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asr_engine.py
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asr_engine.py
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# Copyright (c) 2022 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.
import copy
import os
from typing import Optional
import numpy as np
import paddle
from numpy import float32
from yacs.config import CfgNode
from paddlespeech.cli.asr.infer import ASRExecutor
from paddlespeech.cli.asr.infer import model_alias
from paddlespeech.cli.log import logger
from paddlespeech.cli.utils import download_and_decompress
from paddlespeech.cli.utils import MODEL_HOME
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.frontend.speech import SpeechSegment
from paddlespeech.s2t.modules.ctc import CTCDecoder
from paddlespeech.s2t.transform.transformation import Transformation
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.s2t.utils.tensor_utils import add_sos_eos
from paddlespeech.s2t.utils.tensor_utils import pad_sequence
from paddlespeech.s2t.utils.utility import UpdateConfig
from paddlespeech.server.engine.asr.online.ctc_search import CTCPrefixBeamSearch
from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.audio_process import pcm2float
from paddlespeech.server.utils.paddle_predictor import init_predictor
__all__ = ['ASREngine']
pretrained_models = {
"deepspeech2online_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.1.model.tar.gz',
'md5':
'98b87b171b7240b7cae6e07d8d0bc9be',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2_online/checkpoints/avg_1',
'model':
'exp/deepspeech2_online/checkpoints/avg_1.jit.pdmodel',
'params':
'exp/deepspeech2_online/checkpoints/avg_1.jit.pdiparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"conformer_online_multicn-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/multi_cn/asr1/asr1_chunk_conformer_multi_cn_ckpt_0.2.3.model.tar.gz',
'md5':
'0ac93d390552336f2a906aec9e33c5fa',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/multi_cn',
'model':
'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
'params':
'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
}
# ASR server connection process class
class PaddleASRConnectionHanddler:
def __init__(self, asr_engine):
"""Init a Paddle ASR Connection Handler instance
Args:
asr_engine (ASREngine): the global asr engine
"""
super().__init__()
logger.info(
"create an paddle asr connection handler to process the websocket connection"
)
self.config = asr_engine.config
self.model_config = asr_engine.executor.config
self.asr_engine = asr_engine
self.init()
self.reset()
def init(self):
# model_type, sample_rate and text_feature is shared for deepspeech2 and conformer
self.model_type = self.asr_engine.executor.model_type
self.sample_rate = self.asr_engine.executor.sample_rate
# tokens to text
self.text_feature = self.asr_engine.executor.text_feature
if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
from paddlespeech.s2t.io.collator import SpeechCollator
self.am_predictor = self.asr_engine.executor.am_predictor
self.collate_fn_test = SpeechCollator.from_config(self.model_config)
self.decoder = CTCDecoder(
odim=self.model_config.output_dim, # <blank> is in vocab
enc_n_units=self.model_config.rnn_layer_size * 2,
blank_id=self.model_config.blank_id,
dropout_rate=0.0,
reduction=True, # sum
batch_average=True, # sum / batch_size
grad_norm_type=self.model_config.get('ctc_grad_norm_type',
None))
cfg = self.model_config.decode
decode_batch_size = 1 # for online
self.decoder.init_decoder(
decode_batch_size, self.text_feature.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)
# frame window samples length and frame shift samples length
self.win_length = int(self.model_config.window_ms / 1000 *
self.sample_rate)
self.n_shift = int(self.model_config.stride_ms / 1000 *
