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Merge pull request #743 from LittleChenCc/develop

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ST-related code and script for TED-En-Zh translation
pull/748/head
Hui Zhang 3 years ago committed by GitHub
parent 20d6f9ae4b 9d05a749e2
commit 566f636cc6
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21 changed files with 3042 additions and 6 deletions
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13
deepspeech/exps/u2_st/__init__.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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deepspeech/exps/u2_st/bin/export.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Export for U2 model."""
from deepspeech.exps.u2_st.config import get_cfg_defaults
from deepspeech.exps.u2_st.model import U2STTester as Tester
from deepspeech.training.cli import default_argument_parser
from deepspeech.utils.utility import print_arguments
def main_sp(config, args):
exp = Tester(config, args)
exp.setup()
exp.run_export()
def main(config, args):
main_sp(config, args)
if __name__ == "__main__":
parser = default_argument_parser()
args = parser.parse_args()
print_arguments(args, globals())
# https://yaml.org/type/float.html
config = get_cfg_defaults()
if args.config:
config.merge_from_file(args.config)
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
print(config)
if args.dump_config:
with open(args.dump_config, 'w') as f:
print(config, file=f)
main(config, args)

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deepspeech/exps/u2_st/bin/test.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Evaluation for U2 model."""
import cProfile
from deepspeech.exps.u2_st.config import get_cfg_defaults
from deepspeech.exps.u2_st.model import U2STTester as Tester
from deepspeech.training.cli import default_argument_parser
from deepspeech.utils.utility import print_arguments
# TODO(hui zhang): dynamic load
def main_sp(config, args):
exp = Tester(config, args)
exp.setup()
exp.run_test()
def main(config, args):
main_sp(config, args)
if __name__ == "__main__":
parser = default_argument_parser()
args = parser.parse_args()
print_arguments(args, globals())
# https://yaml.org/type/float.html
config = get_cfg_defaults()
if args.config:
config.merge_from_file(args.config)
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
print(config)
if args.dump_config:
with open(args.dump_config, 'w') as f:
print(config, file=f)
# Setting for profiling
pr = cProfile.Profile()
pr.runcall(main, config, args)
pr.dump_stats('test.profile')

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deepspeech/exps/u2_st/bin/train.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Trainer for U2 model."""
import cProfile
import os
from paddle import distributed as dist
from deepspeech.exps.u2_st.config import get_cfg_defaults
from deepspeech.exps.u2_st.model import U2STTrainer as Trainer
from deepspeech.training.cli import default_argument_parser
from deepspeech.utils.utility import print_arguments
def main_sp(config, args):
exp = Trainer(config, args)
exp.setup()
exp.run()
def main(config, args):
if args.device == "gpu" and args.nprocs > 1:
dist.spawn(main_sp, args=(config, args), nprocs=args.nprocs)
else:
main_sp(config, args)
if __name__ == "__main__":
parser = default_argument_parser()
args = parser.parse_args()
print_arguments(args, globals())
# https://yaml.org/type/float.html
config = get_cfg_defaults()
if args.config:
config.merge_from_file(args.config)
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
print(config)
if args.dump_config:
with open(args.dump_config, 'w') as f:
print(config, file=f)
# Setting for profiling
pr = cProfile.Profile()
pr.runcall(main, config, args)
pr.dump_stats(os.path.join(args.output, 'train.profile'))

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deepspeech/exps/u2_st/config.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from yacs.config import CfgNode
from deepspeech.exps.u2_st.model import U2STTester
from deepspeech.exps.u2_st.model import U2STTrainer
from deepspeech.io.collator_st import SpeechCollator
from deepspeech.io.dataset import ManifestDataset
from deepspeech.models.u2_st import U2STModel
_C = CfgNode()
_C.data = ManifestDataset.params()
_C.collator = SpeechCollator.params()
_C.model = U2STModel.params()
_C.training = U2STTrainer.params()
_C.decoding = U2STTester.params()
def get_cfg_defaults():
"""Get a yacs CfgNode object with default values for my_project."""
# Return a clone so that the defaults will not be altered
# This is for the "local variable" use pattern
config = _C.clone()
config.set_new_allowed(True)
return config

