# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Contains U2 model.""" import json import os import time from collections import defaultdict from contextlib import nullcontext from typing import Optional import jsonlines import numpy as np import paddle from paddle import distributed as dist from yacs.config import CfgNode from paddlespeech.s2t.frontend.featurizer import TextFeaturizer from paddlespeech.s2t.frontend.utility import load_dict from paddlespeech.s2t.io.dataloader import BatchDataLoader from paddlespeech.s2t.models.u2 import U2Model from paddlespeech.s2t.training.optimizer import OptimizerFactory from paddlespeech.s2t.training.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() def get_cfg_defaults(): """Get a yacs CfgNode object with default values for my_project.""" # Return a clone so that the defaults will not be altered # This is for the "local variable" use pattern _C = CfgNode() _C.model = U2Model.params() _C.training = U2Trainer.params() _C.decoding = U2Tester.params() config = _C.clone() config.set_new_allowed(True) return config class U2Trainer(Trainer): @classmethod def params(cls, config: Optional[CfgNode]=None) -> CfgNode: # training config default = CfgNode( dict( n_epoch=50, # train epochs log_interval=100, # steps accum_grad=1, # accum grad by # steps checkpoint=dict( kbest_n=50, latest_n=5, ), )) if config is not None: config.merge_from_other_cfg(default) return default def __init__(self, config, args): super().__init__(config, args) def train_batch(self, batch_index, batch_data, msg): train_conf = self.config.training start = time.time() # forward utt, audio, audio_len, text, text_len = batch_data loss, attention_loss, ctc_loss = self.model(audio, audio_len, text, text_len) # loss div by `batch_size * accum_grad` loss /= train_conf.accum_grad losses_np = {'loss': float(loss) * train_conf.accum_grad} if attention_loss: losses_np['att_loss'] = float(attention_loss) if ctc_loss: losses_np['ctc_loss'] = float(ctc_loss) # loss backward if (batch_index + 1) % train_conf.accum_grad != 0: # Disable gradient synchronizations across DDP processes. # Within this context, gradients will be accumulated on module # variables, which will later be synchronized. context = self.model.no_sync if (hasattr(self.model, "no_sync") and self.parallel) else nullcontext else: # Used for single gpu training and DDP gradient synchronization # processes. context = nullcontext with context(): loss.backward() layer_tools.print_grads(self.model, print_func=None) # optimizer step if (batch_index + 1) % train_conf.accum_grad == 0: self.optimizer.step() self.optimizer.clear_grad() self.lr_scheduler.step() self.iteration += 1 iteration_time = time.time() - start if (batch_index + 1) % train_conf.log_interval == 0: msg += "train time: {:>.3f}s, ".format(iteration_time) msg += "batch size: {}, ".format(self.config.collator.batch_size) msg += "accum: {}, ".format(train_conf.accum_grad) msg += ', '.join('{}: {:>.6f}'.format(k, v) for k, v in losses_np.items()) logger.info(msg) if dist.get_rank() == 0 and self.visualizer: losses_np_v = losses_np.copy() losses_np_v.update({"lr": self.lr_scheduler()}) self.visualizer.add_scalars("step", losses_np_v, self.iteration - 1) @paddle.no_grad() def valid(self): self.model.eval() logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}") valid_losses = defaultdict(list) num_seen_utts = 1 total_loss = 0.0 for i, batch in enumerate(self.valid_loader): utt, audio, audio_len, text, text_len = batch loss, attention_loss, ctc_loss = self.model(audio, audio_len, text, text_len) if paddle.isfinite(loss): num_utts = batch[1].shape[0] num_seen_utts += num_utts total_loss += float(loss) * num_utts valid_losses['val_loss'].append(float(loss)) if attention_loss: valid_losses['val_att_loss'].append(float(attention_loss)) if ctc_loss: valid_losses['val_ctc_loss'].append(float(ctc_loss)) if (i + 1) % self.config.training.log_interval == 0: valid_dump = {k: np.mean(v) for k, v in valid_losses.items()} valid_dump['val_history_loss'] = total_loss / num_seen_utts # logging msg = f"Valid: Rank: {dist.get_rank()}, " msg += "epoch: {}, ".format(self.epoch) msg += "step: {}, ".format(self.iteration) msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader)) msg += ', '.join('{}: {:>.6f}'.format(k, v) for k, v in valid_dump.items()) logger.info(msg) logger.info('Rank {} Val info val_loss {}'.format( dist.get_rank(), total_loss / num_seen_utts)) return total_loss, num_seen_utts def do_train(self): """The training process control by step.""" # !!!IMPORTANT!!! # Try to export the model by script, if fails, we should refine # the code to satisfy the script export requirements # script_model = paddle.jit.to_static(self.model) # script_model_path = str(self.checkpoint_dir / 'init') # paddle.jit.save(script_model, script_model_path) self.before_train() logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}") while self.epoch < self.config.training.n_epoch: with Timer("Epoch-Train Time Cost: {}"): self.model.train() try: data_start_time = time.time() for batch_index, batch in enumerate(self.train_loader): dataload_time = time.time() - data_start_time msg = "Train: Rank: {}, ".format(dist.get_rank()) msg += "epoch: {}, ".format(self.epoch) msg += "step: {}, ".format(self.iteration) msg += "batch : {}/{}, ".format(batch_index + 1, len(self.train_loader)) msg += "lr: {:>.8f}, ".format(self.lr_scheduler()) msg += "data time: {:>.3f}s, ".format(dataload_time) self.train_batch(batch_index, batch, msg) self.after_train_batch() data_start_time = time.time() except Exception as e: logger.error(e) raise e with Timer("Eval Time Cost: {}"): total_loss, num_seen_utts = self.valid() if dist.get_world_size() > 1: num_seen_utts = paddle.to_tensor(num_seen_utts) # the default operator in all_reduce function is sum. dist.all_reduce(num_seen_utts) total_loss = paddle.to_tensor(total_loss) dist.all_reduce(total_loss) cv_loss = total_loss / num_seen_utts cv_loss = float(cv_loss) else: cv_loss = total_loss / num_seen_utts logger.info( 'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss)) if self.visualizer: self.visualizer.add_scalars( 'epoch', {'cv_loss': cv_loss, 'lr': self.lr_scheduler()}, self.epoch) self.save(tag=self.epoch, infos={'val_loss': cv_loss}) self.new_epoch() def setup_dataloader(self): config = self.config.clone() # train/valid dataset, return token ids self.train_loader = BatchDataLoader( json_file=config.data.train_manifest, train_mode=True, sortagrad=False, batch_size=config.collator.batch_size, maxlen_in=float('inf'), maxlen_out=float('inf'), minibatches=0, mini_batch_size=self.args.nprocs, batch_count='auto', batch_bins=0, batch_frames_in=0, batch_frames_out=0, batch_frames_inout=0, preprocess_conf=config.collator.augmentation_config, n_iter_processes=config.collator.num_workers, subsampling_factor=1, num_encs=1) self.valid_loader = BatchDataLoader( json_file=config.data.dev_manifest, train_mode=False, sortagrad=False, batch_size=config.collator.batch_size, maxlen_in=float('inf'), maxlen_out=float('inf'), minibatches=0, mini_batch_size=self.args.nprocs, batch_count='auto', batch_bins=0, batch_frames_in=0, batch_frames_out=0, batch_frames_inout=0, preprocess_conf=None, n_iter_processes=config.collator.num_workers, subsampling_factor=1, num_encs=1) # test dataset, return raw text self.test_loader = BatchDataLoader( json_file=config.data.test_manifest, train_mode=False, sortagrad=False, batch_size=config.decoding.batch_size, maxlen_in=float('inf'), maxlen_out=float('inf'), minibatches=0, mini_batch_size=1, batch_count='auto', batch_bins=0, batch_frames_in=0, batch_frames_out=0, batch_frames_inout=0, preprocess_conf=None, n_iter_processes=1, subsampling_factor=1, num_encs=1) self.align_loader = BatchDataLoader( json_file=config.data.test_manifest, train_mode=False, sortagrad=False, batch_size=config.decoding.batch_size, maxlen_in=float('inf'), maxlen_out=float('inf'), minibatches=0, mini_batch_size=1, batch_count='auto', batch_bins=0, batch_frames_in=0, batch_frames_out=0, batch_frames_inout=0, preprocess_conf=None, n_iter_processes=1, subsampling_factor=1, num_encs=1) logger.info("Setup train/valid/test/align Dataloader!") def setup_model(self): config = self.config # model model_conf = config.model with UpdateConfig(model_conf): model_conf.input_dim = self.train_loader.feat_dim model_conf.output_dim = self.train_loader.vocab_size model = U2Model.from_config(model_conf) if self.parallel: model = paddle.DataParallel(model) layer_tools.print_params(model, logger.info) # lr scheduler_conf = config.scheduler_conf scheduler_args = { "learning_rate": scheduler_conf.lr, "warmup_steps": scheduler_conf.warmup_steps, "gamma": scheduler_conf.lr_decay, "d_model": model_conf.encoder_conf.output_size, "verbose": False, } lr_scheduler = LRSchedulerFactory.from_args(config.scheduler, scheduler_args) # opt def optimizer_args( config, parameters, lr_scheduler=None, ): optim_conf = config.optim_conf return { "grad_clip": optim_conf.global_grad_clip, "weight_decay": optim_conf.weight_decay, "learning_rate": lr_scheduler, "parameters": parameters, } optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler) optimizer = OptimizerFactory.from_args(config.optim, optimzer_args) self.model = model self.lr_scheduler = lr_scheduler self.optimizer = optimizer logger.info("Setup model/optimizer/lr_scheduler!") class U2Tester(U2Trainer): @classmethod def params(cls, config: Optional[CfgNode]=None) -> CfgNode: # decoding config default = CfgNode( dict( alpha=2.5, # Coef of LM for beam search. beta=0.3, # Coef of WC for beam search. cutoff_prob=1.0, # Cutoff probability for pruning. cutoff_top_n=40, # Cutoff number for pruning. lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm', # Filepath for language model. decoding_method='attention', # Decoding method. Options: 'attention', 'ctc_greedy_search', # 'ctc_prefix_beam_search', 'attention_rescoring' error_rate_type='wer', # Error rate type for evaluation. Options `wer`, 'cer' num_proc_bsearch=8, # # of CPUs for beam search. beam_size=10, # Beam search width. batch_size=16, # decoding batch size ctc_weight=0.0, # ctc weight for attention rescoring decode mode. decoding_chunk_size=-1, # decoding chunk size. Defaults to -1. # <0: for decoding, use full chunk. # >0: for decoding, use fixed chunk size as set. # 0: used for training, it's prohibited here. num_decoding_left_chunks=-1, # number of left chunks for decoding. Defaults to -1. simulate_streaming=False, # simulate streaming inference. Defaults to False. )) if config is not None: config.merge_from_other_cfg(default) return default def __init__(self, config, args): super().__init__(config, args) self.text_feature = TextFeaturizer( unit_type=self.config.collator.unit_type, vocab_filepath=self.config.collator.vocab_filepath, spm_model_prefix=self.config.collator.spm_model_prefix) self.vocab_list = self.text_feature.vocab_list def id2token(self, texts, texts_len, text_feature): """ ord() id to chr() chr """ trans = [] for text, n in zip(texts, texts_len): n = n.numpy().item() ids = text[:n] trans.append(text_feature.defeaturize(ids.numpy().tolist())) return trans def compute_metrics(self, utts, audio, audio_len, texts, texts_len, fout=None): cfg = self.config.decoding errors_sum, len_refs, num_ins = 0.0, 0, 0 errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer start_time = time.time() target_transcripts = self.id2token(texts, texts_len, self.text_feature) result_transcripts, result_tokenids = self.model.decode( audio, audio_len, text_feature=self.text_feature, decoding_method=cfg.decoding_method, lang_model_path=cfg.lang_model_path, beam_alpha=cfg.alpha, beam_beta=cfg.beta, beam_size=cfg.beam_size, cutoff_prob=cfg.cutoff_prob, cutoff_top_n=cfg.cutoff_top_n, num_processes=cfg.num_proc_bsearch, ctc_weight=cfg.ctc_weight, decoding_chunk_size=cfg.decoding_chunk_size, num_decoding_left_chunks=cfg.num_decoding_left_chunks, simulate_streaming=cfg.simulate_streaming) decode_time = time.time() - start_time for i, (utt, target, result, rec_tids) in enumerate( zip(utts, target_transcripts, result_transcripts, result_tokenids)): errors, len_ref = errors_func(target, result) errors_sum += errors len_refs += len_ref num_ins += 1 if fout: fout.write({ "utt": utt, "refs": [target], "hyps": [result], "hyps_tokenid": [rec_tids], }) logger.info(f"Utt: {utt}") logger.info(f"Ref: {target}") logger.info(f"Hyp: {result}") logger.info("One example error rate [%s] = %f" % (cfg.error_rate_type, error_rate_func(target, result))) return dict( errors_sum=errors_sum, len_refs=len_refs, num_ins=num_ins, # num examples error_rate=errors_sum / len_refs, error_rate_type=cfg.error_rate_type, num_frames=audio_len.sum().numpy().item(), decode_time=decode_time) @mp_tools.rank_zero_only @paddle.no_grad() def test(self): assert self.args.result_file self.model.eval() logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}") stride_ms = self.config.collator.stride_ms error_rate_type = None errors_sum, len_refs, num_ins = 0.0, 0, 0 num_frames = 0.0 num_time = 0.0 with jsonlines.open(self.args.result_file, 'w') as fout: for i, batch in enumerate(self.test_loader): metrics = self.compute_metrics(*batch, fout=fout) num_frames += metrics['num_frames'] num_time += metrics["decode_time"] errors_sum += metrics['errors_sum'] len_refs += metrics['len_refs'] num_ins += metrics['num_ins'] error_rate_type = metrics['error_rate_type'] rtf = num_time / (num_frames * stride_ms) logger.info( "RTF: %f, Error rate [%s] (%d/?) = %f" % (rtf, error_rate_type, num_ins, errors_sum / len_refs)) rtf = num_time / (num_frames * stride_ms) msg = "Test: " msg += "epoch: {}, ".format(self.epoch) msg += "step: {}, ".format(self.iteration) msg += "RTF: {}, ".format(rtf) msg += "Final error rate [%s] (%d/%d) = %f" % ( error_rate_type, num_ins, num_ins, errors_sum / len_refs) logger.info(msg) # test meta results err_meta_path = os.path.splitext(self.args.result_file)[0] + '.err' err_type_str = "{}".format(error_rate_type) with open(err_meta_path, 'w') as f: data = json.dumps({ "epoch": self.epoch, "step": self.iteration, "rtf": rtf, error_rate_type: errors_sum / len_refs, "dataset_hour": (num_frames * stride_ms) / 1000.0 / 3600.0, "process_hour": num_time / 1000.0 / 3600.0, "num_examples": num_ins, "err_sum": errors_sum, "ref_len": len_refs, "decode_method": self.config.decoding.decoding_method, }) f.write(data + '\n') @paddle.no_grad() def align(self): ctc_utils.ctc_align(self.config, self.model, self.align_loader, self.config.decoding.batch_size, self.config.collator.stride_ms, self.vocab_list, self.args.result_file) def load_inferspec(self): """infer model and input spec. Returns: nn.Layer: inference model List[paddle.static.InputSpec]: input spec. """ from paddlespeech.s2t.models.u2 import U2InferModel infer_model = U2InferModel.from_pretrained(self.test_loader, self.config.model.clone(), self.args.checkpoint_path) feat_dim = self.test_loader.feat_dim input_spec = [ paddle.static.InputSpec(shape=[1, None, feat_dim], dtype='float32'), # audio, [B,T,D] paddle.static.InputSpec(shape=[1], dtype='int64'), # audio_length, [B] ] return infer_model, input_spec @paddle.no_grad() def export(self): infer_model, input_spec = self.load_inferspec() assert isinstance(input_spec, list), type(input_spec) infer_model.eval() static_model = paddle.jit.to_static(infer_model, input_spec=input_spec) logger.info(f"Export code: {static_model.forward.code}") paddle.jit.save(static_model, self.args.export_path) def setup_dict(self): # load dictionary for debug log self.args.char_list = load_dict(self.args.dict_path, "maskctc" in self.args.model_name) def setup(self): super().setup() self.setup_dict()