self.sample_rate)
elif "conformer" in self.model_type or "transformer" in self.model_type:
# acoustic model
self.model = self.asr_engine.executor.model
# ctc decoding config
self.ctc_decode_config = self.asr_engine.executor.config.decode
self.searcher = CTCPrefixBeamSearch(self.ctc_decode_config)
# extract feat, new only fbank in conformer model
self.preprocess_conf = self.model_config.preprocess_config
self.preprocess_args = {"train": False}
self.preprocessing = Transformation(self.preprocess_conf)
# frame window samples length and frame shift samples length
self.win_length = self.preprocess_conf.process[0]['win_length']
self.n_shift = self.preprocess_conf.process[0]['n_shift']
def extract_feat(self, samples):
if "deepspeech2online" in self.model_type:
# self.reamined_wav stores all the samples,
# include the original remained_wav and this package samples
samples = np.frombuffer(samples, dtype=np.int16)
assert samples.ndim == 1
# pcm16 -> pcm 32
# pcm2float will change the orignal samples,
# so we shoule do pcm2float before concatenate
samples = pcm2float(samples)
if self.remained_wav is None:
self.remained_wav = samples
else:
assert self.remained_wav.ndim == 1
self.remained_wav = np.concatenate([self.remained_wav, samples])
logger.info(
f"The connection remain the audio samples: {self.remained_wav.shape}"
)
# read audio
speech_segment = SpeechSegment.from_pcm(
self.remained_wav, self.sample_rate, transcript=" ")
# audio augment
self.collate_fn_test.augmentation.transform_audio(speech_segment)
# extract speech feature
spectrum, transcript_part = self.collate_fn_test._speech_featurizer.featurize(
speech_segment, self.collate_fn_test.keep_transcription_text)
# CMVN spectrum
if self.collate_fn_test._normalizer:
spectrum = self.collate_fn_test._normalizer.apply(spectrum)
# spectrum augment
audio = self.collate_fn_test.augmentation.transform_feature(
spectrum)
audio_len = audio.shape[0]
audio = paddle.to_tensor(audio, dtype='float32')
# audio_len = paddle.to_tensor(audio_len)
audio = paddle.unsqueeze(audio, axis=0)
if self.cached_feat is None:
self.cached_feat = audio
else:
assert (len(audio.shape) == 3)
assert (len(self.cached_feat.shape) == 3)
self.cached_feat = paddle.concat(
[self.cached_feat, audio], axis=1)
# set the feat device
if self.device is None:
self.device = self.cached_feat.place
self.num_frames += audio_len
self.remained_wav = self.remained_wav[self.n_shift * audio_len:]
logger.info(
f"process the audio feature success, the connection feat shape: {self.cached_feat.shape}"
)
logger.info(
f"After extract feat, the connection remain the audio samples: {self.remained_wav.shape}"
)
elif "conformer_online" in self.model_type:
logger.info("Online ASR extract the feat")
samples = np.frombuffer(samples, dtype=np.int16)
assert samples.ndim == 1
logger.info(f"This package receive {samples.shape[0]} pcm data")
self.num_samples += samples.shape[0]
# self.reamined_wav stores all the samples,
# include the original remained_wav and this package samples
if self.remained_wav is None:
self.remained_wav = samples
else:
assert self.remained_wav.ndim == 1
self.remained_wav = np.concatenate([self.remained_wav, samples])
logger.info(
f"The connection remain the audio samples: {self.remained_wav.shape}"
)
if len(self.remained_wav) < self.win_length:
return 0
# fbank
x_chunk = self.preprocessing(self.remained_wav,
**self.preprocess_args)
x_chunk = paddle.to_tensor(
x_chunk, dtype="float32").unsqueeze(axis=0)
if self.cached_feat is None:
self.cached_feat = x_chunk
else:
assert (len(x_chunk.shape) == 3)
assert (len(self.cached_feat.shape) == 3)
self.cached_feat = paddle.concat(
[self.cached_feat, x_chunk], axis=1)
# set the feat device
if self.device is None:
self.device = self.cached_feat.place
num_frames = x_chunk.shape[1]
self.num_frames += num_frames
