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PaddleSpeech/deepspeech/exps/deepspeech2/model.py

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# 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 and DeepSpeech2Online model."""
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 paddle import inference
from paddle.io import DataLoader
from yacs.config import CfgNode
from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
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.ds2 import DeepSpeech2InferModel
from deepspeech.models.ds2 import DeepSpeech2Model
from deepspeech.models.ds2_online import DeepSpeech2InferModelOnline
from deepspeech.models.ds2_online import DeepSpeech2ModelOnline
from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
from deepspeech.training.reporter import report
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
from deepspeech.utils.utility import UpdateConfig
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):
batch_size = self.config.collator.batch_size
accum_grad = self.config.training.accum_grad
start = time.time()
# forward
utt, audio, audio_len, text, text_len = batch_data
loss = self.model(audio, audio_len, text, text_len)
losses_np = {
'train_loss': float(loss),
}
# loss backward
if (batch_index + 1) % 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) % accum_grad == 0:
self.optimizer.step()
self.optimizer.clear_grad()
self.iteration += 1
iteration_time = time.time() - start
for k, v in losses_np.items():
report(k, v)
report("batch_size", batch_size)
report("accum", accum_grad)
report("step_cost", iteration_time)
if dist.get_rank() == 0 and self.visualizer:
for k, v in losses_np.items():
# `step -1` since we update `step` after optimizer.step().
self.visualizer.add_scalar("train/{}".format(k), v,
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.clone()
with UpdateConfig(config):
config.model.feat_size = self.train_loader.collate_fn.feature_size
config.model.dict_size = self.train_loader.collate_fn.vocab_size
if self.args.model_type == 'offline':
model = DeepSpeech2Model.from_config(config.model)
elif self.args.model_type == 'online':
model = DeepSpeech2ModelOnline.from_config(config.model)
else:
raise Exception("wrong model type")
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)
config.data.manifest = config.data.test_manifest
test_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)
config.collator.keep_transcription_text = True
config.collator.augmentation_config = ""
collate_fn_test = 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=int(config.collator.batch_size),
shuffle=False,
drop_last=False,
collate_fn=collate_fn_dev,
num_workers=config.collator.num_workers)
self.test_loader = DataLoader(
test_dataset,
batch_size=config.decoding.batch_size,
shuffle=False,
drop_last=False,
collate_fn=collate_fn_test,
num_workers=config.collator.num_workers)
logger.info("Setup train/valid/test 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)
self._text_featurizer = TextFeaturizer(
unit_type=config.collator.unit_type, vocab_filepath=None)
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)
result_transcripts = self.compute_result_transcripts(audio, audio_len,
vocab_list, cfg)
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": utt, "ref": target, "hyp": result})
logger.info(f"Utt: {utt}")
logger.info(f"Ref: {target}")
logger.info(f"Hyp: {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)
def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
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)
#replace the <space> with ' '
result_transcripts = [
self._text_featurizer.detokenize(sentence)
for sentence in result_transcripts
]
self.autolog.times.stamp()
self.autolog.times.stamp()
self.autolog.times.end()
