# 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 DeepSpeech2 model.""" import time from collections import defaultdict from pathlib import Path from typing import Optional import numpy as np import paddle from paddle import distributed as dist from paddle.io import DataLoader from yacs.config import CfgNode from deepspeech.io.collator import SpeechCollator from deepspeech.io.dataset import ManifestDataset from deepspeech.io.sampler import SortagradBatchSampler from deepspeech.io.sampler import SortagradDistributedBatchSampler from deepspeech.models.deepspeech2 import DeepSpeech2InferModel from deepspeech.models.deepspeech2 import DeepSpeech2Model from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog from deepspeech.training.trainer import Trainer from deepspeech.utils import error_rate from deepspeech.utils import layer_tools from deepspeech.utils import mp_tools from deepspeech.utils.log import Autolog from deepspeech.utils.log import Log logger = Log(__name__).getlog() class DeepSpeech2Trainer(Trainer): @classmethod def params(cls, config: Optional[CfgNode]=None) -> CfgNode: # training config default = CfgNode( dict( lr=5e-4, # learning rate lr_decay=1.0, # learning rate decay weight_decay=1e-6, # the coeff of weight decay global_grad_clip=5.0, # the global norm clip n_epoch=50, # train epochs )) 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): start = time.time() utt, audio, audio_len, text, text_len = batch_data loss = self.model(audio, audio_len, text, text_len) loss.backward() layer_tools.print_grads(self.model, print_func=None) self.optimizer.step() self.optimizer.clear_grad() iteration_time = time.time() - start losses_np = { 'train_loss': float(loss), } msg += "train time: {:>.3f}s, ".format(iteration_time) msg += "batch size: {}, ".format(self.config.collator.batch_size) msg += ', '.join('{}: {:>.6f}'.format(k, v) for k, v in losses_np.items()) logger.info(msg) if dist.get_rank() == 0 and self.visualizer: for k, v in losses_np.items(): self.visualizer.add_scalar("train/{}".format(k), v, self.iteration) self.iteration += 1 @paddle.no_grad() def valid(self): logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}") self.model.eval() 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 = 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 (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 setup_model(self): config = self.config model = DeepSpeech2Model( feat_size=self.train_loader.collate_fn.feature_size, dict_size=self.train_loader.collate_fn.vocab_size, num_conv_layers=config.model.num_conv_layers, num_rnn_layers=config.model.num_rnn_layers, rnn_size=config.model.rnn_layer_size, use_gru=config.model.use_gru, share_rnn_weights=config.model.share_rnn_weights) if self.parallel: model = paddle.DataParallel(model) logger.info(f"{model}") layer_tools.print_params(model, logger.info) grad_clip = ClipGradByGlobalNormWithLog( config.training.global_grad_clip) lr_scheduler = paddle.optimizer.lr.ExponentialDecay( learning_rate=config.training.lr, gamma=config.training.lr_decay, verbose=True) optimizer = paddle.optimizer.Adam( learning_rate=lr_scheduler, parameters=model.parameters(), weight_decay=paddle.regularizer.L2Decay( config.training.weight_decay), grad_clip=grad_clip) self.model = model self.optimizer = optimizer self.lr_scheduler = lr_scheduler logger.info("Setup model/optimizer/lr_scheduler!") def setup_dataloader(self): config = self.config.clone() config.defrost() config.collator.keep_transcription_text = False config.data.manifest = config.data.train_manifest train_dataset = ManifestDataset.from_config(config) config.data.manifest = config.data.dev_manifest dev_dataset = ManifestDataset.from_config(config) if self.parallel: batch_sampler = SortagradDistributedBatchSampler( train_dataset, batch_size=config.collator.batch_size, num_replicas=None, rank=None, shuffle=True, drop_last=True, sortagrad=config.collator.sortagrad, shuffle_method=config.collator.shuffle_method) else: batch_sampler = SortagradBatchSampler( train_dataset, shuffle=True, batch_size=config.collator.batch_size, drop_last=True, sortagrad=config.collator.sortagrad, shuffle_method=config.collator.shuffle_method) collate_fn_train = SpeechCollator.from_config(config) config.collator.augmentation_config = "" collate_fn_dev = SpeechCollator.from_config(config) self.train_loader = DataLoader( train_dataset, batch_sampler=batch_sampler, collate_fn=collate_fn_train, num_workers=config.collator.num_workers) self.valid_loader = DataLoader( dev_dataset, batch_size=config.collator.batch_size, shuffle=False, drop_last=False, collate_fn=collate_fn_dev) logger.info("Setup train/valid Dataloader!") class DeepSpeech2Tester(DeepSpeech2Trainer): @classmethod def params(cls, config: Optional[CfgNode]=None) -> CfgNode: # testing 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='ctc_beam_search', # Decoding method. Options: ctc_beam_search, ctc_greedy error_rate_type='wer', # Error rate type for evaluation. Options `wer`, 'cer' num_proc_bsearch=8, # # of CPUs for beam search. beam_size=500, # Beam search width. batch_size=128, # decoding batch size )) if config is not None: config.merge_from_other_cfg(default) return default def __init__(self, config, args): super().