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deepspeech/exps/u2_st/model.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains U2 model."""
import json
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import List
from typing import Optional
from typing import Tuple
import numpy as np
import paddle
from paddle import distributed as dist
from paddle.io import DataLoader
from yacs.config import CfgNode
from deepspeech.io.collator_st import KaldiPrePorocessedCollator
from deepspeech.io.collator_st import SpeechCollator
from deepspeech.io.collator_st import TripletKaldiPrePorocessedCollator
from deepspeech.io.collator_st import TripletSpeechCollator
from deepspeech.io.dataset import ManifestDataset
from deepspeech.io.dataset import TripletManifestDataset
from deepspeech.io.sampler import SortagradBatchSampler
from deepspeech.io.sampler import SortagradDistributedBatchSampler
from deepspeech.models.u2_st import U2STModel
from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
from deepspeech.training.scheduler import WarmupLR
from deepspeech.training.trainer import Trainer
from deepspeech.utils import bleu_score
from deepspeech.utils import ctc_utils
from deepspeech.utils import error_rate
from deepspeech.utils import layer_tools
from deepspeech.utils import mp_tools
from deepspeech.utils import text_grid
from deepspeech.utils import utility
from deepspeech.utils.log import Log
logger = Log(__name__).getlog()
class U2STTrainer(Trainer):
@classmethod
def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
# training config
default = CfgNode(
dict(
n_epoch=50, # train epochs
log_interval=100, # steps
accum_grad=1, # accum grad by # steps
global_grad_clip=5.0, # the global norm clip
))
default.optim = 'adam'
default.optim_conf = CfgNode(
dict(
lr=5e-4, # learning rate
weight_decay=1e-6, # the coeff of weight decay
))
default.scheduler = 'warmuplr'
default.scheduler_conf = CfgNode(
dict(
warmup_steps=25000,
lr_decay=1.0, # learning rate decay
))
if config is not None:
config.merge_from_other_cfg(default)
return default
def __init__(self, config, args):
super().__init__(config, args)
def train_batch(self, batch_index, batch_data, msg):
train_conf = self.config.training
start = time.time()
utt, audio, audio_len, text, text_len = batch_data
if isinstance(text, list) and isinstance(text_len, list):
# joint training with ASR. Two decoding texts [translation, transcription]
text, text_transcript = text
text_len, text_transcript_len = text_len
loss, st_loss, attention_loss, ctc_loss = self.model(
audio, audio_len, text, text_len, text_transcript,
text_transcript_len)
else:
loss, st_loss, attention_loss, ctc_loss = self.model(
audio, audio_len, text, text_len)
# loss div by `batch_size * accum_grad`
loss /= train_conf.accum_grad
loss.backward()
layer_tools.print_grads(self.model, print_func=None)
losses_np = {'loss': float(loss) * train_conf.accum_grad}
losses_np['st_loss'] = float(st_loss)
if attention_loss:
losses_np['att_loss'] = float(attention_loss)
if ctc_loss:
losses_np['ctc_loss'] = float(ctc_loss)
if (batch_index + 1) % train_conf.accum_grad == 0:
self.optimizer.step()
self.optimizer.clear_grad()
self.lr_scheduler.step()
self.iteration += 1
iteration_time = time.time() - start
if (batch_index + 1) % train_conf.log_interval == 0:
msg += "train time: {:>.3f}s, ".format(iteration_time)
msg += "batch size: {}, ".format(self.config.collator.batch_size)
msg += "accum: {}, ".format(train_conf.accum_grad)
msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_np.items())
logger.info(msg)
if dist.get_rank() == 0 and self.visualizer:
losses_np_v = losses_np.copy()
losses_np_v.update({"lr": self.lr_scheduler()})
self.visualizer.add_scalars("step", losses_np_v,
self.iteration - 1)
@paddle.no_grad()
def valid(self):
self.model.eval()
logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
valid_losses = defaultdict(list)
num_seen_utts = 1
total_loss = 0.0
for i, batch in enumerate(self.valid_loader):
utt, audio, audio_len, text, text_len = batch
if isinstance(text, list) and isinstance(text_len, list):
text, text_transcript = text
text_len, text_transcript_len = text_len
loss, st_loss, attention_loss, ctc_loss = self.model(
audio, audio_len, text, text_len, text_transcript,
text_transcript_len)
else:
loss, st_loss, attention_loss, ctc_loss = self.model(
audio, audio_len, text, text_len)
if paddle.isfinite(loss):
num_utts = batch[1].shape[0]
num_seen_utts += num_utts
total_loss += float(st_loss) * num_utts
valid_losses['val_loss'].append(float(st_loss))
if attention_loss:
valid_losses['val_att_loss'].append(float(attention_loss))
if ctc_loss:
valid_losses['val_ctc_loss'].append(float(ctc_loss))
if (i + 1) % self.config.training.log_interval == 0:
valid_dump = {k: np.mean(v) for k, v in valid_losses.items()}
valid_dump['val_history_st_loss'] = total_loss / num_seen_utts
# logging
msg = f"Valid: Rank: {dist.get_rank()}, "
msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration)
msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in valid_dump.items())
logger.info(msg)
logger.info('Rank {} Val info st_val_loss {}'.format(
dist.get_rank(), total_loss / num_seen_utts))
return total_loss, num_seen_utts
def train(self):
"""The training process control by step."""
# !!!IMPORTANT!!!
# Try to export the model by script, if fails, we should refine
# the code to satisfy the script export requirements
# script_model = paddle.jit.to_static(self.model)
# script_model_path = str(self.checkpoint_dir / 'init')
# paddle.jit.save(script_model, script_model_path)
from_scratch = self.resume_or_scratch()
if from_scratch:
# save init model, i.e. 0 epoch
self.save(tag='init')
self.lr_scheduler.step(self.iteration)
if self.parallel:
self.train_loader.batch_sampler.set_epoch(self.epoch)
logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
while self.epoch < self.config.training.n_epoch:
self.model.train()
try:
data_start_time = time.time()
for batch_index, batch in enumerate(self.train_loader):
dataload_time = time.time() - data_start_time
msg = "Train: Rank: {}, ".format(dist.get_rank())
msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration)
msg += "batch : {}/{}, ".format(batch_index + 1,
len(self.train_loader))
msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
msg += "data time: {:>.3f}s, ".format(dataload_time)
self.train_batch(batch_index, batch, msg)
data_start_time = time.time()
except Exception as e:
logger.error(e)
raise e
total_loss, num_seen_utts = self.valid()
if dist.get_world_size() > 1:
num_seen_utts = paddle.to_tensor(num_seen_utts)
# the default operator in all_reduce function is sum.
dist.all_reduce(num_seen_utts)
total_loss = paddle.to_tensor(total_loss)
dist.all_reduce(total_loss)
cv_loss = total_loss / num_seen_utts
cv_loss = float(cv_loss)
else:
cv_loss = total_loss / num_seen_utts
logger.info(
'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss))
if self.visualizer:
self.visualizer.add_scalars(
'epoch', {'cv_loss': cv_loss,
'lr': self.lr_scheduler()}, self.epoch)
self.save(tag=self.epoch, infos={'val_loss': cv_loss})
self.new_epoch()
def setup_dataloader(self):
config = self.config.clone()
config.defrost()
config.collator.keep_transcription_text = False
# train/valid dataset, return token ids
Dataset = TripletManifestDataset if config.model.model_conf.asr_weight > 0. else ManifestDataset
config.data.manifest = config.data.train_manifest
train_dataset = Dataset.from_config(config)
config.data.manifest = config.data.dev_manifest
dev_dataset = Dataset.from_config(config)
if config.collator.raw_wav:
if config.model.model_conf.asr_weight > 0.:
Collator = TripletSpeechCollator
TestCollator = SpeechCollator
else:
TestCollator = Collator = SpeechCollator
# Not yet implement the mtl loader for raw_wav.
else:
if config.model.model_conf.asr_weight > 0.:
Collator = TripletKaldiPrePorocessedCollator
TestCollator = KaldiPrePorocessedCollator
else:
TestCollator = Collator = KaldiPrePorocessedCollator
collate_fn_train = Collator.from_config(config)
config.collator.augmentation_config = ""
collate_fn_dev = Collator.from_config(config)
if self.parallel:
batch_sampler = SortagradDistributedBatchSampler(
train_dataset,
batch_size=config.collator.batch_size,
num_replicas=None,
rank=None,
shuffle=True,
drop_last=True,
sortagrad=config.collator.sortagrad,
shuffle_method=config.collator.shuffle_method)
else:
batch_sampler = SortagradBatchSampler(
train_dataset,
shuffle=True,
batch_size=config.collator.batch_size,
drop_last=True,
sortagrad=config.collator.sortagrad,
shuffle_method=config.collator.shuffle_method)
self.train_loader = DataLoader(
train_dataset,
batch_sampler=batch_sampler,
collate_fn=collate_fn_train,
num_workers=config.collator.num_workers, )
self.valid_loader = DataLoader(
dev_dataset,
batch_size=config.collator.batch_size,
shuffle=False,
drop_last=False,
collate_fn=collate_fn_dev)
# test dataset, return raw text
config.data.manifest = config.data.test_manifest
# filter test examples, will cause less examples, but no mismatch with training
# and can use large batch size , save training time, so filter test egs now.
# config.data.min_input_len = 0.0 # second
# config.data.max_input_len = float('inf') # second
# config.data.min_output_len = 0.0 # tokens
# config.data.max_output_len = float('inf') # tokens
# config.data.min_output_input_ratio = 0.00
# config.data.max_output_input_ratio = float('inf')
test_dataset = ManifestDataset.from_config(config)
# return text ord id
config.collator.keep_transcription_text = True
config.collator.augmentation_config = ""
self.test_loader = DataLoader(
test_dataset,
batch_size=config.decoding.batch_size,
shuffle=False,
drop_last=False,
collate_fn=TestCollator.from_config(config))
# return text token id
config.collator.keep_transcription_text = False
self.align_loader = DataLoader(
test_dataset,