self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
logger.info(
f"process the audio feature success, the connection feat shape: {self.cached_feat.shape}"
)
logger.info(
f"After extract feat, the connection remain the audio samples: {self.remained_wav.shape}"
)
# logger.info(f"accumulate samples: {self.num_samples}")
def reset(self):
if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
# for deepspeech2
self.chunk_state_h_box = copy.deepcopy(
self.asr_engine.executor.chunk_state_h_box)
self.chunk_state_c_box = copy.deepcopy(
self.asr_engine.executor.chunk_state_c_box)
self.decoder.reset_decoder(batch_size=1)
# for conformer online
self.subsampling_cache = None
self.elayers_output_cache = None
self.conformer_cnn_cache = None
self.encoder_out = None
self.cached_feat = None
self.remained_wav = None
self.offset = 0
self.num_samples = 0
self.device = None
self.hyps = []
self.num_frames = 0
self.chunk_num = 0
self.global_frame_offset = 0
self.result_transcripts = ['']
def decode(self, is_finished=False):
if "deepspeech2online" in self.model_type:
# x_chunk 是特征数据
decoding_chunk_size = 1 # decoding_chunk_size=1 in deepspeech2 model
context = 7 # context=7 in deepspeech2 model
subsampling = 4 # subsampling=4 in deepspeech2 model
stride = subsampling * decoding_chunk_size
cached_feature_num = context - subsampling
# decoding window for model
decoding_window = (decoding_chunk_size - 1) * subsampling + context
if self.cached_feat is None:
logger.info("no audio feat, please input more pcm data")
return
num_frames = self.cached_feat.shape[1]
logger.info(
f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
)
# the cached feat must be larger decoding_window
if num_frames < decoding_window and not is_finished:
logger.info(
f"frame feat num is less than {decoding_window}, please input more pcm data"
)
return None, None
# if is_finished=True, we need at least context frames
if num_frames < context:
logger.info(
"flast {num_frames} is less than context {context} frames, and we cannot do model forward"
)
return None, None
logger.info("start to do model forward")
# num_frames - context + 1 ensure that current frame can get context window
if is_finished:
# if get the finished chunk, we need process the last context
left_frames = context
else:
# we only process decoding_window frames for one chunk
left_frames = decoding_window
for cur in range(0, num_frames - left_frames + 1, stride):
end = min(cur + decoding_window, num_frames)
# extract the audio
x_chunk = self.cached_feat[:, cur:end, :].numpy()
x_chunk_lens = np.array([x_chunk.shape[1]])
trans_best = self.decode_one_chunk(x_chunk, x_chunk_lens)
self.result_transcripts = [trans_best]
self.cached_feat = self.cached_feat[:, end - cached_feature_num:, :]
# return trans_best[0]
elif "conformer" in self.model_type or "transformer" in self.model_type:
try:
logger.info(
f"we will use the transformer like model : {self.model_type}"
)
self.advance_decoding(is_finished)
self.update_result()
except Exception as e:
logger.exception(e)
else:
raise Exception("invalid model name")
@paddle.no_grad()
def decode_one_chunk(self, x_chunk, x_chunk_lens):
logger.info("start to decoce one chunk with deepspeech2 model")
input_names = self.am_predictor.get_input_names()
audio_handle = self.am_predictor.get_input_handle(input_names[0])
audio_len_handle = self.am_predictor.get_input_handle(input_names[1])
h_box_handle = self.am_predictor.get_input_handle(input_names[2])
c_box_handle = self.am_predictor.get_input_handle(input_names[3])
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(self.chunk_state_h_box.shape)
h_box_handle.copy_from_cpu(self.chunk_state_h_box)
c_box_handle.reshape(self.chunk_state_c_box.shape)
c_box_handle.copy_from_cpu(self.chunk_state_c_box)
output_names = self.am_predictor.get_output_names()
