You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
610 lines
25 KiB
610 lines
25 KiB
# 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 collections import OrderedDict
|
|
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 paddlespeech.s2t.frontend.featurizer import TextFeaturizer
|
|
from paddlespeech.s2t.io.dataloader import BatchDataLoader
|
|
from paddlespeech.s2t.models.u2 import U2Model
|
|
from paddlespeech.s2t.training.optimizer import OptimizerFactory
|
|
from paddlespeech.s2t.training.reporter import ObsScope
|
|
from paddlespeech.s2t.training.reporter import report
|
|
from paddlespeech.s2t.training.scheduler import LRSchedulerFactory
|
|
from paddlespeech.s2t.training.timer import Timer
|
|
from paddlespeech.s2t.training.trainer import Trainer
|
|
from paddlespeech.s2t.utils import ctc_utils
|
|
from paddlespeech.s2t.utils import error_rate
|
|
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 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
|
|
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
|
|
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.
|
|
# When using cpu w/o DDP, model does not have `no_sync`
|
|
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
|
|
|
|
for k, v in losses_np.items():
|
|
report(k, v)
|
|
report("batch_size", self.config.collator.batch_size)
|
|
report("accum", train_conf.accum_grad)
|
|
report("step_cost", iteration_time)
|
|
|
|
if (batch_index + 1) % train_conf.accum_grad == 0:
|
|
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
|
|
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 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:"
|
|
observation = OrderedDict()
|
|
with ObsScope(observation):
|
|
report("Rank", dist.get_rank())
|
|
report("epoch", self.epoch)
|
|
report('step', self.iteration)
|
|
report("lr", self.lr_scheduler())
|
|
self.train_batch(batch_index, batch, msg)
|
|
self.after_train_batch()
|
|
report('iter', batch_index + 1)
|
|
report('total', len(self.train_loader))
|
|
report('reader_cost', dataload_time)
|
|
observation['batch_cost'] = observation[
|
|
'reader_cost'] + observation['step_cost']
|
|
observation['samples'] = observation['batch_size']
|
|
observation['ips,sent./sec'] = observation[
|
|
'batch_size'] / observation['batch_cost']
|
|
for k, v in observation.items():
|
|
msg += f" {k.split(',')[0]}: "
|
|
msg += f"{v:>.8f}" if isinstance(v,
|
|
float) else f"{v}"
|
|
msg += f" {k.split(',')[1]}" if len(
|
|
k.split(',')) == 2 else ""
|
|
msg += ","
|
|
msg = msg[:-1] # remove the last ","
|
|
if (batch_index + 1
|
|
) % self.config.training.log_interval == 0:
|
|
logger.info(msg)
|
|
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()
|
|
|
|
if self.train:
|
|
# 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.ngpu,
|
|
batch_count='auto',
|
|
batch_bins=0,
|
|
batch_frames_in=0,
|
|
batch_frames_out=0,
|
|
batch_frames_inout=0,
|
|
preprocess_conf=config.collator.
|
|
augmentation_config, # aug will be off when train_mode=False
|
|
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.ngpu,
|
|
batch_count='auto',
|
|
batch_bins=0,
|
|
batch_frames_in=0,
|
|
batch_frames_out=0,
|
|
batch_frames_inout=0,
|
|
preprocess_conf=config.collator.
|
|
augmentation_config, # aug will be off when train_mode=False
|
|
n_iter_processes=config.collator.num_workers,
|
|
subsampling_factor=1,
|
|
num_encs=1)
|
|
logger.info("Setup train/valid Dataloader!")
|
|
else:
|
|
# 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=config.collator.
|
|
augmentation_config, # aug will be off when train_mode=False
|
|
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=config.collator.
|
|
augmentation_config, # aug will be off when train_mode=False
|
|
n_iter_processes=1,
|
|
subsampling_factor=1,
|
|
num_encs=1)
|
|
logger.info("Setup test/align Dataloader!")
|
|
|
|
def setup_model(self):
|
|
config = self.config
|
|
model_conf = config.model
|
|
|
|
with UpdateConfig(model_conf):
|
|
if self.train:
|
|
model_conf.input_dim = self.train_loader.feat_dim
|
|
model_conf.output_dim = self.train_loader.vocab_size
|
|
else:
|
|
model_conf.input_dim = self.test_loader.feat_dim
|
|
model_conf.output_dim = self.test_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)
|
|
self.model = model
|
|
logger.info("Setup model!")
|
|
|
|
if not self.train:
|
|
return
|
|
|
|
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
|
|
|
|
scheduler_args = {
|
|
"learning_rate": optim_conf.lr,
|
|
"verbose": False,
|
|
"warmup_steps": scheduler_conf.warmup_steps,
|
|
"gamma": scheduler_conf.lr_decay,
|
|
"d_model": model_conf.encoder_conf.output_size,
|
|
}
|
|
lr_scheduler = LRSchedulerFactory.from_args(scheduler_type,
|
|
scheduler_args)
|
|
|
|
def optimizer_args(
|
|
config,
|
|
parameters,
|
|
lr_scheduler=None, ):
|
|
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
|
|
return {
|
|
"grad_clip": train_config.global_grad_clip,
|
|
"weight_decay": optim_conf.weight_decay,
|
|
"learning_rate": lr_scheduler
|
|
if lr_scheduler else optim_conf.lr,
|
|
"parameters": parameters,
|
|
"epsilon": 1e-9 if optim_type == 'noam' else None,
|
|
"beta1": 0.9 if optim_type == 'noam' else None,
|
|
"beat2": 0.98 if optim_type == 'noam' else None,
|
|
}
|
|
|
|
optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler)
|
|
optimizer = OptimizerFactory.from_args(optim_type, optimzer_args)
|
|
|
|
self.optimizer = optimizer
|
|
self.lr_scheduler = lr_scheduler
|
|
logger.info("Setup 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)
|
|
self.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)
|
|
self.vocab_list = self.text_feature.vocab_list
|
|
|
|
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()
|
|
target_transcripts = self.id2token(texts, texts_len, self.text_feature)
|
|
result_transcripts, result_tokenids = self.model.decode(
|
|
audio,
|
|
audio_len,
|
|
text_feature=self.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, rec_tids in zip(
|
|
utts, target_transcripts, result_transcripts, result_tokenids):
|
|
errors, len_ref = errors_func(target, result)
|
|
errors_sum += errors
|
|
len_refs += len_ref
|
|
num_ins += 1
|
|
if fout:
|
|
fout.write({
|
|
"utt": utt,
|
|
"refs": [target],
|
|
"hyps": [result],
|
|
"hyps_tokenid": [rec_tids],
|
|
})
|
|
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.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.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)
|