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PaddleSpeech/paddlespeech/s2t/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 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 paddle.io import DataLoader
from yacs.config import CfgNode
from paddlespeech.s2t.io.collator import SpeechCollator
from paddlespeech.s2t.io.collator import TripletSpeechCollator
from paddlespeech.s2t.io.dataset import ManifestDataset
from paddlespeech.s2t.io.sampler import SortagradBatchSampler
from paddlespeech.s2t.io.sampler import SortagradDistributedBatchSampler
from paddlespeech.s2t.models.u2_st import U2STModel
from paddlespeech.s2t.training.gradclip import ClipGradByGlobalNormWithLog
from paddlespeech.s2t.training.scheduler import WarmupLR
from paddlespeech.s2t.training.timer import Timer
from paddlespeech.s2t.training.trainer import Trainer
from paddlespeech.s2t.utils import bleu_score
from paddlespeech.s2t.utils import ctc_utils
from paddlespeech.s2t.utils import layer_tools
from paddlespeech.s2t.utils import mp_tools
from paddlespeech.s2t.utils.log import Log
from paddlespeech.s2t.utils.utility import UpdateConfig
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()
# forward
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
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()})
for key, val in losses_np_v.items():
self.visualizer.add_scalar(
tag="train/" + key, value=val, step=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 do_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_scalar(
tag='eval/cv_loss', value=cv_loss, step=self.epoch)
self.visualizer.add_scalar(
tag='eval/lr', value=self.lr_scheduler(), step=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
config.data.manifest = config.data.train_manifest
train_dataset = ManifestDataset.from_config(config)
config.data.manifest = config.data.dev_manifest
dev_dataset = ManifestDataset.from_config(config)
if config.model.model_conf.asr_weight > 0.:
Collator = TripletSpeechCollator
TestCollator = SpeechCollator
else:
TestCollator = Collator = SpeechCollator
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,
num_workers=config.collator.num_workers, )
# 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),
num_workers=config.collator.num_workers, )
# 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),
num_workers=config.collator.num_workers, )
logger.info("Setup train/valid/test/align Dataloader!")
def setup_model(self):
config = self.config
model_conf = config.model
with UpdateConfig(model_conf):
model_conf.input_dim = self.train_loader.collate_fn.feature_size
model_conf.output_dim = self.train_loader.collate_fn.vocab_size
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
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}")
grad_clip = ClipGradByGlobalNormWithLog(train_config.global_grad_clip)
weight_decay = paddle.regularizer.L2Decay(optim_conf.weight_decay)
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 translate(self, audio, audio_len):
""""E2E translation from extracted audio feature"""
cfg = self.config.decoding
text_feature = self.test_loader.collate_fn.text_feature
self.model.eval()
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,
word_reward=cfg.word_reward,
decoding_chunk_size=cfg.decoding_chunk_size,
num_decoding_left_chunks=cfg.num_decoding_left_chunks,
simulate_streaming=cfg.simulate_streaming)
return hyps
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)
]
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,
word_reward=cfg.word_reward,
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": utt, "ref": target, "hyp": result})
logger.info(f"Utt: {utt}")
logger.info(f"Ref: {target}")
logger.info(f"Hyp: {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 jsonlines.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')
@paddle.no_grad()
def align(self):
ctc_utils.ctc_align(self.config, self.model, self.align_loader,
self.config.decoding.batch_size,
self.config.collator.stride_ms, self.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 paddlespeech.s2t.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
@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)