# 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 import jsonlines import numpy as np import paddle from paddle import distributed as dist from paddlespeech.s2t.frontend.featurizer import TextFeaturizer from paddlespeech.s2t.io.dataloader import DataLoaderFactory 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): def __init__(self, config, args): super().__init__(config, args) def train_batch(self, batch_index, batch_data, msg): train_conf = self.config 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.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() if not self.use_streamdata: 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.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) if not self.use_streamdata: 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() if not self.use_streamdata: logger.info( f"Train Total Examples: {len(self.train_loader.dataset)}") while self.epoch < self.config.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) if not self.use_streamdata: 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,samples/s'] = 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.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() self.use_streamdata = config.get("use_stream_data", False) if self.train: self.train_loader = DataLoaderFactory.get_dataloader( 'train', config, self.args) self.valid_loader = DataLoaderFactory.get_dataloader( 'valid', config, self.args) logger.info("Setup train/valid Dataloader!") else: decode_batch_size = config.get('decode', dict()).get( 'decode_batch_size', 1) self.test_loader = DataLoaderFactory.get_dataloader('test', config, self.args) self.align_loader = DataLoaderFactory.get_dataloader( 'align', config, self.args) logger.info("Setup test/align Dataloader!") def setup_model(self): config = self.config model_conf = config 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 = 5538 model = U2Model.from_config(model_conf) # params = model.state_dict() # paddle.save(params, 'for_torch/test.pdparams') # exit() 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 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 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): def __init__(self, config, args): super().__init__(config, args) self.text_feature = TextFeaturizer( unit_type=self.config.unit_type, vocab=self.config.vocab_filepath, spm_model_prefix=self.config.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): decode_config = self.config.decode errors_sum, len_refs, num_ins = 0.0, 0, 0 errors_func = error_rate.char_errors if decode_config.error_rate_type == 'cer' else error_rate.word_errors error_rate_func = error_rate.cer if decode_config.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=decode_config.decoding_method, beam_size=decode_config.beam_size, ctc_weight=decode_config.ctc_weight, decoding_chunk_size=decode_config.decoding_chunk_size, num_decoding_left_chunks=decode_config.num_decoding_left_chunks, simulate_streaming=decode_config.simulate_streaming, reverse_weight=self.config.model_conf.reverse_weight) 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" % ( decode_config.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=decode_config.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() if not self.use_streamdata: logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}") stride_ms = self.config.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.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) 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)