# 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 time
from collections import defaultdict
from contextlib import nullcontext
from typing import Optional
import jsonlines
import numpy as np
import paddle
from paddle import distributed as dist
from yacs.config import CfgNode
from deepspeech.frontend.featurizer import TextFeaturizer
from deepspeech.frontend.utility import load_dict
from deepspeech.io.dataloader import BatchDataLoader
from deepspeech.models.u2 import U2Model
from deepspeech.training.optimizer import OptimizerFactory
from deepspeech.training.scheduler import LRSchedulerFactory
from deepspeech.training.timer import Timer
from deepspeech.training.trainer import Trainer
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.log import Log
from deepspeech.utils.utility import UpdateConfig
logger = Log(__name__).getlog()
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
_C = CfgNode()
_C.model = U2Model.params()
_C.training = U2Trainer.params()
_C.decoding = U2Tester.params()
config = _C.clone()
config.set_new_allowed(True)
return config
class U2Trainer(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
checkpoint=dict(
kbest_n=50,
latest_n=5, ), ))
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()
# forward
utt, audio, audio_len, text, text_len = batch_data
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
losses_np = {'loss': float(loss) * train_conf.accum_grad}
if attention_loss:
losses_np['att_loss'] = float(attention_loss)
if ctc_loss:
losses_np['ctc_loss'] = float(ctc_loss)
# loss backward
if (batch_index + 1) % train_conf.accum_grad != 0:
# Disable gradient synchronizations across DDP processes.
# Within this context, gradients will be accumulated on module
# variables, which will later be synchronized.
context = self.model.no_sync if (hasattr(self.model, "no_sync") and
self.parallel) else nullcontext
else:
# Used for single gpu training and DDP gradient synchronization
# processes.
context = nullcontext
with context():
loss.backward()
layer_tools.print_grads(self.model, print_func=None)
# optimizer step
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
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(loss) * num_utts
valid_losses['val_loss'].append(float(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_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 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)
self.before_train()
logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
while self.epoch < self.config.training.n_epoch:
with Timer("Epoch-Train Time Cost: {}"):
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)
self.after_train_batch()
data_start_time = time.time()
except Exception as e:
logger.error(e)
raise e
with Timer("Eval Time Cost: {}"):
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()
# train/valid dataset, return token ids
self.train_loader = BatchDataLoader(
json_file=config.data.train_manifest,
train_mode=True,
sortagrad=False,
batch_size=config.collator.batch_size,
maxlen_in=float('inf'),
maxlen_out=float('inf'),
minibatches=0,
mini_batch_size=self.args.nprocs,
batch_count='auto',
batch_bins=0,
batch_frames_in=0,
batch_frames_out=0,
batch_frames_inout=0,
preprocess_conf=config.collator.augmentation_config,
n_iter_processes=config.collator.num_workers,
subsampling_factor=1,
num_encs=1)
self.valid_loader = BatchDataLoader(
json_file=config.data.dev_manifest,
train_mode=False,
sortagrad=False,
batch_size=config.collator.batch_size,
maxlen_in=float('inf'),
maxlen_out=float('inf'),
minibatches=0,
mini_batch_size=self.args.nprocs,
batch_count='auto',
batch_bins=0,
batch_frames_in=0,
batch_frames_out=0,
batch_frames_inout=0,
preprocess_conf=None,
n_iter_processes=1,
subsampling_factor=1,
num_encs=1)
# test dataset, return raw text
self.test_loader = BatchDataLoader(
json_file=config.data.test_manifest,
train_mode=False,
sortagrad=False,
batch_size=config.decoding.batch_size,
maxlen_in=float('inf'),
maxlen_out=float('inf'),
minibatches=0,
mini_batch_size=1,
batch_count='auto',
batch_bins=0,
batch_frames_in=0,
batch_frames_out=0,
batch_frames_inout=0,
preprocess_conf=None,
n_iter_processes=1,
subsampling_factor=1,
num_encs=1)
self.align_loader = BatchDataLoader(
json_file=config.data.test_manifest,
train_mode=False,
sortagrad=False,
batch_size=config.decoding.batch_size,
maxlen_in=float('inf'),
maxlen_out=float('inf'),
minibatches=0,
mini_batch_size=1,
batch_count='auto',
batch_bins=0,
batch_frames_in=0,
batch_frames_out=0,
batch_frames_inout=0,
preprocess_conf=None,
n_iter_processes=1,
subsampling_factor=1,
num_encs=1)
logger.info("Setup train/valid/test/align Dataloader!")