batch_size=config.decoding.batch_size,
shuffle=False,
drop_last=False,
collate_fn=TestCollator.from_config(config))
logger.info("Setup train/valid/test/align Dataloader!")
def setup_model(self):
config = self.config
model_conf = config.model
model_conf.defrost()
model_conf.input_dim = self.train_loader.collate_fn.feature_size
model_conf.output_dim = self.train_loader.collate_fn.vocab_size
model_conf.freeze()
model = U2STModel.from_config(model_conf)
if self.parallel:
model = paddle.DataParallel(model)
logger.info(f"{model}")
layer_tools.print_params(model, logger.info)
train_config = config.training
optim_type = train_config.optim
optim_conf = train_config.optim_conf
scheduler_type = train_config.scheduler
scheduler_conf = train_config.scheduler_conf
grad_clip = ClipGradByGlobalNormWithLog(train_config.global_grad_clip)
weight_decay = paddle.regularizer.L2Decay(optim_conf.weight_decay)
if scheduler_type == 'expdecaylr':
lr_scheduler = paddle.optimizer.lr.ExponentialDecay(
learning_rate=optim_conf.lr,
gamma=scheduler_conf.lr_decay,
verbose=False)
elif scheduler_type == 'warmuplr':
lr_scheduler = WarmupLR(
learning_rate=optim_conf.lr,
warmup_steps=scheduler_conf.warmup_steps,
verbose=False)
elif scheduler_type == 'noam':
lr_scheduler = paddle.optimizer.lr.NoamDecay(
learning_rate=optim_conf.lr,
d_model=model_conf.encoder_conf.output_size,
warmup_steps=scheduler_conf.warmup_steps,
verbose=False)
else:
raise ValueError(f"Not support scheduler: {scheduler_type}")
if optim_type == 'adam':
optimizer = paddle.optimizer.Adam(
learning_rate=lr_scheduler,
parameters=model.parameters(),
weight_decay=weight_decay,
grad_clip=grad_clip)
else:
raise ValueError(f"Not support optim: {optim_type}")
self.model = model
self.optimizer = optimizer
self.lr_scheduler = lr_scheduler
logger.info("Setup model/optimizer/lr_scheduler!")
class U2STTester(U2STTrainer):
@classmethod
def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
# decoding config
default = CfgNode(
dict(
alpha=2.5, # Coef of LM for beam search.
beta=0.3, # Coef of WC for beam search.
cutoff_prob=1.0, # Cutoff probability for pruning.
cutoff_top_n=40, # Cutoff number for pruning.
lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm', # Filepath for language model.
decoding_method='attention', # Decoding method. Options: 'attention', 'ctc_greedy_search',
# 'ctc_prefix_beam_search', 'attention_rescoring'
error_rate_type='bleu', # Error rate type for evaluation. Options `bleu`, 'char_bleu'
num_proc_bsearch=8, # # of CPUs for beam search.
beam_size=10, # Beam search width.
batch_size=16, # decoding batch size
ctc_weight=0.0, # ctc weight for attention rescoring decode mode.
decoding_chunk_size=-1, # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks=-1, # number of left chunks for decoding. Defaults to -1.
simulate_streaming=False, # simulate streaming inference. Defaults to False.
))
if config is not None:
config.merge_from_other_cfg(default)
return default
def __init__(self, config, args):
super().__init__(config, args)
def ordid2token(self, texts, texts_len):
""" ord() id to chr() chr """
trans = []
for text, n in zip(texts, texts_len):
n = n.numpy().item()
ids = text[:n]
trans.append(''.join([chr(i) for i in ids]))
return trans
def compute_translation_metrics(self,
utts,
audio,
audio_len,
texts,
texts_len,
bleu_func,
fout=None):
cfg = self.config.decoding
len_refs, num_ins = 0, 0
start_time = time.time()
text_feature = self.test_loader.collate_fn.text_feature
refs = [
"".join(chr(t) for t in text[:text_len])
for text, text_len in zip(texts, texts_len)
]
# from IPython import embed
# import os
# embed()
# os._exit(0)
hyps = self.model.decode(
audio,
audio_len,
text_feature=text_feature,
decoding_method=cfg.decoding_method,
lang_model_path=cfg.lang_model_path,
beam_alpha=cfg.alpha,
beam_beta=cfg.beta,
beam_size=cfg.beam_size,
cutoff_prob=cfg.cutoff_prob,
cutoff_top_n=cfg.cutoff_top_n,
num_processes=cfg.num_proc_bsearch,
ctc_weight=cfg.ctc_weight,
decoding_chunk_size=cfg.decoding_chunk_size,
num_decoding_left_chunks=cfg.num_decoding_left_chunks,
simulate_streaming=cfg.simulate_streaming)
decode_time = time.time() - start_time
for utt, target, result in zip(utts, refs, hyps):
len_refs += len(target.split())
num_ins += 1
if fout:
fout.write(utt + " " + result + "\n")
logger.info("\nReference: %s\nHypothesis: %s" % (target, result))
logger.info("One example BLEU = %s" %
(bleu_func([result], [[target]]).prec_str))
return dict(
hyps=hyps,
refs=refs,
bleu=bleu_func(hyps, [refs]).score,
len_refs=len_refs,
num_ins=num_ins, # num examples
num_frames=audio_len.sum().numpy().item(),
decode_time=decode_time)
@mp_tools.rank_zero_only
@paddle.no_grad()
def test(self):
assert self.args.result_file
self.model.eval()
logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
cfg = self.config.decoding
bleu_func = bleu_score.char_bleu if cfg.error_rate_type == 'char-bleu' else bleu_score.bleu
stride_ms = self.test_loader.collate_fn.stride_ms
hyps, refs = [], []
len_refs, num_ins = 0, 0
num_frames = 0.0
num_time = 0.0
with open(self.args.result_file, 'w') as fout:
for i, batch in enumerate(self.test_loader):
metrics = self.compute_translation_metrics(
*batch, bleu_func=bleu_func, fout=fout)
hyps += metrics['hyps']
refs += metrics['refs']
bleu = metrics['bleu']
num_frames += metrics['num_frames']
num_time += metrics["decode_time"]
len_refs += metrics['len_refs']
num_ins += metrics['num_ins']
rtf = num_time / (num_frames * stride_ms)
logger.info("RTF: %f, BELU (%d) = %f" % (rtf, num_ins, bleu))
rtf = num_time / (num_frames * stride_ms)
msg = "Test: "
msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration)
msg += "RTF: {}, ".format(rtf)
msg += "Test set [%s]: %s" % (len(hyps), str(bleu_func(hyps, [refs])))
logger.info(msg)
bleu_meta_path = os.path.splitext(self.args.result_file)[0] + '.bleu'
err_type_str = "BLEU"
with open(bleu_meta_path, 'w') as f:
data = json.dumps({
"epoch":
self.epoch,
"step":
self.iteration,
"rtf":
rtf,
err_type_str:
bleu_func(hyps, [refs]).score,
"dataset_hour": (num_frames * stride_ms) / 1000.0 / 3600.0,
"process_hour":
num_time / 1000.0 / 3600.0,
"num_examples":
num_ins,
"decode_method":
self.config.decoding.decoding_method,
})
f.write(data + '\n')
def run_test(self):
self.resume_or_scratch()
try:
self.test()
except KeyboardInterrupt:
sys.exit(-1)
@paddle.no_grad()
def align(self):
if self.config.decoding.batch_size > 1:
logger.fatal('alignment mode must be running with batch_size == 1')
sys.exit(1)
# xxx.align
assert self.args.result_file and self.args.result_file.endswith(
'.align')
self.model.eval()
logger.info(f"Align Total Examples: {len(self.align_loader.dataset)}")
stride_ms = self.align_loader.collate_fn.stride_ms
token_dict = self.align_loader.collate_fn.vocab_list
with open(self.args.result_file, 'w') as fout:
# one example in batch
for i, batch in enumerate(self.align_loader):
key, feat, feats_length, target, target_length = batch
# 1. Encoder
encoder_out, encoder_mask = self.model._forward_encoder(
feat, feats_length) # (B, maxlen, encoder_dim)
maxlen = encoder_out.size(1)
ctc_probs = self.model.ctc.log_softmax(
encoder_out) # (1, maxlen, vocab_size)
# 2. alignment
ctc_probs = ctc_probs.squeeze(0)
target = target.squeeze(0)
alignment = ctc_utils.forced_align(ctc_probs, target)
logger.info("align ids", key[0], alignment)
fout.write('{} {}\n'.format(key[0], alignment))
# 3. gen praat
# segment alignment
align_segs = text_grid.segment_alignment(alignment)
logger.info("align tokens", key[0], align_segs)
# IntervalTier, List["start end token\n"]
subsample = utility.get_subsample(self.config)
tierformat = text_grid.align_to_tierformat(
align_segs, subsample, token_dict)
# write tier
align_output_path = os.path.join(
os.path.dirname(self.args.result_file), "align")
tier_path = os.path.join(align_output_path, key[0] + ".tier")
with open(tier_path, 'w') as f:
f.writelines(tierformat)
# write textgrid
textgrid_path = os.path.join(align_output_path,
key[0] + ".TextGrid")
second_per_frame = 1. / (1000. /
stride_ms) # 25ms window, 10ms stride
second_per_example = (
len(alignment) + 1) * subsample * second_per_frame
text_grid.generate_textgrid(
maxtime=second_per_example,
intervals=tierformat,
output=textgrid_path)
def run_align(self):
self.resume_or_scratch()
try:
self.align()
except KeyboardInterrupt:
sys.exit(-1)
def load_inferspec(self):
"""infer model and input spec.
Returns:
nn.Layer: inference model
List[paddle.static.InputSpec]: input spec.
"""
from deepspeech.models.u2 import U2InferModel
infer_model = U2InferModel.from_pretrained(self.test_loader,
self.config.model.clone(),
self.args.checkpoint_path)
feat_dim = self.test_loader.collate_fn.feature_size
input_spec = [
paddle.static.InputSpec(shape=[1, None, feat_dim],
dtype='float32'), # audio, [B,T,D]
paddle.static.InputSpec(shape=[1],
dtype='int64'), # audio_length, [B]
]
return infer_model, input_spec
def export(self):
infer_model, input_spec = self.load_inferspec()
assert isinstance(input_spec, list), type(input_spec)
infer_model.eval()
static_model = paddle.jit.to_static(infer_model, input_spec=input_spec)
logger.info(f"Export code: {static_model.forward.code}")
paddle.jit.save(static_model, self.args.export_path)
def run_export(self):
try:
self.export()
except KeyboardInterrupt:
sys.exit(-1)
def setup(self):
"""Setup the experiment.
"""
paddle.set_device(self.args.device)
self.setup_output_dir()
self.setup_checkpointer()
self.setup_dataloader()
self.setup_model()
self.iteration = 0
self.epoch = 0
def setup_output_dir(self):
"""Create a directory used for output.
"""
# output dir
if self.args.output:
output_dir = Path(self.args.output).expanduser()
output_dir.mkdir(parents=True, exist_ok=True)
else:
output_dir = Path(
self.args.checkpoint_path).expanduser().parent.parent
output_dir.mkdir(parents=True, exist_ok=True)
self.output_dir = output_dir