output_handle = self.am_predictor.get_output_handle(output_names[0])
output_lens_handle = self.am_predictor.get_output_handle(
output_names[1])
output_state_h_handle = self.am_predictor.get_output_handle(
output_names[2])
output_state_c_handle = self.am_predictor.get_output_handle(
output_names[3])
self.am_predictor.run()
output_chunk_probs = output_handle.copy_to_cpu()
output_chunk_lens = output_lens_handle.copy_to_cpu()
self.chunk_state_h_box = output_state_h_handle.copy_to_cpu()
self.chunk_state_c_box = output_state_c_handle.copy_to_cpu()
self.decoder.next(output_chunk_probs, output_chunk_lens)
trans_best, trans_beam = self.decoder.decode()
logger.info(f"decode one best result: {trans_best[0]}")
return trans_best[0]
@paddle.no_grad()
def advance_decoding(self, is_finished=False):
logger.info("start to decode with advanced_decoding method")
cfg = self.ctc_decode_config
decoding_chunk_size = cfg.decoding_chunk_size
num_decoding_left_chunks = cfg.num_decoding_left_chunks
assert decoding_chunk_size > 0
subsampling = self.model.encoder.embed.subsampling_rate
context = self.model.encoder.embed.right_context + 1
stride = subsampling * decoding_chunk_size
cached_feature_num = context - subsampling # processed chunk feature cached for next chunk
# decoding window for model
decoding_window = (decoding_chunk_size - 1) * subsampling + context
if self.cached_feat is None:
logger.info("no audio feat, please input more pcm data")
return
num_frames = self.cached_feat.shape[1]
logger.info(
f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
)
# the cached feat must be larger decoding_window
if num_frames < decoding_window and not is_finished:
logger.info(
f"frame feat num is less than {decoding_window}, please input more pcm data"
)
return None, None
# if is_finished=True, we need at least context frames
if num_frames < context:
logger.info(
"flast {num_frames} is less than context {context} frames, and we cannot do model forward"
)
return None, None
logger.info("start to do model forward")
required_cache_size = decoding_chunk_size * num_decoding_left_chunks
outputs = []
# num_frames - context + 1 ensure that current frame can get context window
if is_finished:
# if get the finished chunk, we need process the last context
left_frames = context
else:
# we only process decoding_window frames for one chunk
left_frames = decoding_window
# record the end for removing the processed feat
end = None
for cur in range(0, num_frames - left_frames + 1, stride):
end = min(cur + decoding_window, num_frames)
self.chunk_num += 1
chunk_xs = self.cached_feat[:, cur:end, :]
(y, self.subsampling_cache, self.elayers_output_cache,
self.conformer_cnn_cache) = self.model.encoder.forward_chunk(
chunk_xs, self.offset, required_cache_size,
self.subsampling_cache, self.elayers_output_cache,
self.conformer_cnn_cache)
outputs.append(y)
# update the offset
self.offset += y.shape[1]
ys = paddle.cat(outputs, 1)
if self.encoder_out is None:
self.encoder_out = ys
else:
self.encoder_out = paddle.concat([self.encoder_out, ys], axis=1)
# get the ctc probs
ctc_probs = self.model.ctc.log_softmax(ys) # (1, maxlen, vocab_size)
ctc_probs = ctc_probs.squeeze(0)
self.searcher.search(ctc_probs, self.cached_feat.place)
self.hyps = self.searcher.get_one_best_hyps()
assert self.cached_feat.shape[0] == 1
assert end >= cached_feature_num
self.cached_feat = self.cached_feat[0, end -
cached_feature_num:, :].unsqueeze(0)
assert len(
self.cached_feat.shape
) == 3, f"current cache feat shape is: {self.cached_feat.shape}"
logger.info(
f"This connection handler encoder out shape: {self.encoder_out.shape}"
)
def update_result(self):
logger.info("update the final result")
hyps = self.hyps
self.result_transcripts = [
self.text_feature.defeaturize(hyp) for hyp in hyps
]
self.result_tokenids = [hyp for hyp in hyps]