return result_transcripts
@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 jsonlines.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()
@paddle.no_grad()
def export(self):
if self.args.model_type == 'offline':
infer_model = DeepSpeech2InferModel.from_pretrained(
self.test_loader, self.config, self.args.checkpoint_path)
elif self.args.model_type == 'online':
infer_model = DeepSpeech2InferModelOnline.from_pretrained(
self.test_loader, self.config, self.args.checkpoint_path)
else:
raise Exception("wrong model type")
infer_model.eval()
feat_dim = self.test_loader.collate_fn.feature_size
static_model = infer_model.export()
logger.info(f"Export code: {static_model.forward.code}")
paddle.jit.save(static_model, self.args.export_path)
class DeepSpeech2ExportTester(DeepSpeech2Tester):
def __init__(self, config, args):
super().__init__(config, args)
def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
if self.args.model_type == "online":
output_probs, output_lens = self.static_forward_online(audio,
audio_len)
elif self.args.model_type == "offline":
output_probs, output_lens = self.static_forward_offline(audio,
audio_len)
else:
raise Exception("wrong model type")
self.predictor.clear_intermediate_tensor()
self.predictor.try_shrink_memory()
self.model.decoder.init_decode(cfg.alpha, cfg.beta, cfg.lang_model_path,
vocab_list, cfg.decoding_method)
result_transcripts = self.model.decoder.decode_probs(
output_probs, output_lens, vocab_list, cfg.decoding_method,
cfg.lang_model_path, cfg.alpha, cfg.beta, cfg.beam_size,
cfg.cutoff_prob, cfg.cutoff_top_n, cfg.num_proc_bsearch)
#replace the <space> with ' '
result_transcripts = [
self._text_featurizer.detokenize(sentence)
for sentence in result_transcripts
]
return result_transcripts
def static_forward_online(self, audio, audio_len,
decoder_chunk_size: int=1):
"""
Parameters
----------
audio (Tensor): shape[B, T, D]
audio_len (Tensor): shape[B]
decoder_chunk_size(int)
Returns
-------
output_probs(numpy.array): shape[B, T, vocab_size]
output_lens(numpy.array): shape[B]
"""
output_probs_list = []
output_lens_list = []
subsampling_rate = self.model.encoder.conv.subsampling_rate
receptive_field_length = self.model.encoder.conv.receptive_field_length
chunk_stride = subsampling_rate * decoder_chunk_size
chunk_size = (decoder_chunk_size - 1
) * subsampling_rate + receptive_field_length
x_batch = audio.numpy()
batch_size, Tmax, x_dim = x_batch.shape
x_len_batch = audio_len.numpy().astype(np.int64)
if (Tmax - chunk_size) % chunk_stride != 0:
padding_len_batch = chunk_stride - (
Tmax - chunk_size
) % chunk_stride # The length of padding for the batch
else:
padding_len_batch = 0
x_list = np.split(x_batch, batch_size, axis=0)
x_len_list = np.split(x_len_batch, batch_size, axis=0)
for x, x_len in zip(x_list, x_len_list):
self.autolog.times.start()
self.autolog.times.stamp()
x_len = x_len[0]
assert (chunk_size <= x_len)
if (x_len - chunk_size) % chunk_stride != 0:
padding_len_x = chunk_stride - (x_len - chunk_size
) % chunk_stride
else:
padding_len_x = 0
padding = np.zeros(
(x.shape[0], padding_len_x, x.shape[2]), dtype=x.dtype)
padded_x = np.concatenate([x, padding], axis=1)
num_chunk = (x_len + padding_len_x - chunk_size) / chunk_stride + 1
num_chunk = int(num_chunk)
chunk_state_h_box = np.zeros(
(self.config.model.num_rnn_layers, 1,
self.config.model.rnn_layer_size),
dtype=x.dtype)
chunk_state_c_box = np.zeros(
(self.config.model.num_rnn_layers, 1,
self.config.model.rnn_layer_size),
dtype=x.dtype)
input_names = self.predictor.get_input_names()
audio_handle = self.predictor.get_input_handle(input_names[0])
audio_len_handle = self.predictor.get_input_handle(input_names[1])
h_box_handle = self.predictor.get_input_handle(input_names[2])
c_box_handle = self.predictor.get_input_handle(input_names[3])
