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591 lines
24 KiB
591 lines
24 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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from typing import Optional
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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.io import DataLoader
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from yacs.config import CfgNode
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from paddlespeech.s2t.io.collator import SpeechCollator
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from paddlespeech.s2t.io.dataset import ManifestDataset
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from paddlespeech.s2t.io.sampler import SortagradBatchSampler
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from paddlespeech.s2t.io.sampler import SortagradDistributedBatchSampler
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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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@classmethod
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def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
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# training config
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default = CfgNode(
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dict(
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n_epoch=50, # train epochs
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log_interval=100, # steps
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accum_grad=1, # accum grad by # steps
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global_grad_clip=5.0, # the global norm clip
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))
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default.optim = 'adam'
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default.optim_conf = CfgNode(
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dict(
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lr=5e-4, # learning rate
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weight_decay=1e-6, # the coeff of weight decay
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))
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default.scheduler = 'warmuplr'
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default.scheduler_conf = CfgNode(
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dict(
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warmup_steps=25000,
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lr_decay=1.0, # learning rate decay
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))
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if config is not None:
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config.merge_from_other_cfg(default)
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return default
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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, msg):
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train_conf = self.config.training
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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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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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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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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.collator.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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self.visualizer.add_scalars("step", losses_np_v,
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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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logger.info(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.training.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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msg += "batch: {}/{}, ".format(i + 1, 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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logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
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while self.epoch < self.config.training.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, msg)
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self.after_train_batch()
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report('iter', batch_index + 1)
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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[sent./sec]'] = 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}: "
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msg += f"{v:>.8f}" if isinstance(v,
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float) else f"{v}"
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msg += ","
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msg = msg[:-1] # remove the last ","
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if (batch_index + 1
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) % self.config.training.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_scalars(
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'epoch', {'cv_loss': cv_loss,
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'lr': self.lr_scheduler()}, 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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config.defrost()
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config.collator.keep_transcription_text = False
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# train/valid dataset, return token ids
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config.data.manifest = config.data.train_manifest
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train_dataset = ManifestDataset.from_config(config)
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config.data.manifest = config.data.dev_manifest
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dev_dataset = ManifestDataset.from_config(config)
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collate_fn_train = SpeechCollator.from_config(config)
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config.collator.augmentation_config = ""
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collate_fn_dev = SpeechCollator.from_config(config)
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if self.parallel:
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batch_sampler = SortagradDistributedBatchSampler(
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train_dataset,
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batch_size=config.collator.batch_size,
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num_replicas=None,
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rank=None,
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shuffle=True,
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drop_last=True,
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sortagrad=config.collator.sortagrad,
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shuffle_method=config.collator.shuffle_method)
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else:
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batch_sampler = SortagradBatchSampler(
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train_dataset,
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shuffle=True,
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batch_size=config.collator.batch_size,
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drop_last=True,
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sortagrad=config.collator.sortagrad,
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shuffle_method=config.collator.shuffle_method)
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self.train_loader = DataLoader(
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train_dataset,
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batch_sampler=batch_sampler,
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collate_fn=collate_fn_train,
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num_workers=config.collator.num_workers, )
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self.valid_loader = DataLoader(
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dev_dataset,
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batch_size=config.collator.batch_size,
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shuffle=False,
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drop_last=False,
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collate_fn=collate_fn_dev,
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num_workers=config.collator.num_workers, )
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# test dataset, return raw text
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config.data.manifest = config.data.test_manifest
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# filter test examples, will cause less examples, but no mismatch with training
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# and can use large batch size , save training time, so filter test egs now.
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config.data.min_input_len = 0.0 # second
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config.data.max_input_len = float('inf') # second
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config.data.min_output_len = 0.0 # tokens
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config.data.max_output_len = float('inf') # tokens
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config.data.min_output_input_ratio = 0.00
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config.data.max_output_input_ratio = float('inf')
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test_dataset = ManifestDataset.from_config(config)
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# return text ord id
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config.collator.keep_transcription_text = True
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config.collator.augmentation_config = ""
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self.test_loader = DataLoader(
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test_dataset,
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batch_size=config.decoding.batch_size,
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shuffle=False,
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drop_last=False,
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collate_fn=SpeechCollator.from_config(config),
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num_workers=config.collator.num_workers, )
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# return text token id
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config.collator.keep_transcription_text = False
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self.align_loader = DataLoader(
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test_dataset,
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batch_size=config.decoding.batch_size,
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shuffle=False,
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drop_last=False,
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collate_fn=SpeechCollator.from_config(config),
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num_workers=config.collator.num_workers, )
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logger.info("Setup train/valid/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.model
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with UpdateConfig(model_conf):
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model_conf.input_dim = self.train_loader.collate_fn.feature_size
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model_conf.output_dim = self.train_loader.collate_fn.vocab_size
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model = U2Model.from_config(model_conf)
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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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train_config = config.training
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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.training
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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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"grad_clip": train_config.global_grad_clip,
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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.model = model
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self.optimizer = optimizer
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self.lr_scheduler = lr_scheduler
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logger.info("Setup model/optimizer/lr_scheduler!")
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class U2Tester(U2Trainer):
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@classmethod
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def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
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# decoding config
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default = CfgNode(
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dict(
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alpha=2.5, # Coef of LM for beam search.
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beta=0.3, # Coef of WC for beam search.
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cutoff_prob=1.0, # Cutoff probability for pruning.
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cutoff_top_n=40, # Cutoff number for pruning.
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lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm', # Filepath for language model.
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decoding_method='attention', # Decoding method. Options: 'attention', 'ctc_greedy_search',
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# 'ctc_prefix_beam_search', 'attention_rescoring'
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error_rate_type='wer', # Error rate type for evaluation. Options `wer`, 'cer'
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num_proc_bsearch=8, # # of CPUs for beam search.
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beam_size=10, # Beam search width.
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batch_size=16, # decoding batch size
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ctc_weight=0.0, # ctc weight for attention rescoring decode mode.
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decoding_chunk_size=-1, # decoding chunk size. Defaults to -1.
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# <0: for decoding, use full chunk.
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# >0: for decoding, use fixed chunk size as set.
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# 0: used for training, it's prohibited here.
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num_decoding_left_chunks=-1, # number of left chunks for decoding. Defaults to -1.
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simulate_streaming=False, # simulate streaming inference. Defaults to False.
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))
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if config is not None:
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config.merge_from_other_cfg(default)
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return default
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def __init__(self, config, args):
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super().__init__(config, args)
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def ordid2token(self, texts, texts_len):
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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(''.join([chr(i) for i in ids]))
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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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cfg = self.config.decoding
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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 cfg.error_rate_type == 'cer' else error_rate.word_errors
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error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
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start_time = time.time()
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text_feature = self.test_loader.collate_fn.text_feature
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target_transcripts = self.ordid2token(texts, texts_len)
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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=text_feature,
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decoding_method=cfg.decoding_method,
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lang_model_path=cfg.lang_model_path,
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beam_alpha=cfg.alpha,
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beam_beta=cfg.beta,
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beam_size=cfg.beam_size,
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cutoff_prob=cfg.cutoff_prob,
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cutoff_top_n=cfg.cutoff_top_n,
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num_processes=cfg.num_proc_bsearch,
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ctc_weight=cfg.ctc_weight,
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decoding_chunk_size=cfg.decoding_chunk_size,
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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.test_loader.collate_fn.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.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 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)
|