def get_result(self):
if len(self.result_transcripts) > 0:
return self.result_transcripts[0]
else:
return ''
@paddle.no_grad()
def rescoring(self):
if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
return
logger.info("rescoring the final result")
if "attention_rescoring" != self.ctc_decode_config.decoding_method:
return
self.searcher.finalize_search()
self.update_result()
beam_size = self.ctc_decode_config.beam_size
hyps = self.searcher.get_hyps()
if hyps is None or len(hyps) == 0:
return
# assert len(hyps) == beam_size
hyp_list = []
for hyp in hyps:
hyp_content = hyp[0]
# Prevent the hyp is empty
if len(hyp_content) == 0:
hyp_content = (self.model.ctc.blank_id, )
hyp_content = paddle.to_tensor(
hyp_content, place=self.device, dtype=paddle.long)
hyp_list.append(hyp_content)
hyps_pad = pad_sequence(hyp_list, True, self.model.ignore_id)
hyps_lens = paddle.to_tensor(
[len(hyp[0]) for hyp in hyps], place=self.device,
dtype=paddle.long) # (beam_size,)
hyps_pad, _ = add_sos_eos(hyps_pad, self.model.sos, self.model.eos,
self.model.ignore_id)
hyps_lens = hyps_lens + 1 # Add <sos> at begining
encoder_out = self.encoder_out.repeat(beam_size, 1, 1)
encoder_mask = paddle.ones(
(beam_size, 1, encoder_out.shape[1]), dtype=paddle.bool)
decoder_out, _ = self.model.decoder(
encoder_out, encoder_mask, hyps_pad,
hyps_lens) # (beam_size, max_hyps_len, vocab_size)
# ctc score in ln domain
decoder_out = paddle.nn.functional.log_softmax(decoder_out, axis=-1)
decoder_out = decoder_out.numpy()
# Only use decoder score for rescoring
best_score = -float('inf')
best_index = 0
# hyps is List[(Text=List[int], Score=float)], len(hyps)=beam_size
for i, hyp in enumerate(hyps):
score = 0.0
for j, w in enumerate(hyp[0]):
score += decoder_out[i][j][w]
# last decoder output token is `eos`, for laste decoder input token.
score += decoder_out[i][len(hyp[0])][self.model.eos]
# add ctc score (which in ln domain)
score += hyp[1] * self.ctc_decode_config.ctc_weight
if score > best_score:
best_score = score
best_index = i
# update the one best result
logger.info(f"best index: {best_index}")
self.hyps = [hyps[best_index][0]]
self.update_result()
class ASRServerExecutor(ASRExecutor):
def __init__(self):
super().__init__()
pass
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
res_path = os.path.join(MODEL_HOME, tag)
decompressed_path = download_and_decompress(pretrained_models[tag],
res_path)
decompressed_path = os.path.abspath(decompressed_path)
logger.info(
'Use pretrained model stored in: {}'.format(decompressed_path))
return decompressed_path
def _init_from_path(self,
model_type: str='deepspeech2online_aishell',
am_model: Optional[os.PathLike]=None,
am_params: Optional[os.PathLike]=None,
lang: str='zh',
sample_rate: int=16000,
cfg_path: Optional[os.PathLike]=None,
decode_method: str='attention_rescoring',
am_predictor_conf: dict=None):
"""
Init model and other resources from a specific path.
"""
self.model_type = model_type
self.sample_rate = sample_rate
if cfg_path is None or am_model is None or am_params is None:
sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str
logger.info(f"Load the pretrained model, tag = {tag}")
res_path = self._get_pretrained_path(tag) # wenetspeech_zh
self.res_path = res_path
self.cfg_path = os.path.join(res_path,
pretrained_models[tag]['cfg_path'])
self.am_model = os.path.join(res_path,
pretrained_models[tag]['model'])
self.am_params = os.path.join(res_path,
pretrained_models[tag]['params'])
logger.info(res_path)
else:
self.cfg_path = os.path.abspath(cfg_path)
self.am_model = os.path.abspath(am_model)
self.am_params = os.path.abspath(am_params)
self.res_path = os.path.dirname(
os.path.dirname(os.path.abspath(self.cfg_path)))
logger.info(self.cfg_path)
logger.info(self.am_model)
logger.info(self.am_params)
#Init body.