probs_chunk_list = []
probs_chunk_lens_list = []
for i in range(0, num_chunk):
start = i * chunk_stride
end = start + chunk_size
x_chunk = padded_x[:, start:end, :]
if x_len < i * chunk_stride:
x_chunk_lens = 0
else:
x_chunk_lens = min(x_len - i * chunk_stride, chunk_size)
if (x_chunk_lens <
receptive_field_length): #means the number of input frames in the chunk is not enough for predicting one prob
break
x_chunk_lens = np.array([x_chunk_lens])
audio_handle.reshape(x_chunk.shape)
audio_handle.copy_from_cpu(x_chunk)
audio_len_handle.reshape(x_chunk_lens.shape)
audio_len_handle.copy_from_cpu(x_chunk_lens)
h_box_handle.reshape(chunk_state_h_box.shape)
h_box_handle.copy_from_cpu(chunk_state_h_box)
c_box_handle.reshape(chunk_state_c_box.shape)
c_box_handle.copy_from_cpu(chunk_state_c_box)
output_names = self.predictor.get_output_names()
output_handle = self.predictor.get_output_handle(
output_names[0])
output_lens_handle = self.predictor.get_output_handle(
output_names[1])
output_state_h_handle = self.predictor.get_output_handle(
output_names[2])
output_state_c_handle = self.predictor.get_output_handle(
output_names[3])
self.predictor.run()
output_chunk_probs = output_handle.copy_to_cpu()
output_chunk_lens = output_lens_handle.copy_to_cpu()
chunk_state_h_box = output_state_h_handle.copy_to_cpu()
chunk_state_c_box = output_state_c_handle.copy_to_cpu()
probs_chunk_list.append(output_chunk_probs)
probs_chunk_lens_list.append(output_chunk_lens)
output_probs = np.concatenate(probs_chunk_list, axis=1)
output_lens = np.sum(probs_chunk_lens_list, axis=0)
vocab_size = output_probs.shape[2]
output_probs_padding_len = Tmax + padding_len_batch - output_probs.shape[
1]
output_probs_padding = np.zeros(
(1, output_probs_padding_len, vocab_size),
dtype=output_probs.
dtype) # The prob padding for a piece of utterance
output_probs = np.concatenate(
[output_probs, output_probs_padding], axis=1)
output_probs_list.append(output_probs)
output_lens_list.append(output_lens)
self.autolog.times.stamp()
self.autolog.times.stamp()
self.autolog.times.end()
output_probs = np.concatenate(output_probs_list, axis=0)
output_lens = np.concatenate(output_lens_list, axis=0)
return output_probs, output_lens
def static_forward_offline(self, audio, audio_len):
"""
Parameters
----------
audio (Tensor): shape[B, T, D]
audio_len (Tensor): shape[B]
Returns
-------
output_probs(numpy.array): shape[B, T, vocab_size]
output_lens(numpy.array): shape[B]
"""
x = audio.numpy()
x_len = audio_len.numpy().astype(np.int64)
input_names = self.predictor.get_input_names()
audio_handle = self.predictor.get_input_handle(input_names[0])
audio_len_handle = self.predictor.get_input_handle(input_names[1])
audio_handle.reshape(x.shape)
audio_handle.copy_from_cpu(x)
audio_len_handle.reshape(x_len.shape)
audio_len_handle.copy_from_cpu(x_len)
self.autolog.times.start()
self.autolog.times.stamp()
self.predictor.run()
self.autolog.times.stamp()
self.autolog.times.stamp()
self.autolog.times.end()
output_names = self.predictor.get_output_names()
output_handle = self.predictor.get_output_handle(output_names[0])
output_lens_handle = self.predictor.get_output_handle(output_names[1])
output_probs = output_handle.copy_to_cpu()
output_lens = output_lens_handle.copy_to_cpu()
return output_probs, output_lens
def setup_model(self):
super().setup_model()
speedyspeech_config = inference.Config(
self.args.export_path + ".pdmodel",
self.args.export_path + ".pdiparams")
if (os.environ['CUDA_VISIBLE_DEVICES'].strip() != ''):
speedyspeech_config.enable_use_gpu(100, 0)
speedyspeech_config.enable_memory_optim()
speedyspeech_predictor = inference.create_predictor(speedyspeech_config)
self.predictor = speedyspeech_predictor
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