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614 lines
25 KiB
614 lines
25 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Contains U2 model."""
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import json
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import os
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import time
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from collections import defaultdict
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from collections import OrderedDict
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from contextlib import nullcontext
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import jsonlines
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import numpy as np
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import paddle
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from paddle import distributed as dist
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from paddle.nn.utils import clip_grad_norm_
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from paddlespeech.s2t.frontend.featurizer import TextFeaturizer
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from paddlespeech.s2t.io.dataloader import DataLoaderFactory
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from paddlespeech.s2t.models.u2 import U2Model
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from paddlespeech.s2t.training.optimizer import OptimizerFactory
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from paddlespeech.s2t.training.reporter import ObsScope
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from paddlespeech.s2t.training.reporter import report
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from paddlespeech.s2t.training.scheduler import LRSchedulerFactory
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from paddlespeech.s2t.training.timer import Timer
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from paddlespeech.s2t.training.trainer import Trainer
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from paddlespeech.s2t.utils import ctc_utils
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from paddlespeech.s2t.utils import error_rate
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from paddlespeech.s2t.utils import layer_tools
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from paddlespeech.s2t.utils import mp_tools
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from paddlespeech.s2t.utils.log import Log
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from paddlespeech.s2t.utils.utility import UpdateConfig
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logger = Log(__name__).getlog()
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class U2Trainer(Trainer):
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def __init__(self, config, args):
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super().__init__(config, args)
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def train_batch(self, batch_index, batch_data, scaler, msg):
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train_conf = self.config
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start = time.time()
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# forward
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utt, audio, audio_len, text, text_len = batch_data
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with paddle.amp.auto_cast(
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level=self.amp_level, enable=True if scaler else False):
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loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
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text_len)
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# loss div by `batch_size * accum_grad`
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loss /= train_conf.accum_grad
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losses_np = {'loss': float(loss) * train_conf.accum_grad}
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if attention_loss:
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losses_np['att_loss'] = float(attention_loss)
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if ctc_loss:
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losses_np['ctc_loss'] = float(ctc_loss)
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# loss backward
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if (batch_index + 1) % train_conf.accum_grad != 0:
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# Disable gradient synchronizations across DDP processes.
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# Within this context, gradients will be accumulated on module
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# variables, which will later be synchronized.
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# When using cpu w/o DDP, model does not have `no_sync`
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context = self.model.no_sync if (hasattr(self.model, "no_sync") and
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self.parallel) else nullcontext
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else:
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# Used for single gpu training and DDP gradient synchronization
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# processes.
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context = nullcontext
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with context():
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if scaler:
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scaler.scale(loss).backward()
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else:
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loss.backward()
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layer_tools.print_grads(self.model, print_func=None)
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# optimizer step
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if (batch_index + 1) % train_conf.accum_grad == 0:
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# do global grad clip
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if train_conf.global_grad_clip != 0:
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if scaler:
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scaler.unscale_(self.optimizer)
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# need paddlepaddle==develop or paddlepaddle>=2.5
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clip_grad_norm_(self.model.parameters(),
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train_conf.global_grad_clip)
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if scaler:
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scaler.step(self.optimizer)
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scaler.update()
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else:
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self.optimizer.step()
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self.optimizer.clear_grad()
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self.lr_scheduler.step()
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self.iteration += 1
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iteration_time = time.time() - start
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for k, v in losses_np.items():
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report(k, v)
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report("batch_size", self.config.batch_size)
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report("accum", train_conf.accum_grad)
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report("step_cost", iteration_time)
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if (batch_index + 1) % train_conf.accum_grad == 0:
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if dist.get_rank() == 0 and self.visualizer:
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losses_np_v = losses_np.copy()
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losses_np_v.update({"lr": self.lr_scheduler()})
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for key, val in losses_np_v.items():
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self.visualizer.add_scalar(
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tag='train/' + key, value=val, step=self.iteration - 1)
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@paddle.no_grad()
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def valid(self):
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self.model.eval()
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if not self.use_streamdata:
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logger.info(
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f"Valid Total Examples: {len(self.valid_loader.dataset)}")
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valid_losses = defaultdict(list)
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num_seen_utts = 1
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total_loss = 0.0
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for i, batch in enumerate(self.valid_loader):
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utt, audio, audio_len, text, text_len = batch
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loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
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text_len)
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if paddle.isfinite(loss):
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num_utts = batch[1].shape[0]
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num_seen_utts += num_utts
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total_loss += float(loss) * num_utts
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valid_losses['val_loss'].append(float(loss))
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if attention_loss:
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valid_losses['val_att_loss'].append(float(attention_loss))
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if ctc_loss:
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valid_losses['val_ctc_loss'].append(float(ctc_loss))
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if (i + 1) % self.config.log_interval == 0:
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valid_dump = {k: np.mean(v) for k, v in valid_losses.items()}
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valid_dump['val_history_loss'] = total_loss / num_seen_utts
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# logging
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msg = f"Valid: Rank: {dist.get_rank()}, "
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msg += "epoch: {}, ".format(self.epoch)
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msg += "step: {}, ".format(self.iteration)
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if not self.use_streamdata:
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msg += "batch: {}/{}, ".format(i + 1,
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len(self.valid_loader))
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msg += ', '.join('{}: {:>.6f}'.format(k, v)
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for k, v in valid_dump.items())
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logger.info(msg)
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logger.info('Rank {} Val info val_loss {}'.format(
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dist.get_rank(), total_loss / num_seen_utts))
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return total_loss, num_seen_utts
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def do_train(self):
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"""The training process control by step."""