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deepspeech/io/collator_st.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
from collections import namedtuple
from typing import Optional
from typing import Tuple
import kaldiio
import numpy as np
from yacs.config import CfgNode
from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
from deepspeech.frontend.featurizer.speech_featurizer import SpeechFeaturizer
from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
from deepspeech.frontend.normalizer import FeatureNormalizer
from deepspeech.frontend.speech import SpeechSegment
from deepspeech.frontend.utility import IGNORE_ID
from deepspeech.io.utility import pad_sequence
from deepspeech.utils.log import Log
__all__ = ["SpeechCollator", "KaldiPrePorocessedCollator"]
logger = Log(__name__).getlog()
# namedtupe need global for pickle.
TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object'])
class SpeechCollator():
@classmethod
def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
default = CfgNode(
dict(
augmentation_config="",
random_seed=0,
mean_std_filepath="",
unit_type="char",
vocab_filepath="",
spm_model_prefix="",
specgram_type='linear', # 'linear', 'mfcc', 'fbank'
feat_dim=0, # 'mfcc', 'fbank'
delta_delta=False, # 'mfcc', 'fbank'
stride_ms=10.0, # ms
window_ms=20.0, # ms
n_fft=None, # fft points
max_freq=None, # None for samplerate/2
target_sample_rate=16000, # target sample rate
use_dB_normalization=True,
target_dB=-20,
dither=1.0, # feature dither
keep_transcription_text=False))
if config is not None:
config.merge_from_other_cfg(default)
return default
@classmethod
def from_config(cls, config):
"""Build a SpeechCollator object from a config.
Args:
config (yacs.config.CfgNode): configs object.
Returns:
SpeechCollator: collator object.
"""
assert 'augmentation_config' in config.collator
assert 'keep_transcription_text' in config.collator
assert 'mean_std_filepath' in config.collator
assert 'vocab_filepath' in config.collator
assert 'specgram_type' in config.collator
assert 'n_fft' in config.collator
assert config.collator
if isinstance(config.collator.augmentation_config, (str, bytes)):
if config.collator.augmentation_config:
aug_file = io.open(
config.collator.augmentation_config,
mode='r',
encoding='utf8')
else:
aug_file = io.StringIO(initial_value='{}', newline='')
else:
aug_file = config.collator.augmentation_config
assert isinstance(aug_file, io.StringIO)
speech_collator = cls(
aug_file=aug_file,
random_seed=0,
mean_std_filepath=config.collator.mean_std_filepath,
unit_type=config.collator.unit_type,
vocab_filepath=config.collator.vocab_filepath,
spm_model_prefix=config.collator.spm_model_prefix,
specgram_type=config.collator.specgram_type,
feat_dim=config.collator.feat_dim,
delta_delta=config.collator.delta_delta,
stride_ms=config.collator.stride_ms,
window_ms=config.collator.window_ms,
n_fft=config.collator.n_fft,
max_freq=config.collator.max_freq,
target_sample_rate=config.collator.target_sample_rate,
use_dB_normalization=config.collator.use_dB_normalization,
target_dB=config.collator.target_dB,
dither=config.collator.dither,
keep_transcription_text=config.collator.keep_transcription_text)
return speech_collator
def __init__(
self,
aug_file,
mean_std_filepath,
vocab_filepath,
spm_model_prefix,
random_seed=0,
unit_type="char",
specgram_type='linear', # 'linear', 'mfcc', 'fbank'
feat_dim=0, # 'mfcc', 'fbank'
delta_delta=False, # 'mfcc', 'fbank'
stride_ms=10.0, # ms
window_ms=20.0, # ms
n_fft=None, # fft points
max_freq=None, # None for samplerate/2
target_sample_rate=16000, # target sample rate
use_dB_normalization=True,
target_dB=-20,
dither=1.0,
keep_transcription_text=True):
"""SpeechCollator Collator
Args:
unit_type(str): token unit type, e.g. char, word, spm
vocab_filepath (str): vocab file path.
mean_std_filepath (str): mean and std file path, which suffix is *.npy
spm_model_prefix (str): spm model prefix, need if `unit_type` is spm.
augmentation_config (str, optional): augmentation json str. Defaults to '{}'.
stride_ms (float, optional): stride size in ms. Defaults to 10.0.
window_ms (float, optional): window size in ms. Defaults to 20.0.
n_fft (int, optional): fft points for rfft. Defaults to None.
max_freq (int, optional): max cut freq. Defaults to None.
target_sample_rate (int, optional): target sample rate which used for training. Defaults to 16000.
specgram_type (str, optional): 'linear', 'mfcc' or 'fbank'. Defaults to 'linear'.
feat_dim (int, optional): audio feature dim, using by 'mfcc' or 'fbank'. Defaults to None.
delta_delta (bool, optional): audio feature with delta-delta, using by 'fbank' or 'mfcc'. Defaults to False.
use_dB_normalization (bool, optional): do dB normalization. Defaults to True.
target_dB (int, optional): target dB. Defaults to -20.
random_seed (int, optional): for random generator. Defaults to 0.
keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
if ``keep_transcription_text`` is False, text is token ids else is raw string.
Do augmentations
Padding audio features with zeros to make them have the same shape (or
a user-defined shape) within one batch.
"""
self._keep_transcription_text = keep_transcription_text
self._local_data = TarLocalData(tar2info={}, tar2object={})
self._augmentation_pipeline = AugmentationPipeline(
augmentation_config=aug_file.read(), random_seed=random_seed)
self._normalizer = FeatureNormalizer(
mean_std_filepath) if mean_std_filepath else None
self._stride_ms = stride_ms
self._target_sample_rate = target_sample_rate
self._speech_featurizer = SpeechFeaturizer(
unit_type=unit_type,
vocab_filepath=vocab_filepath,
spm_model_prefix=spm_model_prefix,
specgram_type=specgram_type,
feat_dim=feat_dim,
delta_delta=delta_delta,
stride_ms=stride_ms,
window_ms=window_ms,
n_fft=n_fft,
max_freq=max_freq,
target_sample_rate=target_sample_rate,
use_dB_normalization=use_dB_normalization,
target_dB=target_dB,
dither=dither)
def _parse_tar(self, file):
"""Parse a tar file to get a tarfile object
and a map containing tarinfoes
"""
result = {}
f = tarfile.open(file)
for tarinfo in f.getmembers():
result[tarinfo.name] = tarinfo
return f, result
def _subfile_from_tar(self, file):
"""Get subfile object from tar.
It will return a subfile object from tar file
and cached tar file info for next reading request.
"""
tarpath, filename = file.split(':', 1)[1].split('#', 1)
if 'tar2info' not in self._local_data.__dict__:
self._local_data.tar2info = {}
if 'tar2object' not in self._local_data.__dict__:
self._local_data.tar2object = {}
if tarpath not in self._local_data.tar2info:
object, infoes = self._parse_tar(tarpath)
self._local_data.tar2info[tarpath] = infoes
self._local_data.tar2object[tarpath] = object
return self._local_data.tar2object[tarpath].extractfile(
self._local_data.tar2info[tarpath][filename])
def process_utterance(self, audio_file, translation):
"""Load, augment, featurize and normalize for speech data.
:param audio_file: Filepath or file object of audio file.
:type audio_file: str | file
:param translation: translation text.
:type translation: str
:return: Tuple of audio feature tensor and data of translation part,
where translation part could be token ids or text.
:rtype: tuple of (2darray, list)
"""
if isinstance(audio_file, str) and audio_file.startswith('tar:'):
speech_segment = SpeechSegment.from_file(
self._subfile_from_tar(audio_file), translation)
else:
speech_segment = SpeechSegment.from_file(audio_file, translation)
# audio augment
self._augmentation_pipeline.transform_audio(speech_segment)
specgram, translation_part = self._speech_featurizer.featurize(
speech_segment, self._keep_transcription_text)
if self._normalizer:
specgram = self._normalizer.apply(specgram)
# specgram augment
specgram = self._augmentation_pipeline.transform_feature(specgram)
specgram = specgram.transpose([1, 0])
return specgram, translation_part
def __call__(self, batch):
"""batch examples
Args:
batch ([List]): batch is (audio, text)
audio (np.ndarray) shape (D, T)
text (List[int] or str): shape (U,)
Returns:
tuple(audio, text, audio_lens, text_lens): batched data.
audio : (B, Tmax, D)
audio_lens: (B)
text : (B, Umax)
text_lens: (B)
"""
audios = []
audio_lens = []
texts = []
text_lens = []
utts = []
for utt, audio, text in batch:
audio, text = self.process_utterance(audio, text)
#utt
utts.append(utt)
# audio
audios.append(audio) # [T, D]
audio_lens.append(audio.shape[0])
# text
# for training, text is token ids
# else text is string, convert to unicode ord
tokens = []
if self._keep_transcription_text:
assert isinstance(text, str), (type(text), text)
tokens = [ord(t) for t in text]
else:
tokens = text # token ids
tokens = tokens if isinstance(tokens, np.ndarray) else np.array(
tokens, dtype=np.int64)
texts.append(tokens)
text_lens.append(tokens.shape[0])
padded_audios = pad_sequence(
audios, padding_value=0.0).astype(np.float32) #[B, T, D]
audio_lens = np.array(audio_lens).astype(np.int64)
padded_texts = pad_sequence(
texts, padding_value=IGNORE_ID).astype(np.int64)
text_lens = np.array(text_lens).astype(np.int64)
return utts, padded_audios, audio_lens, padded_texts, text_lens
@property
def manifest(self):
return self._manifest
@property
def vocab_size(self):
return self._speech_featurizer.vocab_size
@property
def vocab_list(self):
return self._speech_featurizer.vocab_list
@property
def vocab_dict(self):
return self._speech_featurizer.vocab_dict
@property
def text_feature(self):
return self._speech_featurizer.text_feature
@property
def feature_size(self):
return self._speech_featurizer.feature_size
@property
def stride_ms(self):
return self._speech_featurizer.stride_ms
class TripletSpeechCollator(SpeechCollator):
def process_utterance(self, audio_file, translation, transcript):
"""Load, augment, featurize and normalize for speech data.
:param audio_file: Filepath or file object of audio file.
:type audio_file: str | file
:param translation: translation text.
:type translation: str
:return: Tuple of audio feature tensor and data of translation part,
where translation part could be token ids or text.
:rtype: tuple of (2darray, list)
"""
if isinstance(audio_file, str) and audio_file.startswith('tar:'):
speech_segment = SpeechSegment.from_file(
self._subfile_from_tar(audio_file), translation)
else:
speech_segment = SpeechSegment.from_file(audio_file, translation)
# audio augment
self._augmentation_pipeline.transform_audio(speech_segment)
specgram, translation_part = self._speech_featurizer.featurize(
speech_segment, self._keep_transcription_text)
transcript_part = self._speech_featurizer._text_featurizer.featurize(
transcript)
if self._normalizer:
specgram = self._normalizer.apply(specgram)
# specgram augment
specgram = self._augmentation_pipeline.transform_feature(specgram)
specgram = specgram.transpose([1, 0])
return specgram, translation_part, transcript_part
def __call__(self, batch):
"""batch examples
Args:
batch ([List]): batch is (audio, text)
audio (np.ndarray) shape (D, T)
text (List[int] or str): shape (U,)
Returns:
tuple(audio, text, audio_lens, text_lens): batched data.
audio : (B, Tmax, D)
audio_lens: (B)
text : (B, Umax)
text_lens: (B)
"""
audios = []
audio_lens = []
translation_text = []
translation_text_lens = []
transcription_text = []
transcription_text_lens = []
utts = []
for utt, audio, translation, transcription in batch:
audio, translation, transcription = self.process_utterance(
audio, translation, transcription)
#utt
utts.append(utt)
# audio
audios.append(audio) # [T, D]
audio_lens.append(audio.shape[0])
# text
# for training, text is token ids
# else text is string, convert to unicode ord
tokens = [[], []]
for idx, text in enumerate([translation, transcription]):
if self._keep_transcription_text:
assert isinstance(text, str), (type(text), text)
tokens[idx] = [ord(t) for t in text]
else:
tokens[idx] = text # token ids
tokens[idx] = tokens[idx] if isinstance(
tokens[idx], np.ndarray) else np.array(
tokens[idx], dtype=np.int64)
translation_text.append(tokens[0])
translation_text_lens.append(tokens[0].shape[0])
transcription_text.append(tokens[1])
transcription_text_lens.append(tokens[1].shape[0])
padded_audios = pad_sequence(
audios, padding_value=0.0).astype(np.float32) #[B, T, D]
audio_lens = np.array(audio_lens).astype(np.int64)
padded_translation = pad_sequence(
translation_text, padding_value=IGNORE_ID).astype(np.int64)
translation_lens = np.array(translation_text_lens).astype(np.int64)
padded_transcription = pad_sequence(
transcription_text, padding_value=IGNORE_ID).astype(np.int64)
transcription_lens = np.array(transcription_text_lens).astype(np.int64)
return utts, padded_audios, audio_lens, (
padded_translation, padded_transcription), (translation_lens,
transcription_lens)
class KaldiPrePorocessedCollator(SpeechCollator):
@classmethod
def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
default = CfgNode(
dict(
augmentation_config="",
random_seed=0,
unit_type="char",
vocab_filepath="",
spm_model_prefix="",
feat_dim=0,
stride_ms=10.0,
keep_transcription_text=False))
if config is not None:
config.merge_from_other_cfg(default)
return default
@classmethod
def from_config(cls, config):
"""Build a SpeechCollator object from a config.
Args:
config (yacs.config.CfgNode): configs object.
Returns:
SpeechCollator: collator object.
"""
assert 'augmentation_config' in config.collator
assert 'keep_transcription_text' in config.collator
assert 'vocab_filepath' in config.collator
assert config.collator
if isinstance(config.collator.augmentation_config, (str, bytes)):
if config.collator.augmentation_config:
aug_file = io.open(
config.collator.augmentation_config,
mode='r',
encoding='utf8')
else:
aug_file = io.StringIO(initial_value='{}', newline='')
else:
aug_file = config.collator.augmentation_config
assert isinstance(aug_file, io.StringIO)
speech_collator = cls(
aug_file=aug_file,
random_seed=0,
unit_type=config.collator.unit_type,
vocab_filepath=config.collator.vocab_filepath,
spm_model_prefix=config.collator.spm_model_prefix,
feat_dim=config.collator.feat_dim,
stride_ms=config.collator.stride_ms,
keep_transcription_text=config.collator.keep_transcription_text)
return speech_collator
def __init__(self,
aug_file,
vocab_filepath,
spm_model_prefix,
random_seed=0,
unit_type="char",
feat_dim=0,
stride_ms=10.0,
keep_transcription_text=True):
"""SpeechCollator Collator
Args:
unit_type(str): token unit type, e.g. char, word, spm
vocab_filepath (str): vocab file path.
spm_model_prefix (str): spm model prefix, need if `unit_type` is spm.
augmentation_config (str, optional): augmentation json str. Defaults to '{}'.
random_seed (int, optional): for random generator. Defaults to 0.
keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
if ``keep_transcription_text`` is False, text is token ids else is raw string.
Do augmentations
Padding audio features with zeros to make them have the same shape (or
a user-defined shape) within one batch.
"""
self._keep_transcription_text = keep_transcription_text
self._feat_dim = feat_dim
self._stride_ms = stride_ms
self._local_data = TarLocalData(tar2info={}, tar2object={})
self._augmentation_pipeline = AugmentationPipeline(
augmentation_config=aug_file.read(), random_seed=random_seed)
self._text_featurizer = TextFeaturizer(unit_type, vocab_filepath,
spm_model_prefix)
def process_utterance(self, audio_file, translation):
"""Load, augment, featurize and normalize for speech data.
:param audio_file: Filepath or file object of kaldi processed feature.
:type audio_file: str | file
:param translation: Translation text.
:type translation: str
:return: Tuple of audio feature tensor and data of translation part,
where translation part could be token ids or text.
:rtype: tuple of (2darray, list)
"""
specgram = kaldiio.load_mat(audio_file)
specgram = specgram.transpose([1, 0])
assert specgram.shape[
0] == self._feat_dim, 'expect feat dim {}, but got {}'.format(
self._feat_dim, specgram.shape[0])
# specgram augment
specgram = self._augmentation_pipeline.transform_feature(specgram)
specgram = specgram.transpose([1, 0])
if self._keep_transcription_text:
return specgram, translation
else:
text_ids = self._text_featurizer.featurize(translation)
return specgram, text_ids
@property
def manifest(self):
return self._manifest
@property
def vocab_size(self):
return self._text_featurizer.vocab_size
@property
def vocab_list(self):
return self._text_featurizer.vocab_list
@property
def vocab_dict(self):
return self._text_featurizer.vocab_dict
@property
def text_feature(self):
return self._text_featurizer
@property
def feature_size(self):
return self._feat_dim
@property
def stride_ms(self):
return self._stride_ms
class TripletKaldiPrePorocessedCollator(KaldiPrePorocessedCollator):
def process_utterance(self, audio_file, translation, transcript):
"""Load, augment, featurize and normalize for speech data.
:param audio_file: Filepath or file object of kali processed feature.
:type audio_file: str | file
:param translation: Translation text.
:type translation: str
:param transcript: Transcription text.
:type transcript: str
:return: Tuple of audio feature tensor and data of translation and transcription parts,
where translation and transcription parts could be token ids or text.
:rtype: tuple of (2darray, (list, list))
"""
specgram = kaldiio.load_mat(audio_file)
specgram = specgram.transpose([1, 0])
assert specgram.shape[
0] == self._feat_dim, 'expect feat dim {}, but got {}'.format(
self._feat_dim, specgram.shape[0])
# specgram augment
specgram = self._augmentation_pipeline.transform_feature(specgram)
specgram = specgram.transpose([1, 0])
if self._keep_transcription_text:
return specgram, translation, transcript
else:
translation_text_ids = self._text_featurizer.featurize(translation)
transcript_text_ids = self._text_featurizer.featurize(transcript)
return specgram, translation_text_ids, transcript_text_ids
def __call__(self, batch):
"""batch examples
Args:
batch ([List]): batch is (audio, text)
audio (np.ndarray) shape (D, T)
translation (List[int] or str): shape (U,)
transcription (List[int] or str): shape (V,)
Returns:
tuple(audio, text, audio_lens, text_lens): batched data.
audio : (B, Tmax, D)
audio_lens: (B)
translation_text : (B, Umax)
translation_text_lens: (B)
transcription_text : (B, Vmax)
transcription_text_lens: (B)
"""
audios = []
audio_lens = []
translation_text = []
translation_text_lens = []
transcription_text = []
transcription_text_lens = []
utts = []
for utt, audio, translation, transcription in batch:
audio, translation, transcription = self.process_utterance(
audio, translation, transcription)
#utt
utts.append(utt)
# audio
audios.append(audio) # [T, D]
audio_lens.append(audio.shape[0])
# text
# for training, text is token ids
# else text is string, convert to unicode ord
tokens = [[], []]
for idx, text in enumerate([translation, transcription]):
if self._keep_transcription_text:
assert isinstance(text, str), (type(text), text)
tokens[idx] = [ord(t) for t in text]
else:
tokens[idx] = text # token ids
tokens[idx] = tokens[idx] if isinstance(
tokens[idx], np.ndarray) else np.array(
tokens[idx], dtype=np.int64)
translation_text.append(tokens[0])
translation_text_lens.append(tokens[0].shape[0])
transcription_text.append(tokens[1])
transcription_text_lens.append(tokens[1].shape[0])
padded_audios = pad_sequence(
audios, padding_value=0.0).astype(np.float32) #[B, T, D]
audio_lens = np.array(audio_lens).astype(np.int64)
padded_translation = pad_sequence(
translation_text, padding_value=IGNORE_ID).astype(np.int64)
translation_lens = np.array(translation_text_lens).astype(np.int64)
padded_transcription = pad_sequence(
transcription_text, padding_value=IGNORE_ID).astype(np.int64)
transcription_lens = np.array(transcription_text_lens).astype(np.int64)
return utts, padded_audios, audio_lens, (
padded_translation, padded_transcription), (translation_lens,
transcription_lens)