self.config = CfgNode(new_allowed=True)
self.config.merge_from_file(self.cfg_path)
with UpdateConfig(self.config):
if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
from paddlespeech.s2t.io.collator import SpeechCollator
self.vocab = self.config.vocab_filepath
self.config.decode.lang_model_path = os.path.join(
MODEL_HOME, 'language_model',
self.config.decode.lang_model_path)
self.collate_fn_test = SpeechCollator.from_config(self.config)
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab)
lm_url = pretrained_models[tag]['lm_url']
lm_md5 = pretrained_models[tag]['lm_md5']
logger.info(f"Start to load language model {lm_url}")
self.download_lm(
lm_url,
os.path.dirname(self.config.decode.lang_model_path), lm_md5)
elif "conformer" in model_type or "transformer" in model_type:
logger.info("start to create the stream conformer asr engine")
if self.config.spm_model_prefix:
self.config.spm_model_prefix = os.path.join(
self.res_path, self.config.spm_model_prefix)
self.vocab = self.config.vocab_filepath
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type,
vocab=self.config.vocab_filepath,
spm_model_prefix=self.config.spm_model_prefix)
# update the decoding method
if decode_method:
self.config.decode.decoding_method = decode_method
# we only support ctc_prefix_beam_search and attention_rescoring dedoding method
# Generally we set the decoding_method to attention_rescoring
if self.config.decode.decoding_method not in [
"ctc_prefix_beam_search", "attention_rescoring"
]:
logger.info(
"we set the decoding_method to attention_rescoring")
self.config.decode.decoding = "attention_rescoring"
assert self.config.decode.decoding_method in [
"ctc_prefix_beam_search", "attention_rescoring"
], f"we only support ctc_prefix_beam_search and attention_rescoring dedoding method, current decoding method is {self.config.decode.decoding_method}"
else:
raise Exception("wrong type")
if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
# AM predictor
logger.info("ASR engine start to init the am predictor")
self.am_predictor_conf = am_predictor_conf
self.am_predictor = init_predictor(
model_file=self.am_model,
params_file=self.am_params,
predictor_conf=self.am_predictor_conf)
# decoder
logger.info("ASR engine start to create the ctc decoder instance")
self.decoder = CTCDecoder(
odim=self.config.output_dim, # <blank> is in vocab
enc_n_units=self.config.rnn_layer_size * 2,
blank_id=self.config.blank_id,
dropout_rate=0.0,
reduction=True, # sum
batch_average=True, # sum / batch_size
grad_norm_type=self.config.get('ctc_grad_norm_type', None))
# init decoder
logger.info("ASR engine start to init the ctc decoder")
cfg = self.config.decode
decode_batch_size = 1 # for online
self.decoder.init_decoder(
decode_batch_size, self.text_feature.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)
# init state box
self.chunk_state_h_box = np.zeros(
(self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
dtype=float32)
self.chunk_state_c_box = np.zeros(
(self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
dtype=float32)
elif "conformer" in model_type or "transformer" in model_type:
model_name = model_type[:model_type.rindex(
'_')] # model_type: {model_name}_{dataset}
logger.info(f"model name: {model_name}")
model_class = dynamic_import(model_name, model_alias)
model_conf = self.config
model = model_class.from_config(model_conf)
self.model = model
self.model.eval()
# load model
model_dict = paddle.load(self.am_model)
self.model.set_state_dict(model_dict)
logger.info("create the transformer like model success")
# update the ctc decoding
self.searcher = CTCPrefixBeamSearch(self.config.decode)
self.transformer_decode_reset()
def reset_decoder_and_chunk(self):
"""reset decoder and chunk state for an new audio
"""
if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
self.decoder.reset_decoder(batch_size=1)
# init state box, for new audio request
self.chunk_state_h_box = np.zeros(
(self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