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# !!!IMPORTANT!!!
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# Try to export the model by script, if fails, we should refine
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# the code to satisfy the script export requirements
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# script_model = paddle.jit.to_static(self.model)
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# script_model_path = str(self.checkpoint_dir / 'init')
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# paddle.jit.save(script_model, script_model_path)
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self.before_train()
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if not self.use_streamdata:
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logger.info(
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f"Train Total Examples: {len(self.train_loader.dataset)}")
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while self.epoch < self.config.n_epoch:
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with Timer("Epoch-Train Time Cost: {}"):
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self.model.train()
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try:
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data_start_time = time.time()
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for batch_index, batch in enumerate(self.train_loader):
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dataload_time = time.time() - data_start_time
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msg = "Train:"
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observation = OrderedDict()
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with ObsScope(observation):
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report("Rank", dist.get_rank())
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report("epoch", self.epoch)
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report('step', self.iteration)
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report("lr", self.lr_scheduler())
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self.train_batch(batch_index, batch, self.scaler,
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msg)
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self.after_train_batch()
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report('iter', batch_index + 1)
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if not self.use_streamdata:
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report('total', len(self.train_loader))
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report('reader_cost', dataload_time)
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observation['batch_cost'] = observation[
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'reader_cost'] + observation['step_cost']
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observation['samples'] = observation['batch_size']
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observation['ips,samples/s'] = observation[
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'batch_size'] / observation['batch_cost']
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for k, v in observation.items():
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msg += f" {k.split(',')[0]}: "
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msg += f"{v:>.8f}" if isinstance(v,
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float) else f"{v}"
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msg += f" {k.split(',')[1]}" if len(
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k.split(',')) == 2 else ""
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msg += ","
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msg = msg[:-1] # remove the last ","
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if (batch_index + 1) % self.config.log_interval == 0:
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logger.info(msg)
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data_start_time = time.time()
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except Exception as e:
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logger.error(e)
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raise e
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with Timer("Eval Time Cost: {}"):
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total_loss, num_seen_utts = self.valid()
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if dist.get_world_size() > 1:
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num_seen_utts = paddle.to_tensor(num_seen_utts)
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# the default operator in all_reduce function is sum.
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dist.all_reduce(num_seen_utts)
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total_loss = paddle.to_tensor(total_loss)
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dist.all_reduce(total_loss)
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cv_loss = total_loss / num_seen_utts
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cv_loss = float(cv_loss)
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else:
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cv_loss = total_loss / num_seen_utts
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logger.info(
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'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss))
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if self.visualizer:
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self.visualizer.add_scalar(
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tag='eval/cv_loss', value=cv_loss, step=self.epoch)
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self.visualizer.add_scalar(
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tag='eval/lr', value=self.lr_scheduler(), step=self.epoch)
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self.save(tag=self.epoch, infos={'val_loss': cv_loss})
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self.new_epoch()
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def setup_dataloader(self):
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config = self.config.clone()
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self.use_streamdata = config.get("use_stream_data", False)
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if self.train:
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self.train_loader = DataLoaderFactory.get_dataloader(
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'train', config, self.args)
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self.valid_loader = DataLoaderFactory.get_dataloader(
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'valid', config, self.args)
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logger.info("Setup train/valid Dataloader!")