def setup_model(self):
config = self.config
# model
model_conf = config.model
with UpdateConfig(model_conf):
model_conf.input_dim = self.train_loader.feat_dim
model_conf.output_dim = self.train_loader.vocab_size
model = U2Model.from_config(model_conf)
if self.parallel:
model = paddle.DataParallel(model)
logger.info(f"{model}")
layer_tools.print_params(model, logger.info)
# lr
scheduler_conf = config.scheduler_conf
scheduler_args = {
"learning_rate": scheduler_conf.lr,
"warmup_steps": scheduler_conf.warmup_steps,
"gamma": scheduler_conf.lr_decay,
"d_model": model_conf.encoder_conf.output_size,
"verbose": False,
}
lr_scheduler = LRSchedulerFactory.from_args(config.scheduler,
scheduler_args)
# opt
def optimizer_args(
config,
parameters,
lr_scheduler=None, ):
optim_conf = config.optim_conf
return {
"grad_clip": optim_conf.global_grad_clip,
"weight_decay": optim_conf.weight_decay,
"learning_rate": lr_scheduler,
"parameters": parameters,
}
optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler)
optimizer = OptimizerFactory.from_args(config.optim, optimzer_args)
self.model = model
self.lr_scheduler = lr_scheduler
self.optimizer = optimizer
logger.info("Setup model/optimizer/lr_scheduler!")
class U2Tester(U2Trainer):
@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='wer', # Error rate type for evaluation. Options `wer`, 'cer'
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 id2token(self, texts, texts_len, text_feature):
""" ord() id to chr() chr """
trans = []
for text, n in zip(texts, texts_len):
n = n.numpy().item()
ids = text[:n]
trans.append(text_feature.defeaturize(ids.numpy().tolist()))
return trans
def compute_metrics(self,
utts,
audio,
audio_len,
texts,
texts_len,
fout=None):
cfg = self.config.decoding
errors_sum, len_refs, num_ins = 0.0, 0, 0
errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
start_time = time.time()
text_feature = TextFeaturizer(
unit_type=self.config.collator.unit_type,
vocab_filepath=self.config.collator.vocab_filepath,
spm_model_prefix=self.config.collator.spm_model_prefix)
target_transcripts = self.id2token(texts, texts_len, text_feature)
result_transcripts = 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, target_transcripts,
result_transcripts):
errors, len_ref = errors_func(target, result)
errors_sum += errors
len_refs += len_ref
num_ins += 1
if fout:
fout.write({"utt": utt, "ref": target, "hyp": result})
logger.info(f"Utt: {utt}")
logger.info(f"Ref: {target}")
logger.info(f"Hyp: {result}")
logger.info("One example error rate [%s] = %f" %
(cfg.error_rate_type, error_rate_func(target, result)))
return dict(
errors_sum=errors_sum,
len_refs=len_refs,
num_ins=num_ins, # num examples
error_rate=errors_sum / len_refs,
error_rate_type=cfg.error_rate_type,
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)}")
stride_ms = self.config.collator.stride_ms
error_rate_type = None
errors_sum, len_refs, num_ins = 0.0, 0, 0
num_frames = 0.0
num_time = 0.0
with jsonlines.open(self.args.result_file, 'w') as fout:
for i, batch in enumerate(self.test_loader):
metrics = self.compute_metrics(*batch, fout=fout)
num_frames += metrics['num_frames']
num_time += metrics["decode_time"]
errors_sum += metrics['errors_sum']
len_refs += metrics['len_refs']
num_ins += metrics['num_ins']
error_rate_type = metrics['error_rate_type']
rtf = num_time / (num_frames * stride_ms)
logger.info(
"RTF: %f, Error rate [%s] (%d/?) = %f" %
(rtf, error_rate_type, num_ins, errors_sum / len_refs))
rtf = num_time / (num_frames * stride_ms)
msg = "Test: "
msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration)
msg += "RTF: {}, ".format(rtf)
msg += "Final error rate [%s] (%d/%d) = %f" % (
error_rate_type, num_ins, num_ins, errors_sum / len_refs)
logger.info(msg)
# test meta results
err_meta_path = os.path.splitext(self.args.result_file)[0] + '.err'
err_type_str = "{}".format(error_rate_type)
with open(err_meta_path, 'w') as f:
data = json.dumps({
"epoch":
self.epoch,
"step":
self.iteration,
"rtf":
rtf,
error_rate_type:
errors_sum / len_refs,
"dataset_hour": (num_frames * stride_ms) / 1000.0 / 3600.0,
"process_hour":
num_time / 1000.0 / 3600.0,
"num_examples":
num_ins,
"err_sum":
errors_sum,
"ref_len":
len_refs,
"decode_method":
self.config.decoding.decoding_method,
})
f.write(data + '\n')
@paddle.no_grad()
def align(self):
ctc_utils.ctc_align(
self.model, self.align_loader, self.config.decoding.batch_size,
self.align_loader.collate_fn.stride_ms,
self.align_loader.collate_fn.vocab_list, self.args.result_file)
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.feat_dim
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
@paddle.no_grad()
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 setup_dict(self):
# load dictionary for debug log
self.args.char_list = load_dict(self.args.dict_path,
"maskctc" in self.args.model_name)
def setup(self):
super().setup()
self.setup_dict()