dtype=float32)
self.chunk_state_c_box = np.zeros(
(self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
dtype=float32)
elif "conformer" in self.model_type or "transformer" in self.model_type:
self.transformer_decode_reset()
def decode_one_chunk(self, x_chunk, x_chunk_lens, model_type: str):
"""decode one chunk
Args:
x_chunk (numpy.array): shape[B, T, D]
x_chunk_lens (numpy.array): shape[B]
model_type (str): online model type
Returns:
str: one best result
"""
logger.info("start to decoce chunk by chunk")
if "deepspeech2online" in model_type:
input_names = self.am_predictor.get_input_names()
audio_handle = self.am_predictor.get_input_handle(input_names[0])
audio_len_handle = self.am_predictor.get_input_handle(
input_names[1])
h_box_handle = self.am_predictor.get_input_handle(input_names[2])
c_box_handle = self.am_predictor.get_input_handle(input_names[3])
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(self.chunk_state_h_box.shape)
h_box_handle.copy_from_cpu(self.chunk_state_h_box)
c_box_handle.reshape(self.chunk_state_c_box.shape)
c_box_handle.copy_from_cpu(self.chunk_state_c_box)
output_names = self.am_predictor.get_output_names()
output_handle = self.am_predictor.get_output_handle(output_names[0])
output_lens_handle = self.am_predictor.get_output_handle(
output_names[1])
output_state_h_handle = self.am_predictor.get_output_handle(
output_names[2])
output_state_c_handle = self.am_predictor.get_output_handle(
output_names[3])
self.am_predictor.run()
output_chunk_probs = output_handle.copy_to_cpu()
output_chunk_lens = output_lens_handle.copy_to_cpu()
self.chunk_state_h_box = output_state_h_handle.copy_to_cpu()
self.chunk_state_c_box = output_state_c_handle.copy_to_cpu()
self.decoder.next(output_chunk_probs, output_chunk_lens)
trans_best, trans_beam = self.decoder.decode()
logger.info(f"decode one best result: {trans_best[0]}")
return trans_best[0]
elif "conformer" in model_type or "transformer" in model_type:
try:
logger.info(
f"we will use the transformer like model : {self.model_type}"
)
self.advanced_decoding(x_chunk, x_chunk_lens)
self.update_result()
return self.result_transcripts[0]
except Exception as e:
logger.exception(e)
else:
raise Exception("invalid model name")
def advanced_decoding(self, xs: paddle.Tensor, x_chunk_lens):
logger.info("start to decode with advanced_decoding method")
encoder_out, encoder_mask = self.encoder_forward(xs)
ctc_probs = self.model.ctc.log_softmax(
encoder_out) # (1, maxlen, vocab_size)
ctc_probs = ctc_probs.squeeze(0)
self.searcher.search(ctc_probs, xs.place)
# update the one best result
self.hyps = self.searcher.get_one_best_hyps()
# now we supprot ctc_prefix_beam_search and attention_rescoring
if "attention_rescoring" in self.config.decode.decoding_method:
self.rescoring(encoder_out, xs.place)
def encoder_forward(self, xs):
logger.info("get the model out from the feat")
cfg = self.config.decode
decoding_chunk_size = cfg.decoding_chunk_size
num_decoding_left_chunks = cfg.num_decoding_left_chunks
assert decoding_chunk_size > 0
subsampling = self.model.encoder.embed.subsampling_rate
context = self.model.encoder.embed.right_context + 1
stride = subsampling * decoding_chunk_size
# decoding window for model
decoding_window = (decoding_chunk_size - 1) * subsampling + context
num_frames = xs.shape[1]
required_cache_size = decoding_chunk_size * num_decoding_left_chunks
logger.info("start to do model forward")
outputs = []
# num_frames - context + 1 ensure that current frame can get context window
for cur in range(0, num_frames - context + 1, stride):
end = min(cur + decoding_window, num_frames)
chunk_xs = xs[:, cur:end, :]
(y, self.subsampling_cache, self.elayers_output_cache,
self.conformer_cnn_cache) = self.model.encoder.forward_chunk(
chunk_xs, self.offset, required_cache_size,
self.subsampling_cache, self.elayers_output_cache,
self.conformer_cnn_cache)
outputs.append(y)
self.offset += y.shape[1]
ys = paddle.cat(outputs, 1)
masks = paddle.ones([1, ys.shape[1]], dtype=paddle.bool)