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else:
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decode_batch_size = config.get('decode', dict()).get(
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'decode_batch_size', 1)
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self.test_loader = DataLoaderFactory.get_dataloader('test', config,
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self.args)
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self.align_loader = DataLoaderFactory.get_dataloader(
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'align', config, self.args)
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logger.info("Setup test/align Dataloader!")
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def setup_model(self):
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config = self.config
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model_conf = config
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with UpdateConfig(model_conf):
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if self.train:
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model_conf.input_dim = self.train_loader.feat_dim
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model_conf.output_dim = self.train_loader.vocab_size
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else:
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model_conf.input_dim = self.test_loader.feat_dim
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model_conf.output_dim = self.test_loader.vocab_size
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model = U2Model.from_config(model_conf)
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# For Mixed Precision Training
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self.use_amp = self.config.get("use_amp", True)
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self.amp_level = self.config.get("amp_level", "O1")
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if self.train and self.use_amp:
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self.scaler = paddle.amp.GradScaler(
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init_loss_scaling=self.config.get(
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"scale_loss", 32768.0)) #amp default num 32768.0
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#Set amp_level
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if self.amp_level == 'O2':
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model = paddle.amp.decorate(models=model, level=self.amp_level)
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else:
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self.scaler = None
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if self.parallel:
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model = paddle.DataParallel(model)
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logger.info(f"{model}")
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layer_tools.print_params(model, logger.info)
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self.model = model
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logger.info("Setup model!")
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if not self.train:
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return
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train_config = config
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optim_type = train_config.optim
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optim_conf = train_config.optim_conf
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scheduler_type = train_config.scheduler
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scheduler_conf = train_config.scheduler_conf
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scheduler_args = {
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"learning_rate": optim_conf.lr,
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"verbose": False,
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"warmup_steps": scheduler_conf.warmup_steps,
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"gamma": scheduler_conf.lr_decay,
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"d_model": model_conf.encoder_conf.output_size,
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}
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lr_scheduler = LRSchedulerFactory.from_args(scheduler_type,
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scheduler_args)
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def optimizer_args(
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config,
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parameters,
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lr_scheduler=None, ):
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train_config = config
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optim_type = train_config.optim
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optim_conf = train_config.optim_conf
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scheduler_type = train_config.scheduler
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scheduler_conf = train_config.scheduler_conf
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return {
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"weight_decay": optim_conf.weight_decay,
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"learning_rate": lr_scheduler
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if lr_scheduler else optim_conf.lr,
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"parameters": parameters,
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"epsilon": 1e-9 if optim_type == 'noam' else None,
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"beta1": 0.9 if optim_type == 'noam' else None,
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"beat2": 0.98 if optim_type == 'noam' else None,
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}
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optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler)
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optimizer = OptimizerFactory.from_args(optim_type, optimzer_args)
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self.optimizer = optimizer
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self.lr_scheduler = lr_scheduler
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logger.info("Setup optimizer/lr_scheduler!")
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class U2Tester(U2Trainer):
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def __init__(self, config, args):