17
deepspeech/io/dataset.py
Unescape View File

@ -19,9 +19,7 @@ from yacs.config import CfgNode
from deepspeech.frontend.utility import read_manifest
from deepspeech.utils.log import Log
__all__ = [
"ManifestDataset",
]
__all__ = ["ManifestDataset", "TripletManifestDataset"]
logger = Log(__name__).getlog()
@ -105,3 +103,16 @@ class ManifestDataset(Dataset):
def __getitem__(self, idx):
instance = self._manifest[idx]
return instance["utt"], instance["feat"], instance["text"]
class TripletManifestDataset(ManifestDataset):
"""
For Joint Training of Speech Translation and ASR.
text: translation,
text1: transcript.
"""
def __getitem__(self, idx):
instance = self._manifest[idx]
return instance["utt"], instance["feat"], instance["text"], instance[
"text1"]

734
deepspeech/models/u2_st.py
Unescape View File

@ -0,0 +1,734 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""U2 ASR Model
Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition
(https://arxiv.org/pdf/2012.05481.pdf)
"""
import sys
import time
from collections import defaultdict
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
import paddle
from paddle import jit
from paddle import nn
from yacs.config import CfgNode
from deepspeech.frontend.utility import IGNORE_ID
from deepspeech.frontend.utility import load_cmvn
from deepspeech.modules.cmvn import GlobalCMVN
from deepspeech.modules.ctc import CTCDecoder
from deepspeech.modules.decoder import TransformerDecoder
from deepspeech.modules.encoder import ConformerEncoder
from deepspeech.modules.encoder import TransformerEncoder
from deepspeech.modules.loss import LabelSmoothingLoss
from deepspeech.modules.mask import make_pad_mask
from deepspeech.modules.mask import mask_finished_preds
from deepspeech.modules.mask import mask_finished_scores
from deepspeech.modules.mask import subsequent_mask
from deepspeech.utils import checkpoint
from deepspeech.utils import layer_tools
from deepspeech.utils.ctc_utils import remove_duplicates_and_blank
from deepspeech.utils.log import Log
from deepspeech.utils.tensor_utils import add_sos_eos
from deepspeech.utils.tensor_utils import pad_sequence
from deepspeech.utils.tensor_utils import th_accuracy
from deepspeech.utils.utility import log_add
__all__ = ["U2STModel", "U2STInferModel"]
logger = Log(__name__).getlog()
class U2STBaseModel(nn.Module):
"""CTC-Attention hybrid Encoder-Decoder model"""
@classmethod
def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
# network architecture
default = CfgNode()
# allow add new item when merge_with_file
default.cmvn_file = ""
default.cmvn_file_type = "json"
default.input_dim = 0
default.output_dim = 0
# encoder related
default.encoder = 'transformer'
default.encoder_conf = CfgNode(
dict(
output_size=256, # dimension of attention
attention_heads=4,
linear_units=2048, # the number of units of position-wise feed forward
num_blocks=12, # the number of encoder blocks
dropout_rate=0.1,
positional_dropout_rate=0.1,
attention_dropout_rate=0.0,
input_layer='conv2d', # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before=True,
# use_cnn_module=True,
# cnn_module_kernel=15,
# activation_type='swish',
# pos_enc_layer_type='rel_pos',
# selfattention_layer_type='rel_selfattn',
))
# decoder related
default.decoder = 'transformer'
default.decoder_conf = CfgNode(
dict(
attention_heads=4,
linear_units=2048,
num_blocks=6,
dropout_rate=0.1,
positional_dropout_rate=0.1,
self_attention_dropout_rate=0.0,
src_attention_dropout_rate=0.0, ))
# hybrid CTC/attention
default.model_conf = CfgNode(
dict(
asr_weight=0.0,
ctc_weight=0.0,
lsm_weight=0.1, # label smoothing option
length_normalized_loss=False, ))
if config is not None:
config.merge_from_other_cfg(default)
return default
def __init__(self,
vocab_size: int,
encoder: TransformerEncoder,
st_decoder: TransformerDecoder,
decoder: TransformerDecoder=None,
ctc: CTCDecoder=None,
ctc_weight: float=0.0,
asr_weight: float=0.0,
ignore_id: int=IGNORE_ID,
lsm_weight: float=0.0,
length_normalized_loss: bool=False):
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
super().__init__()
# note that eos is the same as sos (equivalent ID)
self.sos = vocab_size - 1
self.eos = vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.ctc_weight = ctc_weight
self.asr_weight = asr_weight
self.encoder = encoder
self.st_decoder = st_decoder
self.decoder = decoder
self.ctc = ctc
self.criterion_att = LabelSmoothingLoss(
size=vocab_size,
padding_idx=ignore_id,
smoothing=lsm_weight,
normalize_length=length_normalized_loss, )
def forward(
self,
speech: paddle.Tensor,
speech_lengths: paddle.Tensor,
text: paddle.Tensor,
text_lengths: paddle.Tensor,
asr_text: paddle.Tensor=None,
asr_text_lengths: paddle.Tensor=None,
) -> Tuple[Optional[paddle.Tensor], Optional[paddle.Tensor], Optional[
paddle.Tensor]]:
"""Frontend + Encoder + Decoder + Calc loss
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
text: (Batch, Length)
text_lengths: (Batch,)
Returns:
total_loss, attention_loss, ctc_loss
"""
assert text_lengths.dim() == 1, text_lengths.shape
# Check that batch_size is unified
assert (speech.shape[0] == speech_lengths.shape[0] == text.shape[0] ==
text_lengths.shape[0]), (speech.shape, speech_lengths.shape,
text.shape, text_lengths.shape)
# 1. Encoder
start = time.time()
encoder_out, encoder_mask = self.encoder(speech, speech_lengths)
encoder_time = time.time() - start
#logger.debug(f"encoder time: {encoder_time}")
#TODO(Hui Zhang): sum not support bool type
#encoder_out_lens = encoder_mask.squeeze(1).sum(1) #[B, 1, T] -> [B]
encoder_out_lens = encoder_mask.squeeze(1).cast(paddle.int64).sum(
1) #[B, 1, T] -> [B]
# 2a. ST-decoder branch
start = time.time()
loss_st, acc_st = self._calc_st_loss(encoder_out, encoder_mask, text,
text_lengths)
decoder_time = time.time() - start
loss_asr_att = None
loss_asr_ctc = None
# 2b. ASR Attention-decoder branch
if self.asr_weight > 0.:
if self.ctc_weight != 1.0:
start = time.time()
loss_asr_att, acc_att = self._calc_att_loss(
encoder_out, encoder_mask, asr_text, asr_text_lengths)
decoder_time = time.time() - start
# 2c. CTC branch
if self.ctc_weight != 0.0:
start = time.time()
loss_asr_ctc = self.ctc(encoder_out, encoder_out_lens, asr_text,
asr_text_lengths)
ctc_time = time.time() - start
if loss_asr_ctc is None:
loss_asr = loss_asr_att
elif loss_asr_att is None:
loss_asr = loss_asr_ctc
else:
loss_asr = self.ctc_weight * loss_asr_ctc + (1 - self.ctc_weight
) * loss_asr_att
loss = self.asr_weight * loss_asr + (1 - self.asr_weight) * loss_st
else:
loss = loss_st
return loss, loss_st, loss_asr_att, loss_asr_ctc
def _calc_st_loss(
self,
encoder_out: paddle.Tensor,
encoder_mask: paddle.Tensor,
ys_pad: paddle.Tensor,
ys_pad_lens: paddle.Tensor, ) -> Tuple[paddle.Tensor, float]:
"""Calc attention loss.
Args:
encoder_out (paddle.Tensor): [B, Tmax, D]
encoder_mask (paddle.Tensor): [B, 1, Tmax]
ys_pad (paddle.Tensor): [B, Umax]
ys_pad_lens (paddle.Tensor): [B]
Returns:
Tuple[paddle.Tensor, float]: attention_loss, accuracy rate
"""
ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos,
self.ignore_id)
ys_in_lens = ys_pad_lens + 1
# 1. Forward decoder
decoder_out, _ = self.st_decoder(encoder_out, encoder_mask, ys_in_pad,
ys_in_lens)
# 2. Compute attention loss
loss_att = self.criterion_att(decoder_out, ys_out_pad)
acc_att = th_accuracy(
decoder_out.view(-1, self.vocab_size),
ys_out_pad,
ignore_label=self.ignore_id, )
return loss_att, acc_att
def _calc_att_loss(
self,
encoder_out: paddle.Tensor,
encoder_mask: paddle.Tensor,
ys_pad: paddle.Tensor,
ys_pad_lens: paddle.Tensor, ) -> Tuple[paddle.Tensor, float]:
"""Calc attention loss.
Args:
encoder_out (paddle.Tensor): [B, Tmax, D]
encoder_mask (paddle.Tensor): [B, 1, Tmax]
ys_pad (paddle.Tensor): [B, Umax]
ys_pad_lens (paddle.Tensor): [B]
Returns:
Tuple[paddle.Tensor, float]: attention_loss, accuracy rate
"""
ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos,
self.ignore_id)
ys_in_lens = ys_pad_lens + 1
# 1. Forward decoder
decoder_out, _ = self.decoder(encoder_out, encoder_mask, ys_in_pad,
ys_in_lens)
# 2. Compute attention loss
loss_att = self.criterion_att(decoder_out, ys_out_pad)
acc_att = th_accuracy(
decoder_out.view(-1, self.vocab_size),
ys_out_pad,
ignore_label=self.ignore_id, )
return loss_att, acc_att
def _forward_encoder(
self,
speech: paddle.Tensor,
speech_lengths: paddle.Tensor,
decoding_chunk_size: int=-1,
num_decoding_left_chunks: int=-1,
simulate_streaming: bool=False,
) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Encoder pass.
Args:
speech (paddle.Tensor): [B, Tmax, D]
speech_lengths (paddle.Tensor): [B]
decoding_chunk_size (int, optional): chuck size. Defaults to -1.
num_decoding_left_chunks (int, optional): nums chunks. Defaults to -1.
simulate_streaming (bool, optional): streaming or not. Defaults to False.
Returns:
Tuple[paddle.Tensor, paddle.Tensor]:
encoder hiddens (B, Tmax, D),
encoder hiddens mask (B, 1, Tmax).
"""
# Let's assume B = batch_size
# 1. Encoder
if simulate_streaming and decoding_chunk_size > 0:
encoder_out, encoder_mask = self.encoder.forward_chunk_by_chunk(
speech,
decoding_chunk_size=decoding_chunk_size,
num_decoding_left_chunks=num_decoding_left_chunks
) # (B, maxlen, encoder_dim)
else:
encoder_out, encoder_mask = self.encoder(
speech,
speech_lengths,
decoding_chunk_size=decoding_chunk_size,
num_decoding_left_chunks=num_decoding_left_chunks
) # (B, maxlen, encoder_dim)
return encoder_out, encoder_mask
def translate(
self,
speech: paddle.Tensor,
speech_lengths: paddle.Tensor,
beam_size: int=10,
decoding_chunk_size: int=-1,
num_decoding_left_chunks: int=-1,
simulate_streaming: bool=False, ) -> paddle.Tensor:
""" Apply beam search on attention decoder
Args:
speech (paddle.Tensor): (batch, max_len, feat_dim)
speech_length (paddle.Tensor): (batch, )
beam_size (int): beam size for beam search
decoding_chunk_size (int): decoding chunk for dynamic chunk
trained model.
<0: for decoding, use full chunk.
>0: for decoding, use fixed chunk size as set.
0: used for training, it's prohibited here
simulate_streaming (bool): whether do encoder forward in a
streaming fashion
Returns:
paddle.Tensor: decoding result, (batch, max_result_len)
"""
assert speech.shape[0] == speech_lengths.shape[0]
assert decoding_chunk_size != 0
device = speech.place
batch_size = speech.shape[0]
# Let's assume B = batch_size and N = beam_size
# 1. Encoder
encoder_out, encoder_mask = self._forward_encoder(
speech, speech_lengths, decoding_chunk_size,
num_decoding_left_chunks,
simulate_streaming) # (B, maxlen, encoder_dim)
maxlen = encoder_out.size(1)
encoder_dim = encoder_out.size(2)
running_size = batch_size * beam_size
encoder_out = encoder_out.unsqueeze(1).repeat(1, beam_size, 1, 1).view(
running_size, maxlen, encoder_dim) # (B*N, maxlen, encoder_dim)
encoder_mask = encoder_mask.unsqueeze(1).repeat(
1, beam_size, 1, 1).view(running_size, 1,
maxlen) # (B*N, 1, max_len)
