoder = sb.dataio.encoder.CTCTextEncoder()
# 2. Define audio pipeline:
@sb.utils.data_pipeline.takes("wav")
@sb.utils.data_pipeline.provides("sig")
def audio_pipeline(wav):
# sig = sb.dataio.dataio.read_audio(wav)
# # sample rate change to 16000, e,g, using librosa
# sig = torch.Tensor(librosa.core.load(wav, hparams["sample_rate"])[0])
# Use wav2vec processor to do normalization
sig = hparams["wav2vec2"].feature_extractor(
librosa.core.load(wav, hparams["sample_rate"])[0],
sampling_rate=hparams["sample_rate"],
).input_values[0]
sig = torch.Tensor(sig)
return sig
sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
# 3. Define text pipeline:
@sb.utils.data_pipeline.takes("perceived_train_target")
@sb.utils.data_pipeline.provides(
"phn_list_target",
"phn_encoded_list_target",
"phn_encoded_target",
)
def text_pipeline_train(phn):
phn_list = phn.strip().split()
yield phn_list
phn_encoded_list = label_encoder.encode_sequence(phn_list)
yield phn_encoded_list
phn_encoded = torch.LongTensor(phn_encoded_list)
yield phn_encoded
@sb.utils.data_pipeline.takes("perceived_train_target", "canonical_aligned", "perceived_aligned")
@sb.utils.data_pipeline.provides(
"phn_list_target",
"phn_encoded_list_target",
"phn_encoded_target",
"phn_list_canonical",
"phn_encoded_list_canonical",
"phn_encoded_canonical",
"phn_list_perceived",
"phn_encoded_list_perceived",
"phn_encoded_perceived",
)
def text_pipeline_test(target, canonical, perceived):
phn_list_target = target.strip().split()
yield phn_list_target
phn_encoded_list_target = label_encoder.encode_sequence(phn_list_target)
yield phn_encoded_list_target
phn_encoded_target = torch.LongTensor(phn_encoded_list_target)
yield phn_encoded_target
phn_list_canonical = canonical.strip().split()
yield phn_list_canonical
phn_encoded_list_canonical = label_encoder.encode_sequence(phn_list_canonical)
yield phn_encoded_list_canonical
phn_encoded_canonical = torch.LongTensor(phn_encoded_list_canonical)
yield phn_encoded_canonical
phn_list_perceived = perceived.strip().split()
yield phn_list_perceived
phn_encoded_list_perceived = label_encoder.encode_sequence(phn_list_perceived)
yield phn_encoded_list_perceived
phn_encoded_perceived = torch.LongTensor(phn_encoded_list_perceived)
yield phn_encoded_perceived
sb.dataio.dataset.add_dynamic_item([train_data], text_pipeline_train)
sb.dataio.dataset.add_dynamic_item([valid_data, test_data], text_pipeline_test)
# 3. Fit encoder:
# Load or compute the label encoder
lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
special_labels = {
"blank_label": hparams["blank_index"],
}
label_encoder.load_or_create(
path=lab_enc_file,
from_didatasets=[train_data],
output_key="phn_list_target",
special_labels=special_labels,
sequence_input=True,
)
# 4. Set output:
sb.dataio.dataset.set_output_keys(
[train_data],
["id", "sig", "phn_encoded_target"],
)
sb.dataio.dataset.set_output_keys(
[valid_data, test_data],
["id", "sig", "phn_encoded_target", "phn_encoded_canonical", "phn_encoded_perceived"],
)
return train_data, valid_data, test_data, label_encoder
if __name__ == "__main__":
# CLI:
hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
# Load hyperparameters file with command-line overrides
with open(hparams_file) as fin:
hparams = load_hyperpyyaml(fin, overrides)
# Initialize ddp (useful only for multi-GPU DDP training)
sb.utils.distributed.ddp_init_group(run_opts)
# Create experiment directory
sb.create_experiment_directory(
experiment_directory=hparams["output_folder"],
hyperparams_to_save=hparams_file,
overrides=overrides,
)
# Dataset IO prep: creating Dataset objects and proper encodings for phones
train_data, valid_data, test_data, label_encoder = dataio_prep(hparams)
# Trainer initialization
asr_brain = ASR(
modules=hparams["modules"],
hparams=hparams,
run_opts=run_opts,
checkpointer=hparams["checkpointer"],
)
asr_brain.label_encoder = label_encoder
# Training/validation loop
asr_brain.fit(
asr_brain.hparams.epoch_counter,
train_data,
valid_data,
train_loader_kwargs=hparams["train_dataloader_opts"],
valid_loader_kwargs=hparams["valid_dataloader_opts"],
)
# Test
asr_brain.evaluate(
test_data,
min_key="PER",
test_loader_kwargs=hparams["test_dataloader_opts"],
)