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super().__init__(config, args)
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self.text_feature = TextFeaturizer(
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unit_type=self.config.unit_type,
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vocab=self.config.vocab_filepath,
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spm_model_prefix=self.config.spm_model_prefix)
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self.vocab_list = self.text_feature.vocab_list
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def id2token(self, texts, texts_len, text_feature):
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""" ord() id to chr() chr """
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trans = []
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for text, n in zip(texts, texts_len):
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n = n.numpy().item()
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ids = text[:n]
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trans.append(text_feature.defeaturize(ids.numpy().tolist()))
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return trans
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def compute_metrics(self,
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utts,
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audio,
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audio_len,
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texts,
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texts_len,
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fout=None):
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decode_config = self.config.decode
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errors_sum, len_refs, num_ins = 0.0, 0, 0
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errors_func = error_rate.char_errors if decode_config.error_rate_type == 'cer' else error_rate.word_errors
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error_rate_func = error_rate.cer if decode_config.error_rate_type == 'cer' else error_rate.wer
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reverse_weight = getattr(decode_config, 'reverse_weight', 0.0)
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start_time = time.time()
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target_transcripts = self.id2token(texts, texts_len, self.text_feature)
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result_transcripts, result_tokenids = self.model.decode(
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audio,
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audio_len,
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text_feature=self.text_feature,
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decoding_method=decode_config.decoding_method,
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beam_size=decode_config.beam_size,
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ctc_weight=decode_config.ctc_weight,
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decoding_chunk_size=decode_config.decoding_chunk_size,
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num_decoding_left_chunks=decode_config.num_decoding_left_chunks,
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simulate_streaming=decode_config.simulate_streaming,
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reverse_weight=reverse_weight)
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decode_time = time.time() - start_time
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for utt, target, result, rec_tids in zip(
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utts, target_transcripts, result_transcripts, result_tokenids):
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errors, len_ref = errors_func(target, result)
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errors_sum += errors
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len_refs += len_ref
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num_ins += 1
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if fout:
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fout.write({
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"utt": utt,
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"refs": [target],
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"hyps": [result],
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"hyps_tokenid": [rec_tids],
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})
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logger.info(f"Utt: {utt}")
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logger.info(f"Ref: {target}")
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logger.info(f"Hyp: {result}")
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logger.info("One example error rate [%s] = %f" % (
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decode_config.error_rate_type, error_rate_func(target, result)))
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return dict(
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errors_sum=errors_sum,
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len_refs=len_refs,
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num_ins=num_ins, # num examples
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error_rate=errors_sum / len_refs,
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error_rate_type=decode_config.error_rate_type,
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num_frames=audio_len.sum().numpy().item(),
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decode_time=decode_time)
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@mp_tools.rank_zero_only
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@paddle.no_grad()
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def test(self):
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assert self.args.result_file
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self.model.eval()
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if not self.use_streamdata:
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logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
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stride_ms = self.config.stride_ms
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error_rate_type = None
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errors_sum, len_refs, num_ins = 0.0, 0, 0
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num_frames = 0.0
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num_time = 0.0
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with jsonlines.open(self.args.result_file, 'w') as fout:
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for i, batch in enumerate(self.test_loader):
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metrics = self.compute_metrics(*batch, fout=fout)
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num_frames += metrics['num_frames']
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num_time += metrics["decode_time"]
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errors_sum += metrics['errors_sum']
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len_refs += metrics['len_refs']
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num_ins += metrics['num_ins']
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error_rate_type = metrics['error_rate_type']
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rtf = num_time / (num_frames * stride_ms)
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logger.info(
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"RTF: %f, Error rate [%s] (%d/?) = %f" %
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(rtf, error_rate_type, num_ins, errors_sum / len_refs))
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rtf = num_time / (num_frames * stride_ms)
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msg = "Test: "
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msg += "epoch: {}, ".format(self.epoch)
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msg += "step: {}, ".format(self.iteration)
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msg += "RTF: {}, ".format(rtf)
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msg += "Final error rate [%s] (%d/%d) = %f" % (
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error_rate_type, num_ins, num_ins, errors_sum / len_refs)
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logger.info(msg)
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# test meta results
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err_meta_path = os.path.splitext(self.args.result_file)[0] + '.err'
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err_type_str = "{}".format(error_rate_type)
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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.decode.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.decode.decode_batch_size,
|
|
self.config.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.clone(),
|
|
self.args.checkpoint_path)
|
|
batch_size = 1
|
|
feat_dim = self.test_loader.feat_dim
|
|
model_size = self.config.encoder_conf.output_size
|
|
num_left_chunks = -1
|
|
logger.info(
|
|
f"U2 Export Model Params: batch_size {batch_size}, feat_dim {feat_dim}, model_size {model_size}, num_left_chunks {num_left_chunks}"
|
|
)
|
|
|
|
return infer_model, (batch_size, feat_dim, model_size, num_left_chunks)
|
|
|
|
@paddle.no_grad()
|
|
def export(self):
|
|
infer_model, input_spec = self.load_inferspec()
|
|
infer_model.eval()
|
|
paddle.set_device('cpu')
|
|
|
|
assert isinstance(input_spec, (list, tuple)), type(input_spec)
|
|
batch_size, feat_dim, model_size, num_left_chunks = input_spec
|
|
|
|
######################## infer_model.forward_encoder_chunk ############
|
|
input_spec = [
|
|
# (T,), int16
|
|
paddle.static.InputSpec(shape=[None], dtype='int16'),
|
|
]
|
|
infer_model.forward_feature = paddle.jit.to_static(
|
|
infer_model.forward_feature, input_spec=input_spec)
|
|
|
|
######################### infer_model.forward_encoder_chunk ############
|
|
input_spec = [
|
|
# xs, (B, T, D)
|
|
paddle.static.InputSpec(
|
|
shape=[batch_size, None, feat_dim], dtype='float32'),
|
|
# offset, int, but need be tensor
|
|
paddle.static.InputSpec(shape=[1], dtype='int32'),
|
|
# required_cache_size, int
|
|
num_left_chunks,
|
|
# att_cache
|
|
paddle.static.InputSpec(
|
|
shape=[None, None, None, None], dtype='float32'),
|
|
# cnn_cache
|
|
paddle.static.InputSpec(
|
|
shape=[None, None, None, None], dtype='float32')
|
|
]
|
|
infer_model.forward_encoder_chunk = paddle.jit.to_static(
|
|
infer_model.forward_encoder_chunk, input_spec=input_spec)
|
|
|
|
######################### infer_model.ctc_activation ########################
|
|
input_spec = [
|
|
# encoder_out, (B,T,D)
|
|
paddle.static.InputSpec(
|
|
shape=[batch_size, None, model_size], dtype='float32')
|
|
]
|
|
infer_model.ctc_activation = paddle.jit.to_static(
|
|
infer_model.ctc_activation, input_spec=input_spec)
|
|
|
|
######################### infer_model.forward_attention_decoder ########################
|
|
reverse_weight = 0.3
|
|
input_spec = [
|
|
# hyps, (B, U)
|
|
paddle.static.InputSpec(shape=[None, None], dtype='int64'),
|
|
# hyps_lens, (B,)
|
|
paddle.static.InputSpec(shape=[None], dtype='int64'),
|
|
# encoder_out, (B,T,D)
|
|
paddle.static.InputSpec(
|
|
shape=[batch_size, None, model_size], dtype='float32'),
|
|
reverse_weight
|
|
]
|
|
infer_model.forward_attention_decoder = paddle.jit.to_static(
|
|
infer_model.forward_attention_decoder, input_spec=input_spec)
|
|
|
|
# jit save
|
|
logger.info(f"export save: {self.args.export_path}")
|
|
paddle.jit.save(
|
|
infer_model,
|
|
self.args.export_path,
|
|
combine_params=True,
|
|
skip_forward=True)
|
|
|
|
# test dy2static
|
|
def flatten(out):
|
|
if isinstance(out, paddle.Tensor):
|
|
return [out]
|
|
|
|
flatten_out = []
|
|
for var in out:
|
|
if isinstance(var, (list, tuple)):
|
|
flatten_out.extend(flatten(var))
|
|
else:
|
|
flatten_out.append(var)
|
|
return flatten_out
|
|
|
|
# forward_encoder_chunk dygraph
|
|
xs1 = paddle.full([1, 67, 80], 0.1, dtype='float32')
|
|
offset = paddle.to_tensor([0], dtype='int32')
|
|
required_cache_size = num_left_chunks
|
|
att_cache = paddle.zeros([0, 0, 0, 0])
|
|
cnn_cache = paddle.zeros([0, 0, 0, 0])
|
|
xs_d, att_cache_d, cnn_cache_d = infer_model.forward_encoder_chunk(
|
|
xs1, offset, required_cache_size, att_cache, cnn_cache)
|
|
|
|
# load static model
|
|
from paddle.jit.layer import Layer
|
|
layer = Layer()
|
|
logger.info(f"load export model: {self.args.export_path}")
|
|
layer.load(self.args.export_path, paddle.CPUPlace())
|
|
|
|
# forward_encoder_chunk static
|
|
xs1 = paddle.full([1, 67, 80], 0.1, dtype='float32')
|
|
offset = paddle.to_tensor([0], dtype='int32')
|
|
att_cache = paddle.zeros([0, 0, 0, 0])
|
|
cnn_cache = paddle.zeros([0, 0, 0, 0])
|
|
func = getattr(layer, 'forward_encoder_chunk')
|
|
xs_s, att_cache_s, cnn_cache_s = func(xs1, offset, att_cache, cnn_cache)
|
|
np.testing.assert_allclose(xs_d, xs_s, atol=1e-5)
|
|
np.testing.assert_allclose(att_cache_d, att_cache_s, atol=1e-4)
|
|
np.testing.assert_allclose(cnn_cache_d, cnn_cache_s, atol=1e-4)
|
|
# logger.info(f"forward_encoder_chunk output: {xs_s}")
|