hyps = paddle.ones(
[running_size, 1], dtype=paddle.long).fill_(self.sos) # (B*N, 1)
# log scale score
scores = paddle.to_tensor(
[0.0] + [-float('inf')] * (beam_size - 1), dtype=paddle.float)
scores = scores.to(device).repeat(batch_size).unsqueeze(1).to(
device) # (B*N, 1)
end_flag = paddle.zeros_like(scores, dtype=paddle.bool) # (B*N, 1)
cache: Optional[List[paddle.Tensor]] = None
# 2. Decoder forward step by step
for i in range(1, maxlen + 1):
# Stop if all batch and all beam produce eos
# TODO(Hui Zhang): if end_flag.sum() == running_size:
if end_flag.cast(paddle.int64).sum() == running_size:
break
# 2.1 Forward decoder step
hyps_mask = subsequent_mask(i).unsqueeze(0).repeat(
running_size, 1, 1).to(device) # (B*N, i, i)
# logp: (B*N, vocab)
logp, cache = self.st_decoder.forward_one_step(
encoder_out, encoder_mask, hyps, hyps_mask, cache)
# 2.2 First beam prune: select topk best prob at current time
top_k_logp, top_k_index = logp.topk(beam_size) # (B*N, N)
top_k_logp = mask_finished_scores(top_k_logp, end_flag)
top_k_index = mask_finished_preds(top_k_index, end_flag, self.eos)
# 2.3 Seconde beam prune: select topk score with history
scores = scores + top_k_logp # (B*N, N), broadcast add
scores = scores.view(batch_size, beam_size * beam_size) # (B, N*N)
scores, offset_k_index = scores.topk(k=beam_size) # (B, N)
scores = scores.view(-1, 1) # (B*N, 1)
# 2.4. Compute base index in top_k_index,
# regard top_k_index as (B*N*N),regard offset_k_index as (B*N),
# then find offset_k_index in top_k_index
base_k_index = paddle.arange(batch_size).view(-1, 1).repeat(
1, beam_size) # (B, N)
base_k_index = base_k_index * beam_size * beam_size
best_k_index = base_k_index.view(-1) + offset_k_index.view(
-1) # (B*N)
# 2.5 Update best hyps
best_k_pred = paddle.index_select(
top_k_index.view(-1), index=best_k_index, axis=0) # (B*N)
best_hyps_index = best_k_index // beam_size
last_best_k_hyps = paddle.index_select(
hyps, index=best_hyps_index, axis=0) # (B*N, i)
hyps = paddle.cat(
(last_best_k_hyps, best_k_pred.view(-1, 1)),
dim=1) # (B*N, i+1)
# 2.6 Update end flag
end_flag = paddle.eq(hyps[:, -1], self.eos).view(-1, 1)
# 3. Select best of best
scores = scores.view(batch_size, beam_size)
# TODO: length normalization
best_index = paddle.argmax(scores, axis=-1).long() # (B)
best_hyps_index = best_index + paddle.arange(
batch_size, dtype=paddle.long) * beam_size
best_hyps = paddle.index_select(hyps, index=best_hyps_index, axis=0)
best_hyps = best_hyps[:, 1:]
return best_hyps
@jit.export
def subsampling_rate(self) -> int:
""" Export interface for c++ call, return subsampling_rate of the
model
"""
return self.encoder.embed.subsampling_rate
@jit.export
def right_context(self) -> int:
""" Export interface for c++ call, return right_context of the model
"""
return self.encoder.embed.right_context
@jit.export
def sos_symbol(self) -> int:
""" Export interface for c++ call, return sos symbol id of the model
"""
return self.sos
@jit.export
def eos_symbol(self) -> int:
""" Export interface for c++ call, return eos symbol id of the model
"""
return self.eos
@jit.export
def forward_encoder_chunk(
self,
xs: paddle.Tensor,
offset: int,
required_cache_size: int,
subsampling_cache: Optional[paddle.Tensor]=None,
elayers_output_cache: Optional[List[paddle.Tensor]]=None,
conformer_cnn_cache: Optional[List[paddle.Tensor]]=None,
) -> Tuple[paddle.Tensor, paddle.Tensor, List[paddle.Tensor], List[
paddle.Tensor]]:
""" Export interface for c++ call, give input chunk xs, and return
output from time 0 to current chunk.
Args:
xs (paddle.Tensor): chunk input
subsampling_cache (Optional[paddle.Tensor]): subsampling cache
elayers_output_cache (Optional[List[paddle.Tensor]]):
transformer/conformer encoder layers output cache
conformer_cnn_cache (Optional[List[paddle.Tensor]]): conformer
cnn cache
Returns:
paddle.Tensor: output, it ranges from time 0 to current chunk.
paddle.Tensor: subsampling cache
List[paddle.Tensor]: attention cache
List[paddle.Tensor]: conformer cnn cache
"""
return self.encoder.forward_chunk(
xs, offset, required_cache_size, subsampling_cache,
elayers_output_cache, conformer_cnn_cache)
@jit.export
def ctc_activation(self, xs: paddle.Tensor) -> paddle.Tensor:
""" Export interface for c++ call, apply linear transform and log
softmax before ctc
Args:
xs (paddle.Tensor): encoder output
Returns:
paddle.Tensor: activation before ctc
"""
return self.ctc.log_softmax(xs)
@jit.export
def forward_attention_decoder(
self,
hyps: paddle.Tensor,
hyps_lens: paddle.Tensor,
encoder_out: paddle.Tensor, ) -> paddle.Tensor:
""" Export interface for c++ call, forward decoder with multiple
hypothesis from ctc prefix beam search and one encoder output
Args:
hyps (paddle.Tensor): hyps from ctc prefix beam search, already
pad sos at the begining, (B, T)
hyps_lens (paddle.Tensor): length of each hyp in hyps, (B)
encoder_out (paddle.Tensor): corresponding encoder output, (B=1, T, D)
Returns:
paddle.Tensor: decoder output, (B, L)
"""
assert encoder_out.size(0) == 1
num_hyps = hyps.size(0)
assert hyps_lens.size(0) == num_hyps
encoder_out = encoder_out.repeat(num_hyps, 1, 1)
# (B, 1, T)
encoder_mask = paddle.ones(
[num_hyps, 1, encoder_out.size(1)], dtype=paddle.bool)
# (num_hyps, max_hyps_len, vocab_size)
decoder_out, _ = self.decoder(encoder_out, encoder_mask, hyps,
hyps_lens)
decoder_out = paddle.nn.functional.log_softmax(decoder_out, dim=-1)
return decoder_out
@paddle.no_grad()
def decode(self,
feats: paddle.Tensor,
feats_lengths: paddle.Tensor,
text_feature: Dict[str, int],
decoding_method: str,
lang_model_path: str,
beam_alpha: float,
beam_beta: float,
beam_size: int,
cutoff_prob: float,
cutoff_top_n: int,
num_processes: int,
ctc_weight: float=0.0,
decoding_chunk_size: int=-1,
num_decoding_left_chunks: int=-1,
simulate_streaming: bool=False):
"""u2 decoding.
Args:
feats (Tenosr): audio features, (B, T, D)
feats_lengths (Tenosr): (B)
text_feature (TextFeaturizer): text feature object.
decoding_method (str): decoding mode, e.g.
'fullsentence',
'simultaneous'
lang_model_path (str): lm path.
beam_alpha (float): lm weight.
beam_beta (float): length penalty.
beam_size (int): beam size for search
cutoff_prob (float): for prune.
cutoff_top_n (int): for prune.
num_processes (int):
ctc_weight (float, optional): ctc weight for attention rescoring decode mode. Defaults to 0.0.
decoding_chunk_size (int, optional): decoding chunk size. Defaults to -1.
<0: for decoding, use full chunk.
>0: for decoding, use fixed chunk size as set.
0: used for training, it's prohibited here.
num_decoding_left_chunks (int, optional):
number of left chunks for decoding. Defaults to -1.
simulate_streaming (bool, optional): simulate streaming inference. Defaults to False.
Raises:
ValueError: when not support decoding_method.
Returns:
List[List[int]]: transcripts.
"""
batch_size = feats.size(0)
if decoding_method == 'fullsentence':
hyps = self.translate(
feats,
feats_lengths,
beam_size=beam_size,
decoding_chunk_size=decoding_chunk_size,
num_decoding_left_chunks=num_decoding_left_chunks,
simulate_streaming=simulate_streaming)
hyps = [hyp.tolist() for hyp in hyps]
else:
raise ValueError(f"Not support decoding method: {decoding_method}")
res = [text_feature.defeaturize(hyp) for hyp in hyps]
return res
class U2STModel(U2STBaseModel):
def __init__(self, configs: dict):
vocab_size, encoder, decoder = U2STModel._init_from_config(configs)
if isinstance(decoder, Tuple):
st_decoder, asr_decoder, ctc = decoder
super().__init__(
vocab_size=vocab_size,
encoder=encoder,
st_decoder=st_decoder,
decoder=asr_decoder,
ctc=ctc,
**configs['model_conf'])
else:
super().__init__(
vocab_size=vocab_size,
encoder=encoder,
st_decoder=decoder,
**configs['model_conf'])
@classmethod
def _init_from_config(cls, configs: dict):
"""init sub module for model.
Args:
configs (dict): config dict.
Raises:
ValueError: raise when using not support encoder type.
Returns:
int, nn.Layer, nn.Layer, nn.Layer: vocab size, encoder, decoder, ctc
"""
if configs['cmvn_file'] is not None:
mean, istd = load_cmvn(configs['cmvn_file'],
configs['cmvn_file_type'])
global_cmvn = GlobalCMVN(
paddle.to_tensor(mean, dtype=paddle.float),
paddle.to_tensor(istd, dtype=paddle.float))
else:
global_cmvn = None
input_dim = configs['input_dim']
vocab_size = configs['output_dim']
assert input_dim != 0, input_dim
assert vocab_size != 0, vocab_size
encoder_type = configs.get('encoder', 'transformer')
logger.info(f"U2 Encoder type: {encoder_type}")
if encoder_type == 'transformer':
encoder = TransformerEncoder(
input_dim, global_cmvn=global_cmvn, **configs['encoder_conf'])
elif encoder_type == 'conformer':
encoder = ConformerEncoder(
input_dim, global_cmvn=global_cmvn, **configs['encoder_conf'])
else:
raise ValueError(f"not support encoder type:{encoder_type}")
st_decoder = TransformerDecoder(vocab_size,
encoder.output_size(),
**configs['decoder_conf'])
asr_weight = configs['model_conf']['asr_weight']
logger.info(f"ASR Joint Training Weight: {asr_weight}")
if asr_weight > 0.:
decoder = TransformerDecoder(vocab_size,
encoder.output_size(),
**configs['decoder_conf'])
ctc = CTCDecoder(
odim=vocab_size,
enc_n_units=encoder.output_size(),
blank_id=0,
dropout_rate=0.0,
reduction=True, # sum
batch_average=True) # sum / batch_size
return vocab_size, encoder, (st_decoder, decoder, ctc)
else:
return vocab_size, encoder, st_decoder
@classmethod
def from_config(cls, configs: dict):
"""init model.
Args:
configs (dict): config dict.
Raises:
ValueError: raise when using not support encoder type.
Returns:
nn.Layer: U2STModel
"""
model = cls(configs)
return model
@classmethod
def from_pretrained(cls, dataloader, config, checkpoint_path):
"""Build a DeepSpeech2Model model from a pretrained model.
Args:
dataloader (paddle.io.DataLoader): not used.
config (yacs.config.CfgNode): model configs
checkpoint_path (Path or str): the path of pretrained model checkpoint, without extension name
Returns:
DeepSpeech2Model: The model built from pretrained result.
"""
config.defrost()
config.input_dim = dataloader.collate_fn.feature_size
config.output_dim = dataloader.collate_fn.vocab_size
config.freeze()
model = cls.from_config(config)
if checkpoint_path:
infos = checkpoint.load_parameters(
model, checkpoint_path=checkpoint_path)
logger.info(f"checkpoint info: {infos}")
layer_tools.summary(model)
return model
class U2STInferModel(U2STModel):
def __init__(self, configs: dict):
super().__init__(configs)
def forward(self,
feats,
feats_lengths,
decoding_chunk_size=-1,
num_decoding_left_chunks=-1,
simulate_streaming=False):
"""export model function
Args:
feats (Tensor): [B, T, D]
feats_lengths (Tensor): [B]
Returns:
List[List[int]]: best path result
"""
return self.translate(
feats,
feats_lengths,
decoding_chunk_size=decoding_chunk_size,
num_decoding_left_chunks=num_decoding_left_chunks,
simulate_streaming=simulate_streaming)