masks = masks.unsqueeze(1)
return ys, masks
def rescoring(self, encoder_out, device):
logger.info("start to rescoring the hyps")
beam_size = self.config.decode.beam_size
hyps = self.searcher.get_hyps()
assert len(hyps) == beam_size
hyp_list = []
for hyp in hyps:
hyp_content = hyp[0]
# Prevent the hyp is empty
if len(hyp_content) == 0:
hyp_content = (self.model.ctc.blank_id, )
hyp_content = paddle.to_tensor(
hyp_content, place=device, dtype=paddle.long)
hyp_list.append(hyp_content)
hyps_pad = pad_sequence(hyp_list, True, self.model.ignore_id)
hyps_lens = paddle.to_tensor(
[len(hyp[0]) for hyp in hyps], place=device,
dtype=paddle.long) # (beam_size,)
hyps_pad, _ = add_sos_eos(hyps_pad, self.model.sos, self.model.eos,
self.model.ignore_id)
hyps_lens = hyps_lens + 1 # Add <sos> at begining
encoder_out = encoder_out.repeat(beam_size, 1, 1)
encoder_mask = paddle.ones(
(beam_size, 1, encoder_out.shape[1]), dtype=paddle.bool)
decoder_out, _ = self.model.decoder(
encoder_out, encoder_mask, hyps_pad,
hyps_lens) # (beam_size, max_hyps_len, vocab_size)
# ctc score in ln domain
decoder_out = paddle.nn.functional.log_softmax(decoder_out, axis=-1)
decoder_out = decoder_out.numpy()
# Only use decoder score for rescoring
best_score = -float('inf')
best_index = 0
# hyps is List[(Text=List[int], Score=float)], len(hyps)=beam_size
for i, hyp in enumerate(hyps):
score = 0.0
for j, w in enumerate(hyp[0]):
score += decoder_out[i][j][w]
# last decoder output token is `eos`, for laste decoder input token.
score += decoder_out[i][len(hyp[0])][self.model.eos]
# add ctc score (which in ln domain)
score += hyp[1] * self.config.decode.ctc_weight
if score > best_score:
best_score = score
best_index = i
# update the one best result
self.hyps = [hyps[best_index][0]]
return hyps[best_index][0]
def transformer_decode_reset(self):
self.subsampling_cache = None
self.elayers_output_cache = None
self.conformer_cnn_cache = None
self.offset = 0
# decoding reset
self.searcher.reset()
def update_result(self):
logger.info("update the final result")
hyps = self.hyps
self.result_transcripts = [
self.text_feature.defeaturize(hyp) for hyp in hyps
]
self.result_tokenids = [hyp for hyp in hyps]
def extract_feat(self, samples, sample_rate):
"""extract feat
Args:
samples (numpy.array): numpy.float32
sample_rate (int): sample rate
Returns:
x_chunk (numpy.array): shape[B, T, D]
x_chunk_lens (numpy.array): shape[B]
"""
if "deepspeech2online" in self.model_type:
# pcm16 -> pcm 32
samples = pcm2float(samples)
# read audio
speech_segment = SpeechSegment.from_pcm(
samples, sample_rate, transcript=" ")
# audio augment
self.collate_fn_test.augmentation.transform_audio(speech_segment)
# extract speech feature
spectrum, transcript_part = self.collate_fn_test._speech_featurizer.featurize(
speech_segment, self.collate_fn_test.keep_transcription_text)
# CMVN spectrum
if self.collate_fn_test._normalizer:
spectrum = self.collate_fn_test._normalizer.apply(spectrum)
# spectrum augment
audio = self.collate_fn_test.augmentation.transform_feature(
spectrum)
audio_len = audio.shape[0]
audio = paddle.to_tensor(audio, dtype='float32')
# audio_len = paddle.to_tensor(audio_len)
audio = paddle.unsqueeze(audio, axis=0)
x_chunk = audio.numpy()
x_chunk_lens = np.array([audio_len])
return x_chunk, x_chunk_lens
elif "conformer_online" in self.model_type:
if sample_rate != self.sample_rate:
logger.info(f"audio sample rate {sample_rate} is not match,"
"the model sample_rate is {self.sample_rate}")
logger.info(f"ASR Engine use the {self.model_type} to process")
logger.info("Create the preprocess instance")
preprocess_conf = self.config.preprocess_config
preprocess_args = {"train": False}
preprocessing = Transformation(preprocess_conf)
logger.info("Read the audio file")
logger.info(f"audio shape: {samples.shape}")
# fbank
x_chunk = preprocessing(samples, **preprocess_args)
x_chunk_lens = paddle.to_tensor(x_chunk.shape[0])
x_chunk = paddle.to_tensor(
x_chunk, dtype="float32").unsqueeze(axis=0)