__init__(config, args) def ordid2token(self, texts, texts_len): """ ord() id to chr() chr """ trans = [] for text, n in zip(texts, texts_len): n = n.numpy().item() ids = text[:n] trans.append(''.join([chr(i) for i in ids])) 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 vocab_list = self.test_loader.collate_fn.vocab_list target_transcripts = self.ordid2token(texts, texts_len) self.autolog.times.start() self.autolog.times.stamp() result_transcripts = self.model.decode( audio, audio_len, vocab_list, 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) self.autolog.times.stamp() self.autolog.times.stamp() self.autolog.times.end() for utt, target, result in zip(utts, target_transcripts, result_transcripts): errors, len_ref = errors_func(target, result) errors_sum += errors len_refs += len_ref num_ins += 1 if fout: fout.write(utt + " " + result + "\n") logger.info("\nTarget Transcription: %s\nOutput Transcription: %s" % (target, result)) logger.info("Current 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, error_rate=errors_sum / len_refs, error_rate_type=cfg.error_rate_type) @mp_tools.rank_zero_only @paddle.no_grad() def test(self): logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}") self.autolog = Autolog( batch_size=self.config.decoding.batch_size, model_name="deepspeech2", model_precision="fp32").getlog() self.model.eval() cfg = self.config error_rate_type = None errors_sum, len_refs, num_ins = 0.0, 0, 0 with open(self.args.result_file, 'w') as fout: for i, batch in enumerate(self.test_loader): utts, audio, audio_len, texts, texts_len = batch metrics = self.compute_metrics(utts, audio, audio_len, texts, texts_len, fout) errors_sum += metrics['errors_sum'] len_refs += metrics['len_refs'] num_ins += metrics['num_ins'] error_rate_type = metrics['error_rate_type'] logger.info("Error rate [%s] (%d/?) = %f" % (error_rate_type, num_ins, errors_sum / len_refs)) # logging msg = "Test: " msg += "epoch: {}, ".format(self.epoch) msg += "step: {}, ".format(self.iteration) msg += "Final error rate [%s] (%d/%d) = %f" % ( error_rate_type, num_ins, num_ins, errors_sum / len_refs) logger.info(msg) self.autolog.report() def run_test(self): self.resume_or_scratch() try: self.test() except KeyboardInterrupt: exit(-1) def export(self): infer_model = DeepSpeech2InferModel.from_pretrained( self.test_loader, self.config, self.args.checkpoint_path) infer_model.eval() feat_dim = self.test_loader.collate_fn.feature_size static_model = paddle.jit.to_static( infer_model, input_spec=[ paddle.static.InputSpec( shape=[None, None, feat_dim], dtype='float32'), # audio, [B,T,D] paddle.static.InputSpec(shape=[None], dtype='int64'), # audio_length, [B] ]) logger.info(f"Export code: {static_model.forward.code}") paddle.jit.save(static_model, self.args.export_path) def run_export(self): try: self.export() except KeyboardInterrupt: exit(-1) def setup(self): """Setup the experiment. """ paddle.set_device(self.args.device) self.setup_output_dir() self.setup_checkpointer() self.setup_dataloader() self.setup_model() self.iteration = 0 self.epoch = 0 def setup_model(self): config = self.config model = DeepSpeech2Model( feat_size=self.test_loader.collate_fn.feature_size, dict_size=self.test_loader.collate_fn.vocab_size, num_conv_layers=config.model.num_conv_layers, num_rnn_layers=config.model.num_rnn_layers, rnn_size=config.model.rnn_layer_size, use_gru=config.model.use_gru, share_rnn_weights=config.model.share_rnn_weights) self.model = model logger.info("Setup model!") def setup_dataloader(self): config = self.config.clone() config.defrost() # return raw text config.data.manifest = config.data.test_manifest # filter test examples, will cause less examples, but no mismatch with training # and can use large batch size , save training time, so filter test egs now. # config.data.min_input_len = 0.0 # second # config.data.max_input_len = float('inf') # second # config.data.min_output_len = 0.0 # tokens # config.data.max_output_len = float('inf') # tokens # config.data.min_output_input_ratio = 0.00 # config.data.max_output_input_ratio = float('inf') test_dataset = ManifestDataset.from_config(config) config.collator.keep_transcription_text = True config.collator.augmentation_config = "" # return text ord id self.test_loader = DataLoader( test_dataset, batch_size=config.decoding.batch_size, shuffle=False, drop_last=False, collate_fn=SpeechCollator.from_config(config)) logger.info("Setup test Dataloader!") def setup_output_dir(self): """Create a directory used for output. """ # output dir if self.args.output: output_dir = Path(self.args.output).expanduser() output_dir.mkdir(parents=True, exist_ok=True) else: output_dir = Path( self.args.checkpoint_path).expanduser().parent.parent output_dir.mkdir(parents=True, exist_ok=True) self.output_dir = output_dir