53
deepspeech/utils/bleu_score.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This module provides functions to calculate bleu score in different level.
e.g. wer for word-level, cer for char-level.
"""
import numpy as np
import sacrebleu
__all__ = ['bleu', 'char_bleu']
def bleu(hypothesis, reference):
"""Calculate BLEU. BLEU compares reference text and
hypothesis text in word-level using scarebleu.
:param reference: The reference sentences.
:type reference: list[list[str]]
:param hypothesis: The hypothesis sentence.
:type hypothesis: list[str]
:raises ValueError: If the reference length is zero.
"""
return sacrebleu.corpus_bleu(hypothesis, reference)
def char_bleu(hypothesis, reference):
"""Calculate BLEU. BLEU compares reference text and
hypothesis text in char-level using scarebleu.
:param reference: The reference sentences.
:type reference: list[list[str]]
:param hypothesis: The hypothesis sentence.
:type hypothesis: list[str]
:raises ValueError: If the reference number is zero.
"""
hypothesis =[' '.join(list(hyp.replace(' ', ''))) for hyp in hypothesis]
reference = [[' '.join(list(ref_i.replace(' ', ''))) for ref_i in ref ]for ref in reference ]
return sacrebleu.corpus_bleu(hypothesis, reference)

6
examples/dataset/ted_en_zh/.gitignore vendored
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*.tar.gz.*
manifest.*
*.md
EN-ZH/
train-split/
test-segment/

114
examples/dataset/ted_en_zh/ted_en_zh.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Prepare Ted-En-Zh speech translation dataset
Create manifest files from splited datased.
dev set: tst2010, test set: tst2015
Manifest file is a json-format file with each line containing the
meta data (i.e. audio filepath, transcript and audio duration)
of each audio file in the data set.
"""
import argparse
import codecs
import json
import os
import soundfile
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--src_dir",
default="",
type=str,
help="Directory to kaldi splited data. (default: %(default)s)")
parser.add_argument(
"--manifest_prefix",
default="manifest",
type=str,
help="Filepath prefix for output manifests. (default: %(default)s)")
args = parser.parse_args()
def create_manifest(data_dir, manifest_path_prefix):
print("Creating manifest %s ..." % manifest_path_prefix)
json_lines = []
data_types_infos = [('train', 'train-split/train-segment', 'En-Zh/train.en-zh'),
('dev', 'test-segment/tst2010', 'En-Zh/tst2010.en-zh'),
('test', 'test-segment/tst2015', 'En-Zh/tst2015.en-zh')]
for data_info in data_types_infos:
dtype, audio_relative_dir, text_relative_path = data_info
del json_lines[:]
total_sec = 0.0
total_text = 0.0
total_num = 0
text_path = os.path.join(data_dir, text_relative_path)
audio_dir = os.path.join(data_dir, audio_relative_dir)
for line in codecs.open(text_path, 'r', 'utf-8', errors='ignore'):
line = line.strip()
if len(line) < 1:
continue
audio_id, trancription, translation = line.split('\t')
utt = audio_id.split('.')[0]
audio_path = os.path.join(audio_dir, audio_id)
if os.path.exists(audio_path):
if os.path.getsize(audio_path) < 30000:
continue
audio_data, samplerate = soundfile.read(audio_path)
duration = float(len(audio_data) / samplerate)
json_lines.append(
json.dumps(
{
'utt': utt,
'feat': audio_path,
'feat_shape': (duration, ), # second
'text': " ".join(translation.split()),
'text1': " ".join(trancription.split())
},
ensure_ascii=False))
total_sec += duration
total_text += len(translation.split())
total_num += 1
if not total_num % 1000:
print(dtype, 'Processed:', total_num)
manifest_path = manifest_path_prefix + '.' + dtype + '.raw'
with codecs.open(manifest_path, 'w', 'utf-8') as fout:
for line in json_lines:
fout.write(line + '\n')
def prepare_dataset(src_dir, manifest_path=None):
"""create manifest file."""
if os.path.isdir(manifest_path):
manifest_path = os.path.join(manifest_path, 'manifest')
if manifest_path:
create_manifest(src_dir, manifest_path)
def main():
if args.src_dir.startswith('~'):
args.src_dir = os.path.expanduser(args.src_dir)
prepare_dataset(src_dir=args.src_dir, manifest_path=args.manifest_prefix)
print("manifest prepare done!")
if __name__ == '__main__':
main()

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examples/ted_en_zh/conf/transformer.yaml
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# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.train.tiny
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
min_input_len: 0.5 # second
max_input_len: 3000.0 # second
min_output_len: 0.0 # tokens
max_output_len: 400.0 # tokens
min_output_input_ratio: 0.01
max_output_input_ratio: 20.0
collator:
vocab_filepath: data/vocab.txt
unit_type: 'spm'
spm_model_prefix: data/bpe_unigram_8000
mean_std_filepath: ""
# augmentation_config: conf/augmentation.json
batch_size: 10
raw_wav: True # use raw_wav or kaldi feature
specgram_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 16000
max_freq: None
n_fft: None
stride_ms: 10.0
window_ms: 25.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file_type: "json"
# encoder related
encoder: transformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
asr_weight: 0.0
ctc_weight: 0.0
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
training:
n_epoch: 120
accum_grad: 2
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.004
weight_decay: 1e-06
scheduler: warmuplr # pytorch v1.1.0+ required
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 5
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 5
error_rate_type: char-bleu
decoding_method: fullsentence # 'fullsentence', 'simultaneous'
alpha: 2.5
beta: 0.3
beam_size: 10
cutoff_prob: 1.0
cutoff_top_n: 0
num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: False # simulate streaming inference. Defaults to False.

