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423 lines
16 KiB
423 lines
16 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 DeepSpeech2 model."""
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import time
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from collections import defaultdict
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from pathlib import Path
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from typing import Optional
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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 deepspeech.io.collator import SpeechCollator
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from deepspeech.io.dataset import ManifestDataset
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from deepspeech.io.sampler import SortagradBatchSampler
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from deepspeech.io.sampler import SortagradDistributedBatchSampler
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from deepspeech.models.ds2 import DeepSpeech2InferModel
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from deepspeech.models.ds2 import DeepSpeech2Model
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from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
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from deepspeech.training.trainer import Trainer
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from deepspeech.utils import error_rate
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from deepspeech.utils import layer_tools
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from deepspeech.utils import mp_tools
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from deepspeech.utils.log import Autolog
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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class DeepSpeech2Trainer(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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lr=5e-4, # learning rate
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lr_decay=1.0, # learning rate decay
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weight_decay=1e-6, # the coeff of weight decay
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global_grad_clip=5.0, # the global norm clip
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n_epoch=50, # train epochs
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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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start = time.time()
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utt, audio, audio_len, text, text_len = batch_data
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loss = self.model(audio, audio_len, text, text_len)
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loss.backward()
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layer_tools.print_grads(self.model, print_func=None)
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self.optimizer.step()
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self.optimizer.clear_grad()
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iteration_time = time.time() - start
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losses_np = {
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'train_loss': float(loss),
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}
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msg += "train time: {:>.3f}s, ".format(iteration_time)
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msg += "batch size: {}, ".format(self.config.collator.batch_size)
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msg += ', '.join('{}: {:>.6f}'.format(k, v)
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for k, v in losses_np.items())
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logger.info(msg)
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if dist.get_rank() == 0 and self.visualizer:
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for k, v in losses_np.items():
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self.visualizer.add_scalar("train/{}".format(k), v,
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self.iteration)
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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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logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
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self.model.eval()
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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 = self.model(audio, audio_len, text, 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 (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 setup_model(self):
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config = self.config
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model = DeepSpeech2Model(
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feat_size=self.train_loader.collate_fn.feature_size,
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dict_size=self.train_loader.collate_fn.vocab_size,
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num_conv_layers=config.model.num_conv_layers,
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num_rnn_layers=config.model.num_rnn_layers,
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rnn_size=config.model.rnn_layer_size,
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use_gru=config.model.use_gru,
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share_rnn_weights=config.model.share_rnn_weights,
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apply_online=config.model.apply_online)
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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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grad_clip = ClipGradByGlobalNormWithLog(
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config.training.global_grad_clip)
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lr_scheduler = paddle.optimizer.lr.ExponentialDecay(
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learning_rate=config.training.lr,
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gamma=config.training.lr_decay,
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verbose=True)
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optimizer = paddle.optimizer.Adam(
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learning_rate=lr_scheduler,
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parameters=model.parameters(),
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weight_decay=paddle.regularizer.L2Decay(
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config.training.weight_decay),
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grad_clip=grad_clip)
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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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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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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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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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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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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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logger.info("Setup train/valid Dataloader!")
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class DeepSpeech2Tester(DeepSpeech2Trainer):
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@classmethod
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def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
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# testing 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='ctc_beam_search', # Decoding method. Options: ctc_beam_search, ctc_greedy
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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=500, # Beam search width.
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batch_size=128, # decoding batch size
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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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vocab_list = self.test_loader.collate_fn.vocab_list
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target_transcripts = self.ordid2token(texts, texts_len)
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self.autolog.times.start()
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self.autolog.times.stamp()
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result_transcripts = self.model.decode(
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audio,
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audio_len,
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vocab_list,
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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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self.autolog.times.stamp()
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self.autolog.times.stamp()
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self.autolog.times.end()
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for utt, target, result in zip(utts, target_transcripts,
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result_transcripts):
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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(utt + " " + result + "\n")
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logger.info("\nTarget Transcription: %s\nOutput Transcription: %s" %
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(target, result))
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logger.info("Current error rate [%s] = %f" %
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(cfg.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,
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error_rate=errors_sum / len_refs,
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error_rate_type=cfg.error_rate_type)
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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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logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
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self.autolog = Autolog(
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batch_size=self.config.decoding.batch_size,
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model_name="deepspeech2",
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model_precision="fp32").getlog()
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self.model.eval()
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cfg = self.config
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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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with open(self.args.result_file, 'w') as fout:
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for i, batch in enumerate(self.test_loader):
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utts, audio, audio_len, texts, texts_len = batch
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metrics = self.compute_metrics(utts, audio, audio_len, texts,
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texts_len, fout)
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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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logger.info("Error rate [%s] (%d/?) = %f" %
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(error_rate_type, num_ins, errors_sum / len_refs))
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# logging
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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 += "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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self.autolog.report()
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def run_test(self):
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self.resume_or_scratch()
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try:
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self.test()
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except KeyboardInterrupt:
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exit(-1)
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def export(self):
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infer_model = DeepSpeech2InferModel.from_pretrained(
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self.test_loader, self.config, self.args.checkpoint_path)
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infer_model.eval()
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feat_dim = self.test_loader.collate_fn.feature_size
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static_model = paddle.jit.to_static(
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infer_model,
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input_spec=[
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paddle.static.InputSpec(
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shape=[None, None, feat_dim],
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dtype='float32'), # audio, [B,T,D]
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paddle.static.InputSpec(shape=[None],
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dtype='int64'), # audio_length, [B]
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])
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logger.info(f"Export code: {static_model.forward.code}")
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paddle.jit.save(static_model, self.args.export_path)
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def run_export(self):
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try:
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self.export()
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except KeyboardInterrupt:
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exit(-1)
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def setup(self):
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"""Setup the experiment.
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"""
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paddle.set_device(self.args.device)
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self.setup_output_dir()
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self.setup_checkpointer()
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self.setup_dataloader()
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self.setup_model()
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self.iteration = 0
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self.epoch = 0
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def setup_model(self):
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config = self.config
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model = DeepSpeech2Model(
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feat_size=self.test_loader.collate_fn.feature_size,
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dict_size=self.test_loader.collate_fn.vocab_size,
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num_conv_layers=config.model.num_conv_layers,
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num_rnn_layers=config.model.num_rnn_layers,
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rnn_size=config.model.rnn_layer_size,
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use_gru=config.model.use_gru,
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share_rnn_weights=config.model.share_rnn_weights,
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apply_online=config.model.apply_online)
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self.model = model
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logger.info("Setup model!")
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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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# 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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config.collator.keep_transcription_text = True
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config.collator.augmentation_config = ""
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# return text ord id
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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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logger.info("Setup test Dataloader!")
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def setup_output_dir(self):
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"""Create a directory used for output.
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"""
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# output dir
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if self.args.output:
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output_dir = Path(self.args.output).expanduser()
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output_dir.mkdir(parents=True, exist_ok=True)
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else:
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output_dir = Path(
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self.args.checkpoint_path).expanduser().parent.parent
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output_dir.mkdir(parents=True, exist_ok=True)
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self.output_dir = output_dir
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