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examples/ted_en_zh/conf/transformer_joint_noam.yaml
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# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
min_input_len: 0.5 # second
max_input_len: 3000.0 # second
min_output_len: 0.0 # tokens
max_output_len: 400.0 # tokens
min_output_input_ratio: 0.01
max_output_input_ratio: 20.0
collator:
vocab_filepath: data/vocab.txt
unit_type: 'spm'
spm_model_prefix: data/bpe_unigram_8000
mean_std_filepath: ""
# augmentation_config: conf/augmentation.json
batch_size: 10
raw_wav: True # use raw_wav or kaldi feature
specgram_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 16000
max_freq: None
n_fft: None
stride_ms: 10.0
window_ms: 25.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file_type: "json"
# encoder related
encoder: transformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
asr_weight: 0.5
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
training:
n_epoch: 120
accum_grad: 2
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 2.5
weight_decay: 1e-06
scheduler: noam
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 5
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 5
error_rate_type: char-bleu
decoding_method: fullsentence # 'fullsentence', 'simultaneous'
alpha: 2.5
beta: 0.3
beam_size: 10
cutoff_prob: 1.0
cutoff_top_n: 0
num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: False # simulate streaming inference. Defaults to False.

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examples/ted_en_zh/local/data.sh
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#!/bin/bash
stage=-1
stop_stage=100
# bpemode (unigram or bpe)
nbpe=8000
bpemode=unigram
bpeprefix="data/bpe_${bpemode}_${nbpe}"
DATA_DIR=
source ${MAIN_ROOT}/utils/parse_options.sh
mkdir -p data
TARGET_DIR=${MAIN_ROOT}/examples/dataset
mkdir -p ${TARGET_DIR}
if [ ! -d ${SOURCE_DIR} ]; then
echo "Error: Dataset is not avaiable. Please download and unzip the dataset"
echo "Download Link: https://pan.baidu.com/s/18L-59wgeS96WkObISrytQQ Passwd: bva0"
echo "The tree of the directory should be:"
echo "."
echo "|-- En-Zh"
echo "|-- test-segment"
echo " |-- tst2010"
echo " |-- ..."
echo "|-- train-split"
echo " |-- train-segment"
echo "|-- README.md"
exit 1
fi
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
# generate manifests
python3 ${TARGET_DIR}/ted_en_zh/ted_en_zh.py \
--manifest_prefix="data/manifest" \
--src_dir="${DATA_DIR}"
echo "Complete raw data pre-process."
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# build vocabulary
python3 ${MAIN_ROOT}/utils/build_vocab.py \
--unit_type "spm" \
--spm_vocab_size=${nbpe} \
--spm_mode ${bpemode} \
--spm_model_prefix ${bpeprefix} \
--vocab_path="data/vocab.txt" \
--text_keys 'text' 'text1' \
--manifest_paths="data/manifest.train.raw"
if [ $? -ne 0 ]; then
echo "Build vocabulary failed. Terminated."
exit 1
fi
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# compute mean and stddev for normalizer
num_workers=$(nproc)
python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
--manifest_path="data/manifest.train.raw" \
--num_samples=-1 \
--specgram_type="fbank" \
--feat_dim=80 \
--delta_delta=false \
--sample_rate=16000 \
--stride_ms=10.0 \
--window_ms=25.0 \
--use_dB_normalization=False \
--num_workers=${num_workers} \
--output_path="data/mean_std.json"
if [ $? -ne 0 ]; then
echo "Compute mean and stddev failed. Terminated."
exit 1
fi
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# format manifest with tokenids, vocab size
for set in train dev test; do
{
python3 ${MAIN_ROOT}/utils/format_triplet_data.py \
--feat_type "raw" \
--cmvn_path "data/mean_std.json" \
--unit_type "spm" \
--spm_model_prefix ${bpeprefix} \
--vocab_path="data/vocab.txt" \
--manifest_path="data/manifest.${set}.raw" \
--output_path="data/manifest.${set}"
if [ $? -ne 0 ]; then
echo "Formt mnaifest failed. Terminated."
exit 1
fi
}&
done
wait
fi
echo "Ted En-Zh Data preparation done."
exit 0

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examples/ted_en_zh/local/test.sh
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#! /usr/bin/env bash
if [ $# != 2 ];then
echo "usage: ${0} config_path ckpt_path_prefix"
exit -1
fi
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
device=gpu
if [ ngpu == 0 ];then
device=cpu
fi
config_path=$1
ckpt_prefix=$2
for type in fullsentence; do
echo "decoding ${type}"
batch_size=32
python3 -u ${BIN_DIR}/test.py \
--device ${device} \
--nproc 1 \
--config ${config_path} \
--result_file ${ckpt_prefix}.${type}.rsl \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.decoding_method ${type} decoding.batch_size ${batch_size}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"
exit 1
fi
done
exit 0

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examples/ted_en_zh/local/train.sh
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#!/bin/bash
if [ $# != 2 ];then
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name"
exit -1
fi
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
config_path=$1
ckpt_name=$2
device=gpu
if [ ${ngpu} == 0 ];then
device=cpu
fi
echo "using ${device}..."
mkdir -p exp
python3 -u ${BIN_DIR}/train.py \
--device ${device} \
--nproc ${ngpu} \
--config ${config_path} \
--output exp/${ckpt_name}
if [ $? -ne 0 ]; then
echo "Failed in training!"
exit 1
fi
exit 0

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examples/ted_en_zh/path.sh
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export MAIN_ROOT=${PWD}/../../
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
export LC_ALL=C
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
MODEL=u2_st
export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin

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examples/ted_en_zh/run.sh
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#!/bin/bash
set -e
source path.sh
stage=0
stop_stage=100
conf_path=conf/transformer_joint_noam.yaml
avg_num=5
data_path=./TED-En-Zh # path to unzipped data
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
avg_ckpt=avg_${avg_num}
ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
echo "checkpoint name ${ckpt}"
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# prepare data
bash ./local/data.sh --DATA_DIR ${data_path} || exit -1
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `exp` dir
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./local/train.sh ${conf_path} ${ckpt}
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# avg n best model
../../utils/avg.sh exp/${ckpt}/checkpoints ${avg_num}
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# test ckpt avg_n
CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# export ckpt avg_n
CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
fi

13
utils/build_vocab.py
Unescape View File

@ -44,6 +44,11 @@ add_arg('manifest_paths', str,
"You can provide multiple manifest files.",
nargs='+',
required=True)
add_arg('text_keys', str,
'text',
"keys of the text in manifest for building vocabulary. "
"You can provide multiple k.",
nargs='+')
# bpe
add_arg('spm_vocab_size', int, 0, "Vocab size for spm.")
add_arg('spm_mode', str, 'unigram', "spm model type, e.g. unigram, spm, char, word. only need when `unit_type` is spm")
@ -58,10 +63,10 @@ def count_manifest(counter, text_feature, manifest_path):
line = text_feature.tokenize(line_json['text'])
counter.update(line)
def dump_text_manifest(fileobj, manifest_path):
def dump_text_manifest(fileobj, manifest_path, key='text'):
manifest_jsons = read_manifest(manifest_path)
for line_json in manifest_jsons:
fileobj.write(line_json['text'] + "\n")
fileobj.write(line_json[key] + "\n")
def main():
print_arguments(args, globals())
@ -78,7 +83,9 @@ def main():
fp = tempfile.NamedTemporaryFile(mode='w', delete=False)
for manifest_path in args.manifest_paths:
dump_text_manifest(fp, manifest_path)
text_keys = [args.text_keys] if type(args.text_keys) is not list else args.text_keys
for text_key in text_keys:
dump_text_manifest(fp, manifest_path, key=text_key)
fp.close()
# train
spm.SentencePieceTrainer.Train(

96
utils/format_triplet_data.py
Unescape View File

@ -0,0 +1,96 @@
#!/usr/bin/env python3
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""format manifest with more metadata."""
import argparse
import functools
import json
from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
from deepspeech.frontend.utility import load_cmvn
from deepspeech.frontend.utility import read_manifest
from deepspeech.utils.utility import add_arguments
from deepspeech.utils.utility import print_arguments
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('feat_type', str, "raw", "speech feature type, e.g. raw(wav, flac), kaldi")
add_arg('cmvn_path', str,
'examples/librispeech/data/mean_std.json',
"Filepath of cmvn.")
add_arg('unit_type', str, "char", "Unit type, e.g. char, word, spm")
add_arg('vocab_path', str,
'examples/librispeech/data/vocab.txt',
"Filepath of the vocabulary.")
add_arg('manifest_paths', str,
None,
"Filepaths of manifests for building vocabulary. "
"You can provide multiple manifest files.",
nargs='+',
required=True)
# bpe
add_arg('spm_model_prefix', str, None,
"spm model prefix, spm_model_%(bpe_mode)_%(count_threshold), only need when `unit_type` is spm")
add_arg('output_path', str, None, "filepath of formated manifest.", required=True)
# yapf: disable
args = parser.parse_args()
def main():
print_arguments(args, globals())
fout = open(args.output_path, 'w', encoding='utf-8')
# get feat dim
mean, std = load_cmvn(args.cmvn_path, filetype='json')
feat_dim = mean.shape[0] #(D)
print(f"Feature dim: {feat_dim}")
text_feature = TextFeaturizer(args.unit_type, args.vocab_path, args.spm_model_prefix)
vocab_size = text_feature.vocab_size
print(f"Vocab size: {vocab_size}")
count = 0
for manifest_path in args.manifest_paths:
manifest_jsons = read_manifest(manifest_path)
for line_json in manifest_jsons:
# text: translation text, text1: transcript text.
# Currently only support joint-vocab, will add separate vocabs setting.
line = line_json['text']
tokens = text_feature.tokenize(line)
tokenids = text_feature.featurize(line)
line_json['token'] = tokens
line_json['token_id'] = tokenids
line_json['token_shape'] = (len(tokenids), vocab_size)
line = line_json['text1']
tokens = text_feature.tokenize(line)
tokenids = text_feature.featurize(line)
line_json['token1'] = tokens
line_json['token_id1'] = tokenids
line_json['token_shape1'] = (len(tokenids), vocab_size)
feat_shape = line_json['feat_shape']
assert isinstance(feat_shape, (list, tuple)), type(feat_shape)
if args.feat_type == 'raw':
feat_shape.append(feat_dim)
else: # kaldi
raise NotImplementedError('no support kaldi feat now!')
fout.write(json.dumps(line_json) + '\n')
count += 1
print(f"Examples number: {count}")
fout.close()
if __name__ == '